U.S. patent application number 13/588948 was filed with the patent office on 2013-06-20 for treatment planning systems and methods for body contouring application.
This patent application is currently assigned to Zeltiq Aesthetics, Inc.. The applicant listed for this patent is John W. Allison. Invention is credited to John W. Allison.
Application Number | 20130158440 13/588948 |
Document ID | / |
Family ID | 42058200 |
Filed Date | 2013-06-20 |
United States Patent
Application |
20130158440 |
Kind Code |
A1 |
Allison; John W. |
June 20, 2013 |
TREATMENT PLANNING SYSTEMS AND METHODS FOR BODY CONTOURING
APPLICATION
Abstract
Methods and system for treatment planning for non- and
minimally-invasive alteration of body adipose tissue for reduction
and contouring of body fat are described herein. Treatment plans
can be generated by capturing current body part data (e.g.,
positioning, contour/shape, thickness of adipose tissue, etc.),
determining desired outcome of treatment (e.g., percent reduction
of adipose tissue thickness, degree of contour change, etc.), and
determining treatment parameters to achieve desired results.
Algorithms can be used to determine best-fit treatment parameters
to use in treatment sessions. In some embodiments, the system can
provide a predictive end-result image for communication to patient
and/or for determining alteration of desired outcome. In various
embodiments, real-time monitoring of feedback data can be used to
determine treatment plan efficacy. Additional algorithms can
provide real-time comparison of feedback data to anticipated
feedback data, and can be used to change treatment parameters in
real-time to achieve desired effects.
Inventors: |
Allison; John W.; (Los
Altos, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Allison; John W. |
Los Altos |
CA |
US |
|
|
Assignee: |
Zeltiq Aesthetics, Inc.
Pleasanton
CA
|
Family ID: |
42058200 |
Appl. No.: |
13/588948 |
Filed: |
August 17, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12565613 |
Sep 23, 2009 |
8275442 |
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13588948 |
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61100248 |
Sep 25, 2008 |
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Current U.S.
Class: |
601/2 ; 607/101;
607/96 |
Current CPC
Class: |
A61B 34/10 20160201;
A61N 7/00 20130101; A61N 5/00 20130101; A61N 2005/1041 20130101;
A61F 7/10 20130101; A61N 7/02 20130101; A61B 18/203 20130101; A61F
2007/0075 20130101; A61B 2034/256 20160201; A61F 7/00 20130101;
G16H 50/20 20180101; A61B 2034/102 20160201; A61N 2007/0008
20130101; G16H 20/40 20180101; A61B 2018/00452 20130101 |
Class at
Publication: |
601/2 ; 607/96;
607/101 |
International
Class: |
A61F 7/00 20060101
A61F007/00; A61N 7/00 20060101 A61N007/00; A61N 5/00 20060101
A61N005/00 |
Claims
1.-51. (canceled)
52. A method performed using a treatment system for non-invasively
and transdermally altering a patient's subcutaneous fat, the method
comprising: receiving, by the treatment system, patient
information, the patient information including target region
information that indicates a target region of the patient;
receiving, by the treatment system, treatment session information,
the treatment session information is associated with a number of
treatment sessions for the patient; and transmitting the patient
information and the treatment session information over a network;
and storing the transmitted patient information and the transmitted
treatment session information.
53. The method of claim 52, further comprising manually inputting
the patient information into a client computer of the treatment
system.
54. The method of claim 52, further comprising manually inputting
the treatment session information into a client computer of the
treatment system.
55. The method of claim 52 wherein receiving the patient
information comprises imaging a portion of the patient using at
least one medical imaging device.
56. The method of claim 52, further comprising storing
patient-specific treatment session number information that includes
a number of treatment sessions in a treatment plan for the
patient.
57. The method of claim 52 wherein the patient information further
includes the patient's gender, the method further comprises:
generating a patient-specific treatment plan based on the target
region information and the patient's gender, wherein the
patient-specific treatment plan is adapted for implementation by
the treatment system to non-invasively and transdermally alter the
patient's subcutaneous fat.
58. The method of claim 52, further comprising: comparing a number
of treatment sessions in a treatment plan for the patient and the
stored treatment session information; and displaying information
for viewing by an operator of the treatment system, wherein the
displayed information is based on the comparison.
59. The method of claim 52, further comprising storing information
indicating: a number of treatment sessions in a patient plan for
the patient, and a number of completed treatment sessions in the
patient plan.
60. The method of claim 52, further comprising: receiving, by the
treatment system, a desired treatment period for the patient; and
generating a treatment plan for the patient based, at least in
part, on the desired treatment period and the patient information,
wherein the treatment plan provides a plurality of treatment
sessions over the desired treatment period.
61. The method of claim 52, further comprising: receiving, by the
treatment system, patient-specific data associated with the patient
information; displaying a first graphical image representing the
patient-specific data; receiving, by the treatment system,
objective post-treatment data relating to a target treatment
result; and displaying a second graphical image representing the
target treatment result, wherein the second graphical image is
based on the patient-specific data and the objective post-treatment
data.
62. The method of claim 52, further comprising: transmitting
information relating to the treatment session information from the
network to a database after the treatment session information has
been manually inputted into a client computer of the treatment
system.
63. A method performed using a treatment system for non-invasively
and transdermally altering a subject's subcutaneous tissue, the
method comprising: receiving, by the treatment system, subject
information, the subject information including target region
information that indicates a subcutaneous region of the subject to
undergo non-invasive transdermal alteration, number of treatment
sessions associated with the subject, and gender of the subject;
transmitting the subject information over a network; and storing
the subject information that was transmitted over the network.
64. The method of claim 63, further comprising generating, by the
treatment system, a subject-specific treatment plan for
non-invasively and transdermally altering the subject's
subcutaneous tissue, wherein the subject-specific treatment plan is
generated based, at least in part, on the subject information.
65. The method of claim 63, further comprising: non-invasively and
transdermally altering fat of the subcutaneous region by removing
heat from the fat, delivering radio frequency energy to the fat,
and/or delivering ultrasound energy to the fat.
66. A method performed using a treatment system for non-invasively
and transdermally altering a subject's subcutaneous tissue, the
method comprising: receiving, by the treatment system, treatment
session information, wherein the treatment session information is
associated with a number of treatment sessions for non-invasively
and transdermally affecting the subject's subcutaneous lipid-rich
cells; and storing the treatment session information and a
subject-specific treatment plan for achieving a target treatment
result for the subject, the treatment plan including a number of
treatment sessions for the subject.
67. The method of claim 66, further comprising inputting the
treatment session information into a client computer of the
treatment system.
68. The method of claim 66, further comprising: receiving, by the
treatment system, pre-treatment session data; and evaluating the
pre-treatment session data and the treatment session information to
generate an updated treatment plan associated with the subject.
69. The method of claim 66, further comprising: evaluating the
treatment session information and the subject specific treatment
plan for the subject; and non-invasively and transdermally altering
the subject's subcutaneous lipid-rich cells based, at least in
part, on the evaluation.
70. The method of claim 69 wherein non-invasively and transdermally
altering the subject's subcutaneous lipid-rich cells includes:
removing heat from the subject's lipid-rich cells using the
treatment system, delivering high intensity focused ultrasound
energy to the subject's subcutaneous lipid-rich cells using the
treatment system, and/or delivering radio frequency energy to the
subject's subcutaneous lipid-rich cells using the treatment
system.
71. The method of claim 66, further comprising transmitting the
treatment session information to a storage device of the treatment
system.
72. The method of claim 66, further comprising: after receiving the
treatment session information and non-invasively and transdermally
altering the subject's subcutaneous lipid-rich cells, retrieving
information indicating the number of completed treatment sessions
associated with the subject; and transmitting the retrieved
information to a data storage device.
73. The method of claim 72 wherein transmitting the retrieved
information includes transmitting the retrieved information via a
network to the data storage device, the data storage device
includes one or more databases.
Description
CROSS-REFERENCE TO APPLICATION(S) INCORPORATED BY REFERENCE
[0001] The present application claims priority to U.S. Provisional
Patent Application No. 61/100,248 filed Sep. 25, 2008, entitled
"TREATMENT PLANNING SYSTEMS AND METHODS FOR BODY CONTOURING
APPLICATIONS," and incorporated herein in its entirety by
reference.
[0002] The present application incorporates the following
commonly-assigned U.S. Patent Applications herein by reference in
their entirety:
[0003] U.S. patent application Ser. No. 11/750,953, filed on May
18, 2007, entitled "METHOD OF ENHANCED REMOVAL OF HEAT FROM
SUBCUTANEOUS LIPID-RICH CELLS AND TREATMENT APPARATUS HAVING AN
ACTUATOR";
[0004] U.S. Pat. No. 6,032,675 entitled "FREEZING METHOD FOR
CONTROLLED REMOVAL OF FATTY TISSUE BY LIPOSUCTION";
[0005] U.S. Patent Publication No. 2007/0255362 entitled
"CRYOPROTECTANT FOR USE WITH A TREATMENT DEVICE FOR IMPROVED
COOLING OF SUBCUTANEOUS LIPID-RICH CELLS";
[0006] U.S. Patent Publication No. 2007/0198071 entitled "COOLING
DEVICE FOR REMOVING HEAT FROM SUBCUTANEOUS LIPID-RICH CELLS";
[0007] U.S. Patent Publication No. 2008/0077201 entitled "COOLING
DEVICES WITH FLEXIBLE SENSORS";
[0008] U.S. Patent Publication No. 2008/0077211 entitled "COOLING
DEVICE HAVING A PLURALITY OF CONTROLLABLE COOLING ELEMENTS TO
PROVIDE A PREDETERMINED COOLING PROFILE";
[0009] U.S. patent application Ser. No. 11/933,066, filed Oct. 31,
2007, entitled "METHOD AND APPARATUS FOR COOLING SUBCUTANEOUS
LIPID-RICH CELLS OR TISSUE";
[0010] U.S. patent application Ser. No. 11/777,995, filed Jul. 13,
2007, entitled "LIMITING USE OF DISPOSABLE PATIENT PROTECTION
DEVICES";
[0011] U.S. patent application Ser. No. 11/777,992, filed Jul. 13,
2007, entitled "SYSTEM FOR TREATING LIPID-RICH REGIONS";
[0012] U.S. patent application Ser. No. 11/777,999, filed Jul. 13,
2007, entitled "MANAGING SYSTEM TEMPERATURE TO REMOVE HEAT FROM
LIPID-RICH REGIONS";
[0013] U.S. patent application Ser. No. 11/778,003, filed Jul. 13,
2007, entitled "SECURE SYSTEM FOR REMOVING HEAT FROM LIPID-RICH
REGIONS";
[0014] U.S. patent application Ser. No. 11/778,001, entitled "USER
INTERFACES FOR A SYSTEM THAT REMOVES HEAT FROM LIPID-RICH REGIONS,"
filed Jul. 13, 2007; and
[0015] U.S. Patent Publication No. 2008/0077202 entitled "TISSUE
TREATMENT METHODS".
[0016] U.S. patent application Ser. No. 12/337,544 entitled
"SYSTEMS AND METHODS WITH INTERRUPT/RESUME CAPABILITIES FOR COOLING
SUBCUTANEOUS LIPID-RICH CELLS," filed Dec. 17, 2008.
TECHNICAL FIELD
[0017] The present application relates generally to treatment
planning systems and methods including systems and methods for
generating and implementing treatment plans for body contouring
applications and other non-invasive medical applications.
BACKGROUND
[0018] Excess body fat, or adipose tissue, may be present in
various locations of the body, including, for example, the thigh,
buttocks, abdomen, knees, back, face, arms, chin, and other areas.
Excess adipose tissue can detract from personal appearance and
athletic performance. Moreover, excess adipose tissue is thought to
magnify the unattractive appearance of cellulite, which forms when
subcutaneous fat lobules protrude and penetrate into the dermis and
create dimples where the skin is attached to underlying structural
fibrous strands. Cellulite and excessive amounts of adipose tissue
are often considered to be unappealing. Moreover, significant
health risks may be associated with higher amounts of excess body
fat.
[0019] Adipose tissue is subdivided into lobules by connective
collagen tissue called fibrous septae. The fibrous septae, which
are generally oriented perpendicular to the skin surface and anchor
the epidermis and dermis to the underlying fascia and muscle, are
organized within the subcutaneous layer to form a connective web
around the adipose cells. Subcutaneous adipose cells are not
uniformly distributed throughout the subcutaneous tissue layer
(e.g., between the dermis and the muscle layers), but exhibit
regional differences in lobule size and shape. These regional
differences can, in part, be due to gender, age, genetics and
physical conditioning among other physiological factors. The
number, size, distribution and orientation of fibrous septae also
vary by body location, gender and age. For example, histological
studies have shown that fibrous septae architecture in women
differs from that in men.
[0020] In males, fibrous septae form a network of criss-crossing
septa of connective tissue that divide fat-cell chambers into
small, polygonal units. In contrast, fibrous septae in females
generally tend to be oriented perpendicular to the cutaneous
surface, tending to create "fat cell chambers" or "papillae
adiposae" that are columnar in shape and sequestered by the
connective strands and the overlaying dermis layer. When the
fibrous septae are more uniform in size and elasticity as well as
positioned evenly throughout the subcutaneous layer, such as those
characteristic of males, tension and stress is distributed evenly
among the connective strands and the adipose cells are largely
contained within the web of collagen. However, the subcutaneous fat
cell chambers characteristic of females can bulge into the dermis,
thereby changing the appearance of the skin surface. Added weight
(e.g., fat cell lipid volume) may cause enlargement of the fat
lobules, which can then further protrude into the dermis.
Nurnberger, F., Muller, G., "So-Called Cellulite: An Invented
Disease" J. Dermatol. Surg. Oncol. 4:3, 221-229 (1978).
[0021] Cellulite (Gynoid lipodystrophy) is typically a hormonally
mediated condition characterized by the uneven distribution of
adipose tissue in the subcutaneous layer that gives rise to an
irregular, dimpled skin surface common in women. Cellulite-prone
tissue can be characterized by the uneven thickness and
distribution of some fibrous septae strands. Thicker strands can
continue to act as a buttress to herniation and bulging of the
adipose chambers into the dermis; however, thinning strands near
the dermal layer permit the adipocytes to bulge into and penetrate
the dermal layer, and in some cases cause thinning of the dermal
layer. In exacerbated conditions of cellulite, fat lobules are
enlarged near the dermal layer with excessive stored lipids and
bound only by thin and focally loose connective tissue strands.
Pierard, G. E., Nizet, J. L, Pierard-Franchimont, C., "Cellulite:
From Standing Fat Herniation to Hypodermal Stretch Marks," Am. J.
Dermatol. 22:1, 34-37 (2000).
[0022] Various non- and minimally invasive treatment modalities
have been offered for improving the appearance of cellulite,
including cold therapy, the use of heating such as by radio
frequency, microwave, or laser energy, the use of focused
ultrasound energy, mesotherapy, and other techniques.
[0023] A variety of similar and identical methods have been used or
offered to treat individuals having excess body fat and, in many
instances, non-invasive removal of excess subcutaneous adipose
tissue can eliminate unnecessary recovery time and discomfort
associated with invasive procedures such as liposuction.
Conventional non-invasive treatments for removing excess body fat
typically include topical agents, weight-loss drugs, regular
exercise, dieting, or a combination of these treatments. One
drawback of these treatments is that they may not be effective or
even possible under certain circumstances. For example, when a
person is physically injured or ill, regular exercise may not be an
option. Similarly, weight-loss drugs or topical agents are not an
option when they cause an allergic or negative reaction.
Furthermore, fat loss in selective areas of a person's body often
cannot be achieved using general or systemic weight-loss
methods.
[0024] Other methods designed to reduce subcutaneous adipose tissue
include laser-assisted liposuction and mesotherapy. Non-invasive
methods include applying radiant energy to subcutaneous lipid-rich
cells via, e.g., radio frequency and/or light energy, such as
described in U.S. Patent Publication No. 2006/0036300 and U.S. Pat.
No. 5,143,063, a high intensity focused ultrasound (HIFU) radiation
such as described in U.S. Pat. Nos. 6,071,239, 7,258,674 and
7,347,855. Additional methods and devices for non-invasively
reducing subcutaneous adipose tissue by cooling are disclosed in
U.S. Pat. No. 7,367,341 entitled "METHODS AND DEVICES FOR SELECTIVE
DISRUPTION OF FATTY TISSUE BY CONTROLLED COOLING" to Anderson et
al. and U.S. Patent Publication No. 2005/0251120 entitled "METHODS
AND DEVICES FOR DETECTION AND CONTROL OF SELECTIVE DISRUPTION OF
FATTY TISSUE BY CONTROLLED COOLING" to Anderson et al. The entire
disclosures of the references listed in this paragraph are
incorporated herein by reference.
[0025] The process of treating a patient having excess body fat
and/or cellulite with one or more of non-invasive and/or minimally
invasive techniques can include several preparative and planning
stages. For example, a preliminary examination and assessment of
the region to be treated is required. This preliminary examination
is followed by development of a treatment prescription by a medical
professional.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In the drawings, identical reference numbers identify
similar elements or acts. The sizes and relative positions of
elements in the drawings are not necessarily drawn to scale. For
example, the shapes of various elements and angles are not drawn to
scale, and some of these elements are arbitrarily enlarged and
positioned to improve drawing legibility. Further, the particular
shapes of the elements as drawn are not intended to convey any
information regarding the actual shape of the particular elements,
and have been solely selected for ease of recognition in the
drawings.
[0027] FIG. 1 is an isometric view schematically illustrating a
treatment system for treating subcutaneous lipid-rich regions of a
patient in accordance with an embodiment of the disclosure.
[0028] FIG. 2 is a block diagram of a basic and suitable computer
that may employ aspects of the disclosure.
[0029] FIG. 3 is a block diagram illustrating a simple, yet
suitable system in which aspects of the disclosure may operate in a
networked computer environment.
[0030] FIG. 4 is a schematic block diagram illustrating a treatment
planning system for generating patient-specific treatment plans and
anticipated treatment results in accordance with an embodiment of
the disclosure.
[0031] FIG. 5 is a schematic block diagram illustrating an
environment in which the treatment planning system and treatment
system may operate in accordance with an embodiment of the
disclosure.
[0032] FIG. 6 is a schematic block diagram illustrating
subcomponents of the computing device of FIG. 5 in accordance with
an embodiment of the disclosure.
[0033] FIGS. 7A-7D are views of a user interface for interacting
with a treatment plan generator in accordance with an embodiment of
the disclosure.
[0034] FIG. 8 is a schematic block diagram illustrating a data
storage device employed by the treatment planning system of FIG. 4
in accordance with an embodiment of the disclosure.
[0035] FIG. 9 is a schematic block diagram illustrating table data
structures employed by the treatment planning system of FIG. 4 in
accordance with an embodiment of the disclosure.
[0036] FIG. 10 is a flow diagram illustrating a routine for
generating a patient-specific treatment plan invoked by the
treatment planning system in accordance with an embodiment of the
disclosure.
[0037] FIG. 11 is a flow diagram illustrating a routine for
displaying graphical images invoked by the treatment planning
system in accordance with an embodiment of the disclosure.
[0038] FIG. 12 is a flow diagram illustrating another routine for
displaying graphical images invoked by the treatment planning
system and in accordance with an embodiment of the disclosure.
[0039] FIG. 13 is a flow diagram illustrating a routine for
modifying a treatment plan in real-time invoked by the treatment
planning system and in accordance with an embodiment of the
disclosure.
[0040] FIG. 14 is a flow diagram illustrating a routine for
providing a user interface relating to generating a treatment plan
invoked by treatment planning system and in accordance with an
embodiment of the disclosure.
DETAILED DESCRIPTION
A. Embodiments of Treatment Planning Systems And Methods
[0041] 1. System Overview
[0042] Systems and methods are provided herein that enable
generation and implementation of a medical treatment plan for body
contouring applications. In some embodiments, a treatment plan can
be automatically generated and provided to medical personnel and/or
a patient. In further embodiments, the treatment plan can be
automatically implemented, for example, to remove excess body fat,
change a body contour by removing adipose tissue, improving the
appearance of cellulite (which may or may not include the treatment
of adipose tissue), etc. The treatment plan can be based on
patient-specific information, patient desired treatment results, a
priori information and empirically-derived information relating to
previously implemented treatments and treatment results and/or
clinically-based treatment modeling.
[0043] A treatment planning system is described for providing a
recommended treatment strategy for removing excess subcutaneous
adipose tissue, such as by cooling. The treatment planning system
includes a computing device having a processor, memory and data
stored in the memory. In one embodiment, the system can include a
computer network for transmitting treatment plan requests and data,
images and treatment plans. The treatment planning system can also
include a database connected to the computer network for storing a
plurality of model data sets and a plurality of treatment
parameters. The model data sets can include empirically-derived and
a priori information relating to conditions of excess subcutaneous
adipose tissue, treatment parameters and options, and treatment
results.
[0044] The system also includes encoded computing device
instructions for planning treatment. The instructions (e.g., logic
programming) may be stored in the memory and executable by the
processor, or in another embodiment, reside on a server in
communication with the computer network. The instructions include
logic steps that accept patient-specific data describing the
patient's pre-treatment condition, logic steps that accept data
relating to a desired post-treatment outcome, and logic steps that
evaluate the pre-treatment data and desired post-treatment outcome
data relative to the plurality of model data sets. The instructions
can further include logic steps that calculate a best-fit
combination of treatment parameters from the plurality of treatment
parameters to formulate a patient-specific treatment plan.
[0045] In further embodiments, the treatment planning system can
report alternative treatment plans based on specific criteria. For
example, the patient and/or medical personnel may desire to have a
treatment plan that separates a proposed treatment session into
multiple treatment sessions over a given period of time.
[0046] One embodiment of the disclosure is directed to one or more
algorithms to assist a medical practitioner in the selection of a
treatment plan for reduction and/or contouring of a patient's
adipose tissue at a target body region. Generally, the algorithm(s)
includes the steps of 1) acquiring pre-treatment data about the
patient and the target region; 2) evaluating the pre-treatment data
to automatically categorize the patient's target region into one or
more pre-determined classification data sets; 3) acquiring selected
input data about the desired post-treatment outcome; 4)
automatically calculating treatment parameters for treating the
target region and for achieving the desired outcome, 5) predictive
modeling of the post-treatment outcome; and 5) generating one or
more treatment plans.
[0047] In some embodiments, the algorithm(s) can include logic
steps for optimizing and/or changing the anticipated post-treatment
outcome based upon one or more subjective criteria and/or personal
preference. In other embodiments, the algorithm(s) can include
steps for monitoring, in real-time, treatment system feedback data,
comparing the treatment system feedback data to predicted feedback
data based upon the predictive modeling of the anticipated
post-treatment outcome, and when a difference is detected between
actual and predictive feedback, modifying the treatment plan in
real-time such that the treatment achieves the anticipated
post-treatment outcome.
[0048] 2. Suitable Treatment Systems
[0049] FIG. 1 and the following discussion provide a brief, general
description of one example of a suitable treatment system 100 in
which aspects of the disclosure can be implemented. Those skilled
in the relevant art will appreciate that the disclosure can be
practiced with other treatment systems and treatment protocols,
including invasive, minimally invasive, other non-invasive medical
treatment systems, and/or combinations of one or more of the above
for treating a patient. In general, the term "treatment system", as
used generally herein, refers to any of the above system categories
of medical treatment as well as any treatment regimes or medical
device usage.
[0050] In one embodiment, the treatment system 100 is suitable for
treating a subject's subcutaneous adipose tissue, such as by
cooling. The term "subcutaneous tissue" means tissue lying beneath
the dermis and includes subcutaneous fat, or adipose tissue, which
primarily is composed of lipid-rich cells, or adipocytes. When
cooling subcutaneous tissues to a temperature lower than 37.degree.
C., subcutaneous lipid-rich cells can selectively be affected. In
general, the epidermis and dermis of the patient 101 have lower
amounts of unsaturated fatty acids compared to the underlying
lipid-rich cells forming the subcutaneous tissues. Because
non-lipid-rich cells usually can withstand colder temperatures
better than lipid-rich cells, the subcutaneous lipid-rich cells can
selectively be affected while maintaining the integrity of the
non-lipid-rich cells in the dermis, epidermis and other surrounding
tissue. In some embodiments, the treatment system 100 can apply
cooling temperatures to the skin of the patient in a range of from
about -20.degree. C. to about 20.degree. C. In other embodiments,
the cooling temperatures can be from about -20.degree. C. to about
10.degree. C., from about 0.degree. C. to about 20.degree. C., from
about -15.degree. C. to about 5.degree. C., from about -5.degree.
C. to about 15.degree. C., or from about -10.degree. C. to about
0.degree. C.
[0051] Without being bound by theory, the selective effect of
cooling on lipid-rich cells is believed to result in, for example,
membrane disruption, shrinkage, disabling, destroying, removing,
killing, or another method of lipid-rich cell alteration. Such
alteration is believed to be an intermediate and/or final result of
one or more mechanisms acting alone or in combination. It is
thought that such mechanism or mechanisms trigger an apoptotic
cascade, which is believed to be the dominant form of lipid-rich
cell death by non-invasive cooling.
[0052] Apoptosis, also referred to as "programmed cell death", is a
genetically-induced death mechanism by which cells self-destruct
without incurring damage to surrounding tissues. An ordered series
of biochemical events induce cells to morphologically change. These
changes include cellular blebbing, loss of cell membrane asymmetry
and attachment, cell shrinkage, chromatin condensation, and
chromosomal DNA fragmentation. Injury via an external stimulus,
such as cold exposure, is one mechanism that can induce apoptosis
in cells. Nagle, W. A., Soloff, B. L., Moss, A. J. Jr., Henle, K.
J. "Cultured Chinese Hamster Cells Undergo Apoptosis After Exposure
to Cold but Nonfreezing Temperatures" Cryobiology 27, 439-451
(1990).
[0053] One aspect of apoptosis, in contrast to cellular necrosis (a
traumatic form of cell death causing local inflammation), is that
apoptotic cells express and display phagocytic markers on the
surface of the cell membrane, thus marking the cells for
phagocytosis by, for example, macrophages. As a result, phagocytes
can engulf and remove the dying cells (e.g., the lipid-rich cells)
without eliciting an immune response. Temperature exposures that
elicit these apoptotic events in lipid-rich cells may contribute to
long-lasting and/or permanent reduction and reshaping of
subcutaneous adipose tissue.
[0054] Without being bound by theory, one mechanism of apoptotic
lipid-rich cell death by cooling is believed to involve localized
crystallization of lipids within the adipocytes at temperatures
that do not induce crystallization in non-lipid-rich cells. The
crystallized lipids may selectively injure these cells, inducing
apoptosis (and may also induce necrotic death if the crystallized
lipids damage or rupture the bilayer lipid membrane of the
adipocyte). Another mechanism of injury involves the lipid phase
transition of those lipids within the cell's bilayer lipid
membrane, which results in membrane disruption, thereby inducing
apoptosis. This mechanism is well-documented for many cell types
and may be active when adipocytes, or lipid-rich cells, are cooled.
Mazur, P., "Cryobiology: the Freezing of Biological Systems"
Science, 68: 939-949 (1970); Quinn, P. J., "A Lipid Phase
Separation Model of Low Temperature Damage to Biological Membranes"
Cryobiology, 22: 128-147 (1985); Rubinsky, B., "Principles of Low
Temperature Preservation" Heart Failure Reviews, 8, 277-284 (2003).
Other yet-to-be understood apoptotic mechanisms may exist, based on
the relative sensitivity of lipid-rich cells to cooling compared to
non-lipid rich cells.
[0055] In addition to the apoptotic mechanisms involved in
lipid-rich cell death, local cold exposure may induce lipolysis
(i.e., fat metabolism) of lipid-rich cells. For example, cold
stress has been shown to enhance rates of lipolysis from that
observed under normal conditions which serves to further increase
the volumetric reduction of subcutaneous lipid-rich cells.
Vallerand, A. L., Zamecnik. J., Jones, P. J. H., Jacobs, I. "Cold
Stress Increases Lipolysis, FFA Ra and TG/FFA Cycling in Humans"
Aviation, Space and Environmental Medicine 70, 42-50 (1999). In
various embodiments, the system 100 includes a controller, a
computing device, a data acquisition device, a treatment unit, and
one or more applicators. The system can employ these components in
various embodiments to receive a selection of a treatment profile
and apply the selected treatment using an applicator.
[0056] FIG. 1 is an isometric view schematically illustrating a
treatment system 100 for non-invasively removing heat from
subcutaneous lipid-rich regions of a subject patient 101 in
accordance with an embodiment of the disclosure. The system 100 can
include a treatment device 104 including an applicator 105 that
engages a target region of the subject 101. The treatment device
104 can be placed, for example, at an abdominal area 102 of the
subject 101 or another suitable area for cooling or removing heat
from the subcutaneous lipid-rich cells of the subject 101. It will
be understood that treatment devices 104 and applicators 105 can be
provided having various configurations, shapes and sizes suitable
for different body regions and body parts such that any suitable
area for removing heat from a subcutaneous lipid-rich region of the
subject 101 can be achieved.
[0057] An applicator, such as applicator 105, is a component of the
system 100 that cools a region of a subject 101, such as a human or
animal (i.e., "patient"). Various types of applicators may be
applied during treatment, such as a vacuum applicator, a belt
applicator (either of which may be used in combination with a
massage or vibrating capability), and so forth. Each applicator may
be designed to treat identified portions of the patient's body,
such as chin, cheeks, arms, pectoral areas, thighs, calves,
buttocks, abdomen, "love handles", back, and so forth. For example,
the vacuum applicator may be applied at the back region, and the
belt applicator can be applied around the thigh region, either with
or without massage or vibration. Exemplary applicators and their
configurations usable, or adaptable for use, with system 100
variously are described in, e.g., commonly assigned U.S. Patent
Publication Nos. 2007/0198071, 2008/0077201, and 2008/0077211 and
in U.S. patent application Ser. No. 11/750,953. In further
embodiments, the system 100 may also include a patient protection
device (not shown) incorporated into or configured for use with the
applicator that prevents the applicator from directly contacting a
patient's skin and thereby reducing the likelihood of
cross-contamination between patients, minimizing cleaning
requirements for the applicator. The patient protection device may
also include or incorporate various storage, computing, and
communications devices, such as a radio frequency identification
(RFID) component, allowing for example, use to be monitored and/or
metered. Exemplary patient protection devices are described in
commonly assigned U.S. Patent Publication No. 2008/0077201.
[0058] In the present example, the system 100 can further include a
treatment unit 106 and supply and return fluid lines 108a-b between
the treatment device 104 and the treatment unit 106. A treatment
unit 106 is a device that, based on variable power input, can
increase or decrease the temperature at a connected treatment
device 104 that in turn may be attached to or incorporated into the
applicator 105. The treatment unit 106 can remove heat from a
circulating coolant to a heat sink and provide a chilled coolant to
the treatment device 104 via the fluid lines 108a-b. Alternatively,
treatment unit 106 can circulate warm coolant to the treatment
device 104 during periods of warming. Examples of the circulating
coolant include water, glycol, synthetic heat transfer fluid, oil,
a refrigerant, and/or any other suitable heat conducting fluid. The
fluid lines 108a-b can be hoses or other conduits constructed from
polyethylene, polyvinyl chloride, polyurethane, and/or other
materials that can accommodate the particular circulating coolant.
The treatment unit 106 can be a refrigeration unit, a cooling
tower, a thermoelectric chiller, or any other device capable of
removing heat from a coolant. Alternatively, a municipal water
supply (e.g., tap water) can be used in place of the treatment unit
106. One skilled in the art will recognize that there are a number
of other cooling technologies that could be used such that the
treatment unit or chiller need not be limited to those described
herein.
[0059] In this example, the treatment device 104 includes at least
one applicator 105 and is associated with at least one treatment
unit 106. The applicator 105 can provide mechanical energy to
create a vibratory, massage, and/or pulsatile effect. The
applicator 105 can include one or more actuators, such as, motors
with eccentric weight, or other vibratory motors such as hydraulic
motors, electric motors, pneumatic motors, solenoids, other
mechanical motors, piezoelectric shakers, and so on, to provide
vibratory energy or other mechanical energy to the treatment site.
Further examples include a plurality of actuators for use in
connection with a single treatment device 104 and/or applicator 105
in any desired combination. For example, an eccentric weight
actuator can be associated with one treatment device 104 or
applicator 105, while a pneumatic motor, can be associated with
another section of the same treatment device or applicator. This,
for example, would give the operator of the treatment system 100
options for differential treatment of lipid rich cells within a
single region or among multiple regions of the subject 101. The use
of one or more actuators and actuator types in various combinations
and configurations with a treatment device 104 or applicator 105
may be possible.
[0060] The treatment device 104 can include one or more heat
exchanging units. The heat exchanging unit can be a Peltier-type
thermoelectric element, and the treatment device 104 can have
multiple individually controlled heat exchanging units (e.g.,
between 1 and 50, between 10 and 45; between 15 and 21,
approximately 100, etc.) to create a custom spatial cooling profile
and/or a time-varying cooling profile. Each custom treatment
profile can include one or more segments, and each segment can
include a specified duration, a target temperature, and control
parameters for features such as vibration, massage, vacuum, and
other treatment modes. Treatment devices having multiple
individually controlled heat exchanging units are described in
commonly assigned U.S. Patent Publication No. 2008/0077211.
[0061] The system 100 can further include a power supply 110 and a
controller 114 operatively coupled to the treatment device 104 and
the applicator 105. In one embodiment, the power supply 110 can
provide a direct current voltage to the thermoelectric treatment
device 104 and/or the applicator 105 to remove heat from the
subject 101. The controller 114 can monitor process parameters via
sensors (not shown) placed proximate to the treatment device 104
via a control line 116 to, among other things, adjust the heat
removal rate based on the process parameters. The controller 114
can further monitor process parameters to adjust the applicator 105
based on treatment parameters, such as treatment parameters defined
in a custom treatment profile or patient-specific treatment
plan.
[0062] The controller 114 can exchange data with the applicator 105
via an electrical line 112 or, alternatively, via a wireless or an
optical communication link. Note that control line 116 and
electrical line 112 are shown in FIG. 1 without any support
structure. Alternatively, control line 116 and electrical line 112
(and other lines including, but not limited to fluid lines 108a-b)
may be bundled into or otherwise accompanied by a conduit or the
like to protect such lines, enhance ergonomic comfort, minimize
unwanted motion (and thus potential inefficient removal of heat
from subject 101), and to provide an aesthetic appearance to system
100. Examples of such a conduit include a flexible polymeric,
fabric, or composite sheath, an adjustable arm, etc. Such a conduit
(not shown) may be designed (via adjustable joints, etc.) to "set"
the conduit in place for the treatment of subject 101.
[0063] The controller 114 can include any processor, Programmable
Logic Controller, Distributed Control System, secure processor, and
the like. A secure processor can be implemented as an integrated
circuit with access-controlled physical interfaces; tamper
resistant containment; means of detecting and responding to
physical tampering; secure storage; and shielded execution of
computer-executable instructions. Some secure processors also
provide cryptographic accelerator circuitry. Secure storage may
also be implemented as a secure flash memory, secure serial EEPROM,
secure field programmable gate array, or secure
application-specific integrated circuit.
[0064] In another aspect, the controller 114 can receive data from
an input device 118 (shown as a touch screen), transmit data to an
output device 120, and/or exchange data with a control panel (not
shown). The input device 118 can include a keyboard, a mouse, a
stylus, a touch screen, a push button, a switch, a potentiometer, a
scanner, or any other device suitable for accepting user input. The
output device 120 can include a display or touch screen, a printer,
a medium reader, an audio device, any combination thereof, and any
other device or devices suitable for providing user feedback. In
the embodiment of FIG. 1, the output device 120 is a touch screen
that functions as both an input device 118 and an output device
120. The control panel can include visual indicator devices or
controls (e.g., indicator lights, numerical displays, etc.) and/or
audio indicator devices or controls. The control panel may be a
component separate from the input device 118 and/or output device
120, may be integrated with one or more of the devices, may be
partially integrated with one or more of the devices, may be in
another location, and so on. In alternative examples, the control
panel, input device 118, output device 120, or parts thereof
(described herein) may be contained in, attached to, or integrated
with the treatment device 104 and/or applicator 105. In this
example, the controller 114, power supply 110, control panel,
treatment unit 106, input device 118, and output device 120 are
carried by a rack 124 with wheels 126 for portability. In
alternative embodiments, the controller 114 can be contained in,
attached to, or integrated with the treatment device 104 and/or the
applicator 105 and/or the patient protection device described
above. In yet other embodiments, the various components can be
fixedly installed at a treatment site. Further details with respect
to components and/or operation of treatment device 104, treatment
unit 106, applicator 105 and other components may be found in
commonly-assigned U.S. patent application Ser. No. 11/750,953.
[0065] In operation, and upon receiving input to start a treatment
protocol, the controller 114 can cause the applicator 105 to cycle
through each segment of a prescribed treatment plan. In so doing,
the applicator 105 applies power to one or more treatment devices
104, such as thermoelectric coolers (e.g., TEC "zones"), to begin a
cooling cycle and, for example, activate features or modes such as
vibration, massage, vacuum, etc. Using temperature sensors (not
shown) proximate to the one or more treatment devices 104, the
patient's skin, a patient protection device, or other locations or
combinations thereof, the controller 114 determines whether a
temperature or heat flux is at a sufficient temperature close to
the target temperature or heat flux. It will be appreciated that
while a region of the body (e.g., adipose tissue) has been cooled
or heated to the target temperature, in actuality that region of
the body may be close but not equal to the target temperature,
e.g., because of the body's natural heating and cooling variations.
Thus, although the system 100 may attempt to heat or cool the
tissue to the target temperature or to provide by a target heat
flux, a sensor may measure a sufficiently close temperature. If the
target temperature has not been reached, power can be increased or
decreased to change heat flux, to maintain the target temperature
or "set-point." When the prescribed segment duration expires, the
controller 114 may apply the temperature and duration indicated in
the next treatment profile segment. In some embodiments,
temperature can be controlled using a variable other than, or in
addition to, power.
[0066] Although a noninvasive applicator is illustrated and
discussed herein, minimally invasive applicators may also be
employed. In such a case, the applicator and patient protection
device may be integrated. As an example, a cryoprobe that may be
inserted directly into the subcutaneous adipose tissue to cool or
freeze the tissue is an example of such a minimally invasive
applicator. Cryoprobes manufactured by, e.g., Endocare, Inc., of
Irvine, Calif. are suitable for such applications. This patent
application incorporates by reference U.S. Pat. No. 6,494,844,
entitled "DEVICE FOR BIOPSY AND TREATMENT OF BREAST TUMORS"; U.S.
Pat. No. 6,551,255, entitled "DEVICE FOR BIOPSY OF TUMORS"; U.S.
Publication No. 2007-0055173, entitled "ROTATIONAL CORE BIOPSY
DEVICE WITH LIQUID CRYOGEN ADHESION PROBE"; U.S. Pat. No.
6,789,545, entitled "METHOD AND SYSTEM FOR CRYOABLATING
FIBROADENOMAS"; U.S. Publication No. 2004-0215294, entitled
"CRYOTHERAPY PROBE"; U.S. Pat. No. 7,083,612, entitled "CRYOTHERAPY
SYSTEM"; and U.S. Publication No. 2005-0261753, entitled "METHODS
AND SYSTEMS FOR CRYOGENIC COOLING".
[0067] 3. Suitable Computing Environments
[0068] FIG. 2 and the following discussion provide a general
description of a suitable computing environment in which aspects of
the disclosure can be implemented. Although not required, aspects
and embodiments of the disclosure will be described in the general
context of computer-executable instructions, such as routines
executed by a general-purpose computer, e.g., a server or personal
computer. Those skilled in the relevant art will appreciate that
the disclosure can be practiced with other computer system
configurations, including Internet appliances, hand-held devices,
wearable computers, cellular or mobile phones, multi-processor
systems, microprocessor-based or programmable consumer electronics,
set-top boxes, network PCs, mini-computers, mainframe computers and
the like. The disclosure can be embodied in a special purpose
computer or data processor that is specifically programmed,
configured or constructed to perform one or more of the
computer-executable instructions explained in detail below. Indeed,
the term "computer", as used generally herein, refers to any of the
above devices, as well as any data processor.
[0069] The disclosure can also be practiced in distributed
computing environments, where tasks or modules are performed by
remote processing devices, which are linked through a
communications network, such as a Local Area Network ("LAN"), Wide
Area Network ("WAN") or the Internet. In a distributed computing
environment, program modules or sub-routines may be located in both
local and remote memory storage devices. Aspects of the disclosure
described below may be stored or distributed on computer-readable
media, including magnetic and optically readable and removable
computer discs, stored as firmware in chips (e.g., EEPROM chips),
as well as distributed electronically over the Internet or over
other networks (including wireless networks). Those skilled in the
relevant art will recognize that portions of the disclosure may
reside on a server computer, while corresponding portions reside on
a client computer. Data structures and transmission of data
particular to aspects of the disclosure are also encompassed within
the scope of the disclosure.
[0070] Referring to FIG. 2, one embodiment of the disclosure
employs a computer 200, such as a personal computer or workstation,
having one or more processors 201 coupled to one or more user input
devices 202 and data storage devices 204. The computer is also
coupled to at least one output device such as a display device 206
and one or more optional additional output devices 208 (e.g.,
printer, plotter, speakers, tactile or olfactory output devices,
etc.). The computer may be coupled to external computers, such as
via an optional network connection 210, a wireless transceiver 212,
or both.
[0071] The input devices 202 may include a keyboard and/or a
pointing device such as a mouse. Other input devices are possible
such as a microphone, joystick, pen, touch screen, scanner, digital
camera, video camera, and the like. Further input devices can
include medical imaging devices (e.g., Magnetic Resonance Imaging
device, Computed Tomography imaging device, x-ray, ultrasound,
surface profile scanning devices, etc.). The data storage devices
204 may include any type of computer-readable media that can store
data accessible by the computer 200, such as magnetic hard and
floppy disk drives, optical disk drives, magnetic cassettes, tape
drives, flash memory cards, digital video disks (DVDs), Bernoulli
cartridges, RAMs, ROMs, smart cards, etc. Indeed, any medium for
storing or transmitting computer-readable instructions and data may
be employed, including a connection port to or node on a network
such as a local area network (LAN), wide area network (WAN) or the
Internet (not shown in FIG. 2).
[0072] Aspects of the disclosure may be practiced in a variety of
other computing environments. For example, referring to FIG. 3, a
distributed computing environment with a network interface includes
one or more user computers 302 in a system 300 are shown, each of
which includes a browser program module 304 that permits the
computer to access and exchange data with the Internet 306,
including web sites within the World Wide Web portion of the
Internet. The user computers may be substantially similar to the
computer described above with respect to FIG. 2. User computers may
include other program modules such as an operating system, one or
more application programs (e.g., word processing or spread sheet
applications), and the like. The computers may be general-purpose
devices that can be programmed to run various types of
applications, or they may be single-purpose devices optimized or
limited to a particular function or class of functions. More
importantly, while shown with network browsers, any application
program for providing a graphical user interface to users may be
employed, as described in detail below; the use of a web browser
and web interface are only used as a familiar example here.
[0073] At least one server computer 308, coupled to the Internet or
World Wide Web ("Web") 306, performs much or all of the functions
for receiving, routing and storing of electronic messages, such as
web pages, data streams, audio signals, and electronic images.
While the Internet is shown, a private network, such as an intranet
may indeed be preferred in some applications. The network may have
a client-server architecture, in which a computer is dedicated to
serving other client computers, or it may have other architectures
such as a peer-to-peer, in which one or more computers serve
simultaneously as servers and clients. A database 310 or databases,
coupled to the server computer(s), stores much of the web pages and
content exchanged between the user computers. The server
computer(s), including the database(s), may employ security
measures to inhibit malicious attacks on the system, and to
preserve integrity of the messages and data stored therein (e.g.,
firewall systems, secure socket layers (SSL), password protection
schemes, encryption, and the like).
[0074] The server computer 308 may include a server engine 312, a
web page management component 314, a content management component
316 and a database management component 318. The server engine
performs basic processing and operating system level tasks. The web
page management component handles creation and display or routing
of web pages. Users may access the server computer by means of a
URL associated therewith. The content management component handles
most of the functions in the embodiments described herein. The
database management component includes storage and retrieval tasks
with respect to the database, queries to the database, read and
write functions to the database and storage of data such as video,
graphics and audio signals.
[0075] Many of the functional units described herein have been
labeled as modules, in order to more particularly emphasize their
implementation independence. For example, modules may be
implemented in software for execution by various types of
processors, such as processor 201. An identified module of
executable code may, for instance, comprise one or more physical or
logical blocks of computer instructions which may, for instance, be
organized as an object, procedure, or function. The identified
blocks of computer instructions need not be physically located
together, but may comprise disparate instructions stored in
different locations which, when joined logically together, comprise
the module and achieve the stated purpose for the module.
[0076] A module may also be implemented as a hardware circuit
comprising custom VLSI circuits or gate arrays, off-the-shelf
semiconductors such as logic chips, transistors, or other discrete
components. A module may also be implemented in programmable
hardware devices such as field programmable gate arrays,
programmable array logic, programmable logic devices or the
like.
[0077] A module of executable code may be a single instruction, or
many instructions, and may even be distributed over several
different code segments, among different programs, and across
several memory devices. Similarly, operational data may be
identified and illustrated herein within modules, and may be
embodied in any suitable form and organized within any suitable
type of data structure. The operational data may be collected as a
single data set, or may be distributed over different locations
including over different storage devices, and may exist, at least
partially, merely as electronic signals on a system or network.
B. System Components
[0078] FIG. 4 depicts a treatment planning system 400 for
generating patient-specific treatment plans and anticipated
treatment results in accordance with an embodiment of the
disclosure. Use of the system 400 can provide medical practitioners
with technical tools for capturing data related to a patient's
pre-treated target region as well as desired post-treatment
outcome, and analyzing the data sets relative to a priori (e.g.,
reasoned information, computer-simulated-derived, general
information known in the art, etc.) and/or empirically-derived
(e.g., clinical treatment of prior patients) information specific
to the treatment type. The system 400 can further provide medical
practitioners with tools for calculating best-fit treatment
parameters for achieving as near as possible the desired
post-treatment outcome, and communicating an anticipated
post-treatment outcome to the patient. For example, in some
embodiments, the medical practitioner can use the system 400 to
generate visual images of a patient's pre-treated target region as
well as generate simulated images depicting the anticipated
post-treatment outcome. The simulated image can be generated from
the a priori and/or empirically-derived information stored and
accessed from database(s), or in another embodiment, the simulated
image can be created by the system user (e.g., the medical
practitioner, system technician, etc.) through graphic
manipulation. The system 400 can generate one or more
patient-specific treatment plans for achieving the desired and/or
anticipated post-treatment outcome, and in some embodiments, direct
treatment systems to implement the treatment plan.
[0079] The system 400 includes a treatment plan generator 402,
which can reside on a server such as server 308, in communication
with client computers, such as personal computer 410, workstation
412, laptop computer 414, etc. ("client computer"), through a
computer network 406. The computer network 406 can be substantially
similar in structure and function to computer network 306. The
treatment plan generator 402 can be in communication with a data
storage device 408 which can be a repository for one or more
databases 409. The system 400 can also include a printer 416,
and/or other devices in communication with the treatment plan
generator 402 through the computer network 406.
[0080] The treatment plan generator 402 is capable of receiving
patient-specific data and other information relating to treatment
plan requests, comparing patient-specific data to the a priori
and/or empirically-derived information stored and accessed from
database(s) 409, calculating a best-fit combination of treatment
parameters and formulating a treatment plan specific to a request.
The requests and/or treatment plan(s) can be communicated through
the computer network 406 to/from one or more requesting client
computers. Medical practitioners conducting remote physical
examinations in which a target region of a patient is manually
examined by the practitioner, or a target region is imaged by one
or more medical imaging devices, can enter, download, or otherwise
input data into a client computer for transmitting the data to the
treatment plan generator 402. Additionally, the treatment plan
generator 402 and the network 406 can include other add-on systems
(e.g., treatment system 404) arranged in other ways without
departing from the spirit or scope of the present disclosure.
[0081] As described above, the treatment planning system 400 can
include and/or be connected to a treatment system 404, such as the
treatment system 100 described above and with reference to FIG. 1,
via the network 406. In one embodiment, the treatment system 404
can include a treatment device for cooling subcutaneous lipid-rich
cells, for example, to reduce adipose tissue thickness and/or
change a body contour of a patient's target region. One of ordinary
skill in the art will recognize additional embodiments in which a
variety of treatment systems 404 suitable for use with treatment
planning system 400 can be included. For example, the treatment
system 404 can include any treatment device for applying positive
heat transfer (i.e., increasing tissue temperature) or negative
heat transfer (i.e., cooling/deceasing tissue temperature).
[0082] In some embodiments, the treatment planning system 400 can
comprise, include and/or be connected with a treatment system 404
having one or more radio frequency electrode(s), having one or more
ultrasound transducer(s) (e.g., for delivery of focused ultrasound
(FU), high intensity focused ultrasound (HIFU) and/or low intensity
ultrasound energy), one more laser(s), and/or other energy-emitting
devices. For example, the treatment system 404 can be configured
for delivery of HIFU energy, low frequency ultrasound energy,
bipolar radio frequency energy, microwave energy, laser energy,
infrared (IR) heat, etc. to a target region of a patient. In some
embodiments, the treatment system 404 can cause subcutaneous
lipid-rich cells to lyse or otherwise be selectively disrupted. In
further embodiments, the treatment system 404 can cause
denaturation of connective tissue, such as fibrous septae. In other
embodiments, the treatment system 404 can include a device (e.g.,
vacuum, vibration applicator) or means for mechanical disruption of
tissue. In other embodiments, the treatment planning system 400 can
include a plurality of treatment systems 404 suitable for
non-invasive and/or minimally invasive, alteration of a lipid-rich
target region contour.
[0083] Examples of such devices and treatment systems are generally
known in the art and described, e.g., in U.S. patent and Patent
Publication Nos. U.S. Pat. No. 6,071,239, U.S. Pat. No. 6,607,498,
U.S. Pat. No. 7,258,674, U.S. Pat. No. 7,331,951, U.S. Pat. No.
7,347,855, 2005/0154314, 2005/0154431, 2005/0187495, 2006/0036300,
2006/0122509, 2007/0055156, 2007/0219540, 2007/0282318,
2008/0014627, 2008/0248554, 2008/0312651, 2009/0076488,
2009/0171253, and 2009/0221938. The disclosures of the
above-referenced patents and patent publications are incorporated
in their entirety herein by reference.
[0084] The treatment plan generator 402 can be associated directly
with a provider of a priori and empirically-derived information
relating to treatment plans. For example, the treatment plan
generator 402 can be associated with a service provider or clinical
database manager (e.g., hospital, privately or publicly held
company, third party organization, etc.). In another embodiment,
the treatment plan generator 402 can be associated directly with a
provider and/or manufacturer of the treatment system 404. In some
embodiments, the treatment plan generator 402 is in direct
communication with the network 406, which can be operatively
connected to medical institutions and/or medical service providers
for providing efficient and efficacious treatment, and for
providing a higher level of patient satisfaction during all stages
(e.g., pre-treatment, treatment, and post-treatment phases) of
elective and non-elective procedures. In a further embodiment not
shown, the treatment plan generator 402 and data storage device 408
can be hosted directly on an individual client computer and be used
to generate treatment plans in an on-site capacity. In this
embodiment, the client computer and/or data storage device 408 may
be connected to the network 406 for transmitting updated
information (e.g., new treatment protocol information, data
libraries, software updates, etc.) in real-time or in a periodic
manner.
[0085] As illustrated in FIG. 4, the treatment plan generator 402
can include a data acquisition module 418, a treatment plan request
module 420, a treatment modeling module 422, an optimization module
424 and a treatment plan formulation module 426. In other
embodiments, the treatment plan generator 402 can also include one
or more additional modules, such as a real-time optimization module
428, all of which will be described in detail below. In general,
modules 418, 420, 422, 424, 426 and 428 comprise listings of
executable instructions for implementing logical functions which
can be embodied in any computer readable medium for use by or in
connection with instruction execution system or device (e.g.,
computer-based system, processor-containing system, etc.).
[0086] The data acquisition module 418 can be included for
receiving patient-specific pre-treatment data from the client
computer (e.g., via operator input, file download, etc.), wherein
the data relates to a specific patient. The data acquisition module
418 is further configured to create a patient-specific
pre-treatment data set from the received pre-treatment data and, in
one embodiment, deposit that data set into an existing
pre-treatment data set library. The data acquisition module 418 can
be configured to receive a plurality of data characterizing one or
more target regions for medical treatment. In one example, a
patient's target region characteristics and/or measurements are
known and an operator can manually enter the data into a client
computer and transmit the data to the treatment plan generator 402.
In another example, the target region can be scanned or otherwise
imaged using one or more medical imaging devices (e.g., ultrasound
device, MRI, etc.), and the resulting image files, with embedded
data, can be transmitted to the treatment plan generator 402. The
data acquisition module 418 can receive and categorize the target
region data, for example, by formatting the data and/or extracting
the data from the one or more images. If additional data is
required, the data acquisition module 418 can query the operator
for the additional information during the data acquisition logic
steps.
[0087] A pre-treatment data set can include general patient
information such as gender, age, height, weight, etc. The
pre-treatment data set can also include information characterizing
the patient's target region, for example, the target area body
position (e.g., abdominal, love handle, hip, buttocks, back, thigh,
arms, knees, face, chin, etc.), the outer parameter of the effected
region (e.g., shape, size, skin surface area, etc.), adipose tissue
thickness, etc. In one embodiment, the data may indicate, or
otherwise be assumed, that the adipose tissue at the target region
has a uniform thickness. In another embodiment, the pre-treatment
data set may provide more than one thickness measurements, wherein
each measurement corresponds to one or more subset regions within
the target region.
[0088] In some aspects of the disclosure, detailed information
regarding positioning of target region (i.e., relative to one or
more reference points) can be acquired using position sensing
devices in communication with the client computer. For example, a
treatment system applicator can include coupled position sensors.
In an initial examination, a practitioner can place the applicator
over the target region and place a single reference sensor at a
position away from the target region. The client computer can
receive position data indicating the exact position and orientation
of the applicator relative to the reference sensor and transmit the
position data to the treatment plan generator 402. In other
embodiments, wands or other devices having position sensors or
other infra-red and/or scanning capabilities can be used to extract
position and orientation data of the target region.
[0089] In one embodiment, the pre-treatment data set may contain
patient identification information, for example a patient's name or
medical identification number for archiving and retrieval of the
pre-treatment data set to/from the data storage device 408. In a
further embodiment, the pre-treatment data set may include
insurance billing and/or other billing information for automatic
and efficient billing for treatment planning services rendered by
the system 400. Although it has been described that general
patient-specific information and data characterizing a patient's
target region to be treated are included in the pre-treatment data
set, it will be understood by those of ordinary skill in the art
that general patient information can be created, maintained and/or
updated in a separate patient-specific file associated with the
system and/or database 409.
[0090] In yet another embodiment, the pre-treatment data set does
not contain patient-specific identification information, such that
patient identification information is not shared over the network
406 and/or cannot be determined by an operator accessing the
treatment plan generator 402 or data storage device 408. In any of
the above described embodiments, the data acquisition module 418
can assign the pre-treatment data set a unique patient identifier
(e.g., unique identification number, etc.). For example, a unique
identification number can be assigned on a priority basis and/or be
generated in real-time by the data acquisition module 418. The
unique identification number can encode information such as source
(e.g., a medical provider office, a hospital, a specific client
computer, etc.), date/time information, order of receipt, etc.
Communication regarding the pre-treatment data set and/or other
data sets and treatment plans associated with a particular patient
can be communicated in a secure manner between the treatment plan
generator 402 and a patient's provider using the unique
identification number. Furthermore, security of medical data, such
as the pre-treatment data set, can be ensured using encryption and
decryption protocols known and appreciated by those of ordinary
skill in the relevant art.
[0091] The data acquisition module 418 can also be configured to
receive patient-specific objective post-treatment data from the
client computer (e.g., via operator input, file download, etc.),
wherein the objective post-treatment data relates to a desired
post-treatment result. The data acquisition module 418 is further
configured to create a patient-specific objective post-treatment
data set (e.g., a desired and/or anticipated post-treatment data
set). For example, the data acquisition module 418 can receive
desired data elements relating to the expected or desired
improvement to the pre-treatment status (e.g., adipose tissue
reduction expressed in terms of percentage or millimeters, volume
of adipose tissue removed, degree of change in body curvature
and/or target area contour, etc.). The data acquisition module 418
can receive and categorize the objective post-treatment data, and
in one embodiment, deposit the objective post-treatment data set
into an existing objective post-treatment data library.
[0092] The treatment plan request module 420 can be provided to
receive a treatment plan request from the client computer. In one
embodiment, the request indicates a specific pre-treatment data set
upon which to base the treatment plan. The treatment plan request
module 420 is further configured to initiate a treatment plan
generation session corresponding to the indicated pre-treatment
data set. Following reception and categorization of
patient-specific pre-treatment data by the data acquisition module
418, the treatment plan request module 420 can be invoked upon
receiving a user request from a client computer to generate a
treatment plan based upon at least one pre-treatment data set and,
in some embodiments, at least one objective post-treatment data
set. If a treatment plan is requested, the treatment plan request
module 420 searches data storage device 408 to locate and retrieve
1) the patient-specific pre-treatment data set, and, if indicated,
2) the patient-specific objective post-treatment data set.
[0093] The treatment plan request module 420 can also retrieve a
plurality of empirically derived and/or a priori data sets (the
"model data sets") for comparison to the patient-specific data
sets. The model data sets can include information such as the body
position of the target region, the starting point data points
(i.e., before actual and/or theoretical treatment), and the ending
data points (i.e., actual and/or theoretical post-treatment
results). The model data sets correspond to unique combinations of
treatment parameters, wherein the treatment parameters were used
(empirically) and/or modeled (a prion) to create the model data
starting and ending data points.
[0094] The treatment plan request module 420 can invoke search and
retrieve functions to collect the appropriate data sets from the
appropriate databases 409. The predictive modeling module 422 can
receive the accumulated set of search results from the invoked
treatment plan request module 420 and rank the plurality of model
data sets in accordance with a degree of affinity to the 1)
pre-treatment data set, and, if included in the request, 2)
objective post-treatment data set. Those of ordinary skill in the
art will recognize that "ranking" means assigning an order of
relative value to each model data set with respect to the other
model data sets in the database 409.
[0095] For example, a relative ranking code may be assigned to each
compared model data set with a predetermined range, such as 1-100.
Alternatively, the compared model data sets may be ordered in
accordance with their relative value; or, a combination of ordering
and ranking codes may be utilized. In other various embodiments,
compared model data sets may be dropped from the accumulated result
set when the degree of affinity is below a pre-determined threshold
value. The resulting collection, following the aforementioned
ranking/pruning process, can be referred to as a sub-collection of
model data sets from which data weighting, additional data entry
and other optimization (via the optimization module 424) can reduce
to a yet more refined sub-collection of model data sets.
[0096] In one embodiment, the predictive modeling module 422 can
generate and transmit to the client computer a first graphical
image representing the pre-treatment status of the patient's target
region. For example, the predictive modeling module 422 can
generate a graphical display of one or more of the highest ranked
model data sets and/or a combination of model data sets to visually
represent a best fit to the patient-specific pre-treatment data
set. In another embodiment, the pre-treatment data set can be used
to generate the first graphical image through computer-operated
simulation programs and the like.
[0097] The predictive modeling module 422 can also be configured to
generate and transmit to the client computer a second graphical
image representing a desired post-treatment result/outcome. The
second graphical image can be based on the first graphical image
and the objective post-treatment data set. For example, the first
graphical image can be a starting point from which to render the
image by the desired specifications indicated in the objective
post-treatment data set. In another embodiment, the second
graphical image can be a graphical display of one or more of the
highest ranked model data sets (e.g., from the highest ranked model
data set, a composite of a plurality of highly ranked model sets,
etc.), wherein the model data sets are ranked according to a level
of affinity to the patient specific objective post-treatment data
set. In some aspects of the disclosure, patient-specific objective
post-treatment data may not be received. As such, the predictive
modeling module 422 can be configured to generate a second
graphical image representing a recommended post-treatment
outcome.
[0098] In some embodiments, the first and second graphical images
(i.e., "before" and "after" treatment images) can be displayed on a
user interface screen display (described in more detail below)
either simultaneously or sequentially such that the images can be
used to assist communication to a system operator and/or patient.
In other embodiments, the first and second graphical images can
include representation of the pretreated target region and desired
post-treatment result, respectively, in three-dimensions.
[0099] The optimization module 424 can be configured to receive
additional data from the client computer and/or rewrite original or
previous data received by the data acquisition module 418. In one
embodiment, predictive modeling module 422 may require additional
patient-specific pre-treatment data and/or objective post-treatment
data to optimize the ranked order of the collection of model data
sets from which a graphical display can be generated. As such,
optimization module 424 can be invoked to query the client
computer. In another embodiment, the optimization module 424 can
receive instruction from a client computer (e.g., an optimization
command) to alter output from the predictive modeling module 422.
For example, graphical representation of pre-treatment status
and/or post-treatment objective may not represent actual
pre-treatment status and/or desired outcome. In this example,
optimization module 424 can further query the user for additional
information. The optimization module 424 transmits the updated
patient-specific data to the predictive modeling module 422 for
re-ranking the model data sets.
[0100] Upon realizing a final collection of model data sets (e.g.,
following optimization steps), the predictive modeling module 422
can, in one embodiment, generate one or more final graphical
displays (e.g., modify the first and second graphical images,
generate third and fourth graphical images, etc.) of one or more of
the highest ranked model data sets and/or a combination of model
data sets to visually represent the best fit to the
patient-specific pre-treatment data set and post-treatment
desires.
[0101] Also, upon realizing a final collection of model data sets,
the treatment plan formulation module 426 can generate a
patient-specific treatment plan to present to the user for
implementation with the treatment system 404, such as treatment
system 100 (FIG. 1). Treatment plan formulation module 426 can
calculate the best-fit combination of treatment parameters from a
plurality of possible treatment parameters (e.g., applicator
size/shape and relative positioning on the target region, number of
thermoelectric cooler (TEC) zones, number of ultrasound
transducers, the type of ultrasound transducer, the arrangement and
control setup of such transducers (e.g., use of one or more
transducer matrices or arrays), number of radio frequency
electrodes, the type of radio frequency electrodes, the arrangement
of such electrodes, target temperature, duration of treatment,
power, frequency, applicator movement velocity and pattern, and
control parameters for features such as vibration, massage, vacuum,
and other treatment modes) to generate the patient-specific
treatment plan. In one embodiment, the treatment plan formulation
module 426 calculates the best-fit combination of treatment
parameters by determining the unique combination of treatment
parameters corresponding to one or more model data sets having a
highest affinity to the patient-specific data. The treatment plan
formulation model 426 is also configured to output the
patient-specific treatment plan to the client computer for
treatment implementation.
[0102] For instance, in the case of HIFU therapy to selectively
affect tissue such as subcutaneous adipose tissue, a number of
parameters may be considered by treatment plan formulation module
426 in determining an optimal treatment plan. Such parameters
include, by way of example only, and as described in, e.g., U.S.
Pat. No. 7,258,674 and U.S. Patent Publication No. 2006/0122509:
transducer movement (scanning or continuous modes vs. discrete or
jumping modes) to affect continuous vs. discrete lesion fields,
lesion pattern (e.g., linear, circumlinear, etc.), line of therapy
spacing (e.g., between about 1 mm and about 10 mm), energy flux
(e.g., between about 35 J/cm.sup.2 and about 456 J/cm.sup.2),
frequency (e.g., between about 256 kHz and 6 MHz), power (e.g.,
between about 100 watts (acoustic) and about 378 watts (acoustic)),
pulse repetition frequency (e.g., between about 1 kHz and about 10
kHz), burst length (e.g., between about 5 .mu.sec and about 15
.mu.sec), burst mode (continuous vs. pulsed), scan rate (e.g.,
between about 1 mm/sec and about 30 mm/sec), sweep velocity (e.g.,
between about 4 mm/sec and 25 mm/sec), focal depth for one or more
transducers in an array (e.g., between about 0.10 cm and about 4.0
cm), on-off cycle time to promote cooling (e.g., between about 1
second and about 4 seconds), and so forth.
[0103] In the case of the use of microbubble solutions selectively
to affect tissue such as subcutaneous adipose tissue as described
in U.S. Patent Publication Nos. 2008/0014627 and 2008/0248554,
other parameters may be considered by treatment plan formulation
module 426 in determining an optimal treatment plan. Such
parameters may include, by way of example only: microbubble type,
state and composition (e.g., encapsulated vs. unencapsulated
microbubbles, active or dissolved microbubbles, ambient air,
oxygen, carbon dioxide, argon, hydrogen, perfluoropropane and
mixtures thereof, etc.), liquid solution type and composition
(aqueous, saline, degree of tonicity, buffering agents to control
the pH of the liquid solution, surfactants, vasoconstrictors,
anesthetics, etc.), liquid/microbubble ratio, microbubble size,
degree of lysing capability, etc.), needle size and configuration,
microbubble insertion rate and depth, type of ultrasound used to
effect cavitation such as large duty pulsed signals, continuous
wave signals (at frequencies, e.g., between about 500 kHz and 15
MHz), degree of energy focus (focused, unfocused, or defocused),
mechanical index (e.g., between about 0.5 and about 1.9),
transducer type and configuration, acoustic pressure (e.g., between
about 100 kPa and 20 MPa), pulse repetition frequency (e.g.,
greater than 500 Hz), duration of insonation required to both
distribute the microbubbles and to induce transient cavitation, and
so forth.
[0104] In the case of the use of radio frequency heating
selectively to affect tissue such as subcutaneous adipose tissue as
described in, e.g., U.S. Patent Publication Nos. 2007/0282318,
2008/0312651, and 2009/0171253, other parameters may be considered
by treatment plan formulation module 426 in determining an optimal
treatment plan. Such parameters may include, by way of example
only: radio frequency electrode geometric dimensions, type of
coupling (e.g., capacitive or inductive), cooling modality (e.g.,
conduction, forced air, spray cooling, etc.), the use and rate of
electrode movement during treatment, monopolar vs. bipolar
configurations, frequency of the radio energy, electrode movement
rate over the treatment area, cooling requirements, power level,
treatment time, etc. Similarly, for the use of laser heating, other
parameters such as power level, wavelength, dwell time, pulsed vs.
continuous energy, type and degree of cooling used, etc. are
examples of parameters that may be considered by treatment plan
formulation module 426.
[0105] In the case of the use of minimally invasive techniques to
cool or freeze adipose tissue described elsewhere herein by, e.g.,
one or more cryoprobes, other parameters may be considered by
treatment plan formulation module 426 in determining an optimal
treatment plan. Such parameters may include, by way of example
only: cryogen gas temperature, type of cryogen, cryoprobe
dimensions and configuration (e.g., length, diameter, tapered,
cylindrical, etc.), the number of cryoprobes and, in the case of
multiple cryoprobes, their configuration to effect a desired
treatment (e.g., any number of two-dimensional or three-dimensional
arrays, etc.), power level, depth of tissue insertion, orientation
within the tissue, dwell time, and so forth.
[0106] Other techniques for effecting a desired treatment outcome
may require other parameters; such parameters may be incorporated
into the treatment plan formulation module 426 as desired.
[0107] The treatment plan formulation module 426 is configured to
create a treatment plan that is comprehensive for achieving
results. In one embodiment, treatment plan formulation module 426
utilizes additional a priori and empirically-derived information to
account for natural diffusion rates of cold temperature through
subcutaneous adipose tissue. For example, the present inventor
recognized that cold temperatures diffuse to deeper levels as the
thickness of the adipose tissue layer increases. In other
embodiments, the treatment plan formulation module 426 utilizes a
priori and empirically-derived information to account for
applicator edge effects (i.e., temperature differences between the
middle of the applicator plate and the edge of the applicator
plate), and effects of more than one treatment sessions (e.g.,
adjacent target regions, overlapping target regions, etc.). In some
embodiments, the treatment plan includes TEC zone specific
parameters such that each zone is controlled independently of other
zones.
[0108] In another embodiment, the system 400 facilitates periodic,
ongoing evaluation of a patient's actual, monitored progress in
response to the prescribed treatment. For example, a patient's
response data can be collected and compared to the database 408
comprising empirically-derived data sets (e.g., clinical treatment
of prior patients) and/or a priori data sets (e.g., reasoned
information, computer-simulated-derived, general information known
in the art, etc.) which are collectively referred to as the "model
data sets". In one embodiment, one or more model data sets and
associated treatment plans that rank with the highest affinity to
the pre-treatment data set and desired post-treatment data set from
the patient of interest are chosen from the database 408. The
patient's progress at the particular point in time in the treatment
course, e.g., pre-treatment, 1 month post-treatment, 6 months
post-treatment, etc., can be compared relative to the efficiency
and efficacy time line demonstrated by the one or more model data
sets.
[0109] In the instances wherein the actual response matches the
expected response, the information generated from the new patient
can be added to the database 408. If the patient's treatment
outcome differs from the anticipated post-treatment outcome, a root
cause analysis can be performed to identify the source of the
difference. For example, such an analysis could determine if the
source of the difference is a result of patient-specific behavior
(e.g., increased calorie consumption), medication-related effects,
or patient-specific genetics or structural abnormalities not
accounted for in the pre-treatment data set (e.g., greater than
normal connective tissue in the target area, abnormal inflammatory
response, etc.). Alternatively, the analysis could determine if the
result difference was due to human error, such as measurement error
or data entry error. In instances wherein the actual treatment
result differs from the anticipated result, and wherein the root
source analysis determined a verifiable cause for the difference
that does not include human error, the information generated from
the new patient can be added to the database 409. If the number of
model data sets in the database 409 is n, then the information
generated from the new patient can be added as the n.sup.th+1 model
data set. In some embodiments, such information may include
additional data not routinely acquired during a pre-treatment
examination. In these instances, the system's newly acquired data
can be used for querying future operators for more information
and/or for more refined predictive modeling using more or less
model data sets for generating treatment plans for future
patients.
[0110] In one embodiment, the actual results obtained from a first
treatment session can be utilized in the predictive modeling and or
optimization phases for generating future treatment plans for the
same patient. In this embodiment, the treatment plan request module
420 can receive one or more unique identifier codes with the
transmitted request. Presentation of the one or more unique
identifiers can initiate a protocol run by the treatment plan
request module 420 to retrieve the data sets corresponding to the
one or more unique identifiers and preference (e.g., weight) these
data sets with, or in another embodiment, over the model data sets
when generating the predictive model (e.g., by the predictive
modeling module 422) or when optimizing the treatment parameters
(e.g., by the optimization module 424).
[0111] In some aspects of the present disclosure, the system
provides for real-time optimization of the treatment plan. For
example, once the treatment is in progress, the treatment system
404 provides the capability of real-time monitoring the actual
patient response to the treatment. Real-time feedback data can be
collected in the initial treatment stages and compared to the
predicted modeling data generated and/or compiled by the predictive
modeling module 422.
[0112] Accordingly, the treatment plan generator 402 can also
include the real-time optimization module 428 configured to receive
real-time feedback data during treatment administration from the
client computer. When associated with the treatment system 100
(referred to in FIG. 1), the feedback data can include, e.g., heat
flux measurements, such as detected by heat flux sensors in a
treatment system applicator, and/or monitor power usage for drawing
heat from a skin surface. The heat flux measurements can indicate
the thickness of the subcutaneous adipose tissue, for example, by
gauging the distance from the skin to underlying muscle. For
example, the lower the temperature reading, the greater the
thickness. In contrast, the thinner the subcutaneous adipose layer,
the higher the initial temperature measurements (i.e., due to heat
transfer from the underlying muscle tissue).
[0113] Heat flux measurements can indicate other changes or
anomalies that can occur during treatment administration. For
example, an increase in temperature detected by a heat flux sensor
can indicate a freezing event at the skin or underlying tissue
(e.g., dermal tissue). An increase in temperature as detected by
the heat flux sensors can also indicate movement associated with
the applicator, causing the applicator to contact a warmer area of
the skin, for example. Methods and systems for collection of
feedback data and monitoring of temperature measurements are
described in commonly assigned U.S. patent application Ser. No.
12/196,246, entitled "MONITORING THE COOLING OF SUBCUTANEOUS
LIPID-RICH CELLS, SUCH AS THE COOLING OF ADIPOSE TISSUE," filed on
Aug. 21, 2008, which is incorporated herein in its entirety by
reference.
[0114] In one embodiment, the heat flux measurements (e.g.,
feedback data) can be collected during initial stages of treatment
at desired and/or pre-determined time intervals. For example, the
feedback data can include heat flux measurements collected one time
per minute for about the first 5 minutes to about 10 minutes of a
treatment session.
[0115] In other embodiments, feedback data can include skin and/or
other tissue and properties (such as, e.g., temperature, epidermal
and dermal thickness, optical transmissivity, electrical
conductivity/resistivity, thermal conductivity/resistivity, heat
capacity, elasticity, tensile and shear strength, relative
composition of various components such as lipids, water, collagen,
etc.), data relating to the device used, such as, e.g., device
position coordinates, device velocity measurements, pressure
measurements, etc., as detected by, for example, temperature
sensors, tracking sensors, accelerometers, and, e.g., hepatic
sensors associated with the treatment system 404, and as generally
described, e.g., in U.S. patent and Publication Nos. U.S. Pat. No.
7,258,674, U.S. Pat. No. 7,347,855, U.S. Pat. No. 7,532,201,
2005/0154431, 2009/0024023, 2009/0076488, the disclosures of which
are incorporated by reference herein in their entirety.
[0116] The real-time optimization module 428 can also be configured
to compare the real-time feedback data to an anticipated feedback
data. The anticipated feedback data can be based, for example, on
the one or more model data sets having a highest affinity to the
patient-specific data. In another embodiment, the predictive
modeling module 422 can predict anticipated feedback data based on
the pre-treatment data set, the best-fit combination of treatment
parameters and/or additional empirically-derived and/or a priori
information. The real-time optimization module 428 can also be
configured to calculate a difference between the real-time feedback
data and the anticipated feedback data. If the real-time feedback
data is significantly different (i.e., difference is greater than a
pre-determined threshold difference), the real-time optimization
module 428 can modify the best-fit combination of treatment
parameters to generate a modified treatment plan. The modified
treatment plan can be transmitted from the real-time optimization
module 428 to the client computer for changing treatment
administration in real-time.
[0117] In some aspects of the disclosure, the patient-specific data
received by the system 400 includes one or more objective
post-treatment data elements and limited or no patient-specific
pre-treatment data elements. In other aspects, the patient-specific
data received by the system 400 includes estimated pre-treatment
data elements. In these embodiments, the system can include a
real-time optimization module 428 configured to receive real-time
feedback data during treatment administration (e.g., preliminary
and/or "explorative` treatment, etc.) from the client computer to
determine actual target region pre-treatment data. The predictive
modeling module 422 can be configured to receive and compare the
actual target region pre-treatment data to the plurality of model
sets, and to rank the plurality of model data sets in accordance
with a degree of affinity to the actual target region pre-treatment
data, and if provided, objective post-treatment data.
[0118] As described above, the treatment plan formulation module
426 can be configured to calculate the best-fit combination of
treatment parameters to generate the patient-specific treatment
plan. To calculate the best-fit combination of treatment
parameters, the treatment plan formulation module 426 may determine
the unique combination of treatment parameters corresponding to one
or more model data sets having a highest affinity to the actual
target region pre-treatment data and, if provided, objective
post-treatment data. The real-time optimization module 428 can be
configured to deliver the patient-specific treatment plan to the
client computer in real-time. The treatment system 404 can be
configured to receive the patient-specific treatment plan from the
client computer in real-time and modify treatment parameters during
treatment based on the treatment plan (e.g., in an automatic or
semi-automatic manner).
[0119] In current practice, medical practitioners or clinicians
rely heavily upon their own clinical experiences as well as trial
and error methods for examining patients, designing a best-guess
treatment protocol and formulating a treatment prescription for a
particular patient. Typically, these conventional treatment
protocols can be generic, such that multiple patients will be
treated with the identical treatment protocol. The treatment can be
executed using the prescribed treatment system; however, the
generic and/or best-guess protocols and prescriptions can be
subject to highly variable results and an unanticipated outcome in
part because specific knowledge of the patient is not known when
determining the treatment regimen.
[0120] In contrast, the systems and methods disclosed herein
facilitate consistent and optimal results. Additionally, the system
provides practitioners with communication and visual tools for
rendering simulated images of anticipated results. These display
tools allow a practitioner and/or a patient to visualize the
anticipated results before engaging in the treatment course.
Furthermore, upon visualizing the anticipated results, the
practitioner and/or patient have opportunity to request changes
and/or optimize the anticipated outcome based on additional
subjective criteria and preferences. These requested changes can be
incorporated into the final generated treatment plan.
[0121] In particular embodiments, the systems and methods for
treatment planning provided herein can be applied to body
contouring applications using the treatment system 100 described
above with respect to FIG. 1, e.g., to remove excess subcutaneous
adipose tissue by cooling (i.e., generating negative heat
transfer). However, one of ordinary skill in the art will recognize
that the treatment planning systems and methods as described herein
may be applied to planning treatment protocols for a variety of
medical applications. For example, the treatment planning system
can be configured to incorporate other treatment systems for
adipose tissue reduction, such as high intensity focused ultrasound
(HIFU) radiation, radio frequency (RF) and/or light energy,
minimally invasive applications for removing excess subcutaneous
adipose tissue, etc. It is also anticipated that other medical
procedures beyond those used for body contouring and adipose tissue
reduction can employ the treatment planning systems and methods
described herein. For example, physical therapy protocols and
applications (e.g., for recovery following surgery) can be provided
using the treatment planning systems and methods. In yet further
embodiments, the treatment planning system can be configured to
incorporate a plurality of treatment systems. In such embodiments,
the treatment planning system can be used to assess a best-fit
treatment plan by determining the most suitable regime among a host
of regimes available.
C. Embodiments of User Systems And Interfaces
[0122] FIG. 5 is a schematic block diagram illustrating an
environment in which the system may operate in some embodiments.
The environment 500 includes a computing device 506 and a user
interface 508. In the illustrated embodiment, the computing device
506 is integrated with a controller 510; however, in other
embodiments, the computing device 506 can be a separate unit. For
example, the computing device 506 can be any client computer
described above with respect to FIG. 4. In another example, the
computing device 506 can be a single board computer that is adapted
for use within a housing of a treatment system controller 510. The
environment 500 can also include a power supply 502 and, in medical
treatment settings, an isolation transformer 504. The power supply
502 can be any ordinary type of power supply, such as alternating
current or direct current. The isolation transformer 504 can be a
medical grade transformer that isolates the patient from power
fluctuations and problems, such as leakage current, voltage spikes
or dips, and so forth.
[0123] The user interface 508 can include various input devices for
collecting input from a user, such as an operator of the system,
and can also include various output devices, such as for providing
information to the operator, patient, and so forth. In some
embodiments, the computing device 506 can be connected to the
controller 510 to receive input from the controller and provide
commands to the controller. Various components of the system may
connect to other components via wired or wireless connections, such
as Ethernet, serial (e.g., RS-232 or universal serial bus)
connections, parallel connections, IEEE 802.11, IEEE 802.15, IEEE
802.16, "WiMAX," IEEE 1394, infrared, Bluetooth, and so forth.
[0124] The environment 500 can also include one or more imaging
devices 511, such as medical imaging devices, connected to the
computing device 506. For example, imaging devices can include a
Magnetic Resonance Imaging (MRI) device, a Computed Tomography (CT)
imaging device, an x-ray device, a camera, an ultrasound device, a
surface profile scanning device, etc. In one embodiment, the
computing device 506 can receive images and/or other related data
generated from any one of devices 211.
[0125] In another embodiment, additional measuring devices 505
and/or position determination devices 507 can be connected to the
computing device 506. Such devices may acquire data relating to the
relative position of the target region to other anatomical or
artificial reference points, target region surface area and shape,
adipose tissue thickness, etc. In a specific example, the system
can include a wand having a position sensor 507. The wand can relay
information pertaining to relative position of the sensor with
respect to a reference point or other fiduciary. Other measuring
devices 505 may include calipers for pinching and measuring the
thickness of subcutaneous adipose tissue, near-infrared
interactance devices for transmitting infra-red light through the
skin and detecting light reflection and adsorption by the
underlying tissues, ultrasonic fat depth measuring devices,
magnetic resonance imaging devices, etc. One of ordinary skill in
the art will recognize other measuring devices and position
determination devices for characterizing the subcutaneous adipose
tissue of a patient's target region.
[0126] The computing device 506 can also connect to a data
acquisition device 512. The data acquisition device 512 can acquire
data from various components, such as the controller 510, a
suitable treatment unit 514, an applicator 516, a patient
protection device (not shown), and provide the retrieved data to
other components, such as to the computing device 506. In various
embodiments, the data acquisition device 512 can be incorporated
into the controller 510 or applicator 516. As examples, the data
acquisition device 512 can collect information such as how much
power is being applied to treatment devices, the temperature at
each treatment device, the temperature at the patient's skin, the
status of the treatment unit, controller, or applicator, and so
forth.
[0127] The computing device 506 may connect to network resources,
such as other computers 522a-c and one or more data storage devices
518. As examples, the computing device 506 may connect to a server
522a to upload data logs, patient information, use information, and
so forth. The computing device 506 may also connect to a server
522b to download updates to software, lists of applicators or
patient protection devices that should be disabled, and so forth.
The treatment plan generator 402 can reside on any one of servers
522a-c, and accordingly, treatment plan requests can be transmitted
through network resource connections. The computing device 506 can
also connect to the data storage device 518, such as the data
storage device 408 containing a priori information and empirically
derived information for generating treatment plans. As described
above, the computing device 506 may connect to network resources
via a network 520, such as the Internet or an intranet.
[0128] FIG. 6 is a schematic block diagram illustrating
subcomponents of the computing device 506 of FIG. 5 in accordance
with an embodiment of the disclosure. The computing device 506 can
include a processor 601, a memory 602 (e.g., SRAM, DRAM, flash, or
other memory devices), input/output devices 603, and/or subsystems
and other components 604. The computing device 506 can perform any
of a wide variety of computing processing, storage, sensing,
imaging, and/or other functions. Components of the computing device
may be housed in a single unit or distributed over multiple,
interconnected units (e.g., though a communications network). The
components of the computing device 506 can accordingly include
local and/or remote memory storage devices and any of a wide
variety of computer-readable media.
[0129] As illustrated in FIG. 6, the processor 601 can include a
plurality of functional modules 606, such as software modules, for
execution by the processor 601. The various implementations of
source code (i.e., in a conventional programming language) can be
stored on a computer-readable storage medium or can be embodied on
a transmission medium in a carrier wave. The modules 606 of the
processor can include an input module 608, a database module 610, a
process module 612, an output module 614, and, optionally, a
display module 616.
[0130] In operation, the input module 608 accepts an operator input
via the one or more input devices described above with respect to
FIGS. 2 and 5, and communicates the accepted information or
selections to other components for further processing. The database
module 610 organizes records, including patient records,
pre-treatment data sets, generated treatment plans and operating
records, post-treatment results, and other operator activities, and
facilitates storing and retrieving of these records to and from a
data storage device (e.g., internal memory 602, external database
518, etc.). Any type of database organization can be utilized,
including a flat file system, hierarchical database, relational
database, distributed database, etc.
[0131] In the example illustrated in FIG. 5, the process module 612
can generate control variables based on applicator sensor readings,
treatment plan operational parameters, etc., and the output module
614 can communicate operator input to external computing devices
and control variables to the controller 510. Referring to FIG. 6,
the display module 616 can be configured to convert and transmit
processing parameters, sensor readings, input data, treatment plan
modeling and prescribed operational parameters through one or more
connected display devices, such as a display screen, printer,
speaker system, etc.
[0132] In various embodiments, the processor 601 can be a standard
central processing unit or a secure processor. Secure processors
can be special-purpose processors (e.g., reduced instruction set
processor) that can withstand sophisticated attacks that attempt to
extract data or programming logic. The secure processors may not
have debugging pins that enable an external debugger to monitor the
secure processor's execution or registers. In other embodiments,
the system may employ a secure field programmable gate array, a
smartcard, or other secure devices.
[0133] The memory 602 can be standard memory, secure memory, or a
combination of both memory types. By employing a secure processor
and/or secure memory, the system can ensure that data and
instructions are both highly secure and sensitive operations such
as decryption are shielded from observation.
[0134] Referring to FIG. 5, the computing environment 500, and
thereby the treatment planning system 400, can receive user input
in a plurality of formats. In one embodiment, data is received from
a user-operated computer interface 508 (i.e., "user interface"). In
various embodiments, the user interface 508 is associated with the
computing device 506 and can include various input and output
devices, such as a keyboard, a mouse, buttons, knobs, styluses,
trackballs, microphones, touch screens, liquid crystal displays,
light emitting diode displays, lights, speakers, earphones,
headsets, and the like. In other embodiments not shown, the user
interface 508 can be directly associated with the controller 510 or
the applicator 516.
[0135] FIGS. 7A-7D are views of a user interface 700 for
interacting with the treatment plan generator 402 in accordance
with an embodiment of the disclosure. It will be appreciated that
the user interface, screen displays and information expressed via
user interface described below in and depicted in FIGS. 7A-7D are
exemplary only and are not intended to in any way limit the scope
of the disclosure.
[0136] FIG. 7A is a view of a first display screen 702 of a user
interface (UI) 700 for interacting with the treatment plan
generator 402 (FIG. 4) in accordance with an embodiment of the
disclosure. In one embodiment, the UI 700 is a graphical user
interface (GUI) configured to allow a user to operate a software
application, for example. The GUI can accept input via an
integrated touch screen display and/or through devices such as a
keyboard or mouse, and can provide graphical output on the computer
display screen. In another embodiment, the UI 700 is a web-based
user interface that can accept input and provide output by
generating web pages. Input/output information is transmitted via
the internet or other network and viewed by the user using a
network browser or other interface, for example. In web-based
applications, display pages can include known Internet browser
functions (e.g., address fields, back/forward buttons, refresh,
other menu options, etc.) which operations are familiar to those of
ordinary skill in the art and are not further explained.
[0137] A user of the treatment planning system 400 can engage the
UI 700 to send and/or retrieve information regarding one or more
patients during treatment planning sessions. As illustrated in
FIGS. 7A-7D, the UI 700 includes one or more data entry display
screens for initiating and completing a treatment planning session.
Referring to FIG. 7A, the display screen 702 can include a
plurality of data entry fields and/or drop down menus that are
typically present with known browser technology as well as other
windows based applications. As an example, display screen 702 can
provide an entry area 704 for the user to enter non-topical data
(e.g., practitioner identification data, patient identification
data, etc.) to initiate a new treatment planning session, continue
an existing treatment planning session, or to conduct follow-up on
a treatment plan, For example, radial dial selectors 706 allow a
user to select "new patient" or "existing patient." In one
embodiment, an existing patient may include a patient that has had
a previous treatment plan generated or partially generated. Once
entry area 704 has been populated, the user can depress or "click"
an ENTER button 708 to transmit the information.
[0138] FIG. 7B is a view of a second display screen 710 of the UI
700 responsive to user interaction with the first display screen
702 of FIG. 7A and in accordance with an embodiment of the
disclosure. In the illustrated example, wherein the user selected
"new patient", the display screen 710 includes an entry fields 712
for entering additional patient identification information (e.g.,
insurance plan information, medical identification number, etc.)
and/or other non-topical data (e.g., age, gender, height, weight,
prescription medication, medical conditions, skin type/color,
etc.). The user can also indicate other attributes, such as the
patient's pain sensitivity, total number of treatments desired, and
so forth. Once entry fields 712 have been populated, the user can
depress or "click" an ENTER button 714 to transmit the
information.
[0139] FIG. 7C is a view of a third display screen 716 of the UI
700 responsive to user interaction with the second display screen
710 of FIG. 7B and in accordance with an embodiment of the
disclosure. On the display screen 716, the UI 700 can display a
unique identification code 718. For example, the treatment planning
generator 402 (FIG. 4) may generate a patient-specific data file
identifiable by the unique identification code 718 that optionally
blinds the user to the patient's personally-identifiable
information so that any privacy standards that the treatment
situation may require may be met. A user can record the code 718
and/or use the code 718 for future retrieval or referral to the
corresponding patient-specific data file. In the illustrated
example, the display screen 716 can include target region data
entry fields 720. For example, a user can select at field 722 a
body region to be treated (e.g., love handle, abdomen, back, thigh,
chin, buttocks, arms, face, knee, etc.). Data entry fields 720 can
also include pre-treatment data entry fields 724, for entering data
relating to target area surface area, adipose tissue thickness,
etc. In some embodiments, a user may select if the tissue thickness
is uniform or varied. If varied, a user may enter additional data
relating to subsections of the target area. The display screen 716
may also include a file upload function 726 for retrieving and
uploading pre-entered pre-treatment data and/or image files. Once
entry fields 720 have been populated and/or files have been
uploaded at 726, the user can depress or "click" an ENTER button
728 to transmit the information.
[0140] Following transmission of data entered(at display screen
716, the treatment plan generator 402 may generate and transmit to
UI 700 a pre-treatment graphical display or image (not shown) of
the patient's target area. In one embodiment, the graphical display
can be a three-dimensional rendering of the patient's target area.
In some embodiments, the pre-treatment graphical display can be
generated in part from extracted data from uploaded image files. In
other embodiments, the pre-treatment graphical display can be
generated from a combination of previously modeled images (from a
database of modeled generic images and/or images associated with
model data sets) and patient-specific pre-treatment data entered at
display screens 710 and 716. In some embodiments, if the rendered
graphical display does not accurately depict actual target area
appearance, a user can enter additional data or revise data entry
at display screen 716. The pre-treatment graphical display or image
of the patient's target area may be complemented by data from one
or more pre-treatment data entry fields 724 displayed as, e.g.,
alphanumeric characters, overlaid on the graphical display or image
of the patient's target area corresponding to particular points or
locations in the patient's target area. Such an overlay can be an
efficient way to display large amounts of information in a manner
that is readily discernible by the user. These data may
alternatively or additionally be displayed on the display screen
716 of UI 700 in, e.g., tabular format, on different screens, etc.
to provide maximum flexibility in the display of such information
as desired by the user.
[0141] FIG. 7D is a view of a fourth display screen 730 of the UI
700 responsive to user interaction with the third display screen
716 of FIG. 7C and in accordance with an embodiment of the
disclosure. In the illustrated example, the display screen 730 can
include desired post-treatment outcome data entry fields 732, for
entering parameters and/or data representative of a desired
post-treatment outcome (e.g., percent adipose tissue thickness
reduction, millimeter increments of adipose tissue reduction, +/-
percent curvature change in contour profile, amount of volume
reduction, etc.). Once entry fields 732 have been populated, the
user can depress or "click" an ENTER button 734 to transmit the
information.
[0142] Following transmission of data entered at display screen
730, the treatment plan generator 402 may generate and transmit to
UI 700 a predicted post-treatment graphical display or image (not
shown) of the patient's target area. In one embodiment, and as
described above with respect to the pre-treatment graphical
display, the predicted post-treatment graphical display can be a
three-dimensional rendering of the patient's target area. In some
embodiments, the predicted post-treatment graphical display can be
generated in part from a simulation or manipulation of the
pre-treatment graphical display. In other embodiments, the
predicted post-treatment graphical display can be generated from a
combination of previously modeled images (from a database of
modeled generic images and/or images associated with model data
sets), patient-specific pre-treatment data entered at display
screens 710 and 716, and identified desired treatment results
entered at display screen 730. In some embodiments, if the rendered
graphical display does not accurately depict the desired
post-treatment outcome, a user can enter additional data or revise
data entry at display screens 716 and/or 730. In one embodiment,
weighting criteria for data in the database (i.e., a priori
information and/or empirically-derived data stored in data storage
device 408; FIG. 4) can be altered, and these effects can be shown
through graphical display. As described above with reference to the
pre-treatment graphical display, the predicted post-treatment
graphical display or image of the patient's target area may be
complemented by predicted post-treatment data displayed as, e.g.,
alphanumeric characters, overlaid on the graphical display or image
of the patient's target area corresponding to particular points or
locations in the patient's target area. These data may
alternatively or additionally be displayed on the display screen
730 of UI 700 in, e.g., tabular format, on different screens, etc.
to provide maximum flexibility in the display of such information
as desired by the user.
[0143] A user e.g., medical practitioner) may use the pre-treatment
graphical display and the predicted post-treatment graphical
display to present to the patient a visual representation of the
anticipated treatment outcome. Graphical representation of "before"
and "after" states can be an effective means for communicating the
achievable results of treatment and eliminating certain aspects of
patient as well as practitioner uncertainty. In one aspect, the
patient can have peace-of-mind regarding treatment results prior to
engaging in treatment. In another aspect, if the predicted
post-treatment graphical display does not appeal or is otherwise
unsatisfactory to the patient, a user can change desired outcome
parameters to achieve a more desirable post-treatment result.
[0144] It is anticipated that during treatment plan generation,
simulations may be run for one or more treatment plans.
Accordingly, simulations can provide data and/or graphical display
corresponding to the likely post-treatment outcome for multiple
treatment plans in a manner specific for a particular patient
(i.e., using patient-specific data). The effects of the respective
treatments can be visually represented to the practitioner and/or
patient via the UI 700 prior to treatment administration.
[0145] In some embodiments, the UI 700 can display a treatment plan
upon final approval of the input data and/or graphical displays.
For example, the treatment plan generator 402 may transmit the
treatment plan for display in graphs, tables, etc. The treatment
plan can contain all the directive instruction for implementing the
prescribed treatment.
[0146] In the instance where a user selects "existing patient" on
display screen 702, different first, second and third display
screens may be presented in response to user interaction with
previous display screens. For example, a user may be prompted to
input a unique identification code (e.g., to accommodate privacy
considerations), or alternatively, other identification information
such as patient name, medical identification number, etc., for
identifying the existing patient data and/or identifying a previous
treatment planning session. In some instances, a user can input
follow-up treatment data via the UI 700. For example, a patient can
be evaluated at various time points post-treatment and measurement,
imaging files, and/or other subjective or objective observation can
be entered into the system via UI 700.
[0147] As described in more detail below, the databases associated
with the system 400 can be updated with new data and adaptively
incorporate the new data into evaluation and generation of future
treatment plans for the same patient and/or different patients. As
such, entry of actual post-treatment results can increase the
volume and variation of empirically-derived data in the database
(discussed in more detail below with respect to FIG. 8).
D. System Data Structures
[0148] In various embodiments, the system 400 can employ data
structures that are stored in memory, such as in memory associated
with secure processors ("secure processor memory") or in secure
memory associated with client computers. The system 400 can also
employ data structures stored in memory associated with the data
storage device 408. The data structures enable the system 400 to
generate and implement treatment plans, ensure system integrity,
and protect patient privacy. The data structures also enable the
system 400 to model the predicted post-treatment outcome, and
display the predictive models with both visual representation and
treatment parameter schemes. Some of the data structures disclosed
herein can be indicated for read-only access, write-only access, or
read/write access. The type of access can be enforced via a
combination of hardware and/or software. As an example, when a
field of the data structure is marked for read-only access, various
algorithms associated with the system 400 may not attempt to write
to the field. Moreover, the data storage device 408 or memory
device 602 (referring to FIG. 6) storing the data structure may
also prevent the field from being written to. When a field is
marked for read-only access, the field may nevertheless be writable
before it is deployed, such as by the manufacturer or distributor.
As an example, a special encryption key or authentication key may
be employed to write to read-only data structure fields.
[0149] FIG. 8 is a block diagram illustrating the data storage
device 408 employed by the system 400 and FIG. 9 is a block diagram
illustrating table data structures employed by the system 400 in
accordance with various embodiments of the disclosure. While the
data storage device 408 and the table data structures discussed
below illustrate data structures with contents and organization
that are designed to make them more comprehensible by a human
reader, those skilled in the art will appreciate that actual data
storage device 408 and data structures used by the system 400 to
store information may take on other forms without departing from
the scope or spirit of the present disclosure. For example, the
data storage device 408 and/or illustrated data structures may be
organized in a different manner, may contain more or less
information than shown, may be compressed and/or encrypted; etc.
Furthermore, the data stored in the data storage device 408 and/or
data structures can be numerical, textual, graphical, etc. It is
also anticipated that the one or more data sets and subsets can be
organized, linked and retrieved in any manner suitable for the
system 400.
[0150] Referring to FIG. 8, the data storage device 408 can include
one or more databases 409, data libraries, and/or other
empirically-derived and a priori information described herein.
Database 409 and/or data libraries can include multiple data
structures, each having one or more tables of accessible or
archived information. In one embodiment, the database 409 can be a
relational database and can include, multiple tables and/or data
libraries pertaining to pre-treatment data sets 802, objective
post-treatment data sets 804, predictive modeling data sets 806,
actual post-treatment data sets 808, etc. It will be appreciated
that any classification of data sets (e.g., pre-treatment,
post-treatment, etc.) can be further broken down into subsets of
data and the database 409 can include sub-tables within the primary
table structure.
[0151] If a treatment plan is requested, treatment plan request
module 420 searches data storage device 408 to locate and retrieve
1) pre-treatment data set, and 2) desired post-treatment data set,
and predictive modeling module 422 compares the data sets to
empirically derived and/or a priori data sets (the "model data
sets"). As discussed above, treatment plan request module 420 can
invoke search and retrieve functions to collect the appropriate
data sets. As described above with respect to FIG. 4, the
predictive modeling module 422 receives the accumulated set of
search results from the invoked treatment plan request module 420
and ranks the model data sets in accordance with a degree of
affinity to the 1) pre-treatment data set, and/or 2) desired
post-treatment data set.
[0152] Referring to FIG. 9, relational database table 900
illustrates one embodiment of a treatment plan request data search
for high affinity model data sets, wherein user input data captured
by the UI 700 is retained. As an example, table 900 includes
patient-specific pre-treatment data set information captured by the
user interface (UI) 700 at display screen 710 (FIG. 7B), display
screen 716 (FIG. 7C) and 730 (FIG. 7D). In various embodiments, the
table 900 is identified by the unique identification code 718
assigned to a patient-specific data file. Data captured in the
table 900 can be representative of data entered by the user during
its creation or as later modified. Column 910 comprises the
categories of information collected corresponding to entry fields
712 (FIG. 7B), entry fields 724 (FIG. 7C) and entry fields 732
(FIG. 7D).
[0153] Column 920 comprises various sub-categories for each primary
category. For example, table 900, column 920 subcategorizes PATIENT
into GENDER and AGE. Column 930 refers to the information elements
corresponding to the various categories and sub-categories of
column 910 and 920. Referring to FIGS. 7B-7D, in conjunction with
FIG. 9, it is apparent that the exemplary informational and/or data
elements represented in the display screens 710, 716 and 730, such
as Female, Male, Age groups (e.g., 20-39, 40-54, 55-70), Target
Region (e.g., Love Handle, Abdomen, Back, Thigh), Thickness ranges
(e.g., 4-20 mm, 20-40 mm, +40 mm), Percent Reduction ranges (e.g.,
1-5%, 5-10%, 10-15%, 15-20%) are accommodated in column 930 of
table 900.
[0154] Column 940 represents the specified data values for each of
the information elements of column 930. For example, once again
using FIGS. 7B-D, it can be seen that the selection of the radial
button associated with Female at data field 712 is captured in
table 900 by the specification of "YES" in the row
"PATIENT-GENDER-FEMALE-YES". In the like manner, all of the
selections made with the user interface 700 for display pages 710,
716 and 730 are represented in the exemplary relational table 900.
Other information elements and data values associated with display
pages 702, 710, 716 and 730 are not included in exemplary table 900
in the interest of simplicity. However, those of ordinary skill in
the relevant art will appreciate that table 900 will expand as
necessary to accommodate all categories, sub-categories,
information elements and data values specified by the user during a
treatment planning session.
[0155] In various embodiments, additional data structures can be
added, such as to store calibration data, diagnostic data, test
data, security data (e.g., to store security keys), executable
code, and so forth.
E. System Routines
[0156] The system invokes a number of routines. While some of the
routines are described herein, one skilled in the art is capable of
identifying other routines the system could perform. Moreover, the
routines described herein can be altered in various ways. As
examples, the order of illustrated logic may be rearranged,
substeps may be performed in parallel, illustrated logic may be
omitted, other logic may be included, etc.
[0157] FIG. 10 is a flow diagram illustrating a routine 1000 for
generating a patient-specific treatment plan invoked by the system
in some embodiments. The routine 1000 can be invoked by a computing
device, such as a client computer or a server computer coupled to a
computer network. In one embodiment the computing device includes
treatment plan generator. As an example, the computing device may
invoke the routine 1000 after an operator engages a user interface
in communication with the computing device.
[0158] The routine 1000 begins at block 1002 and the data
acquisition module receives patient-specific data (e.g., general
patient information, target region pre-treatment data, etc.) (block
1004) and creates a pre-treatment data set comprising target region
data elements (block 1006). In some embodiments, the treatment plan
includes a treatment plan for non-invasive, transdermal removal of
heat from subcutaneous lipid-rich cells of a patient. In these
embodiments, the patient-specific data can relate to target region
body position (e.g., love handle, abdomen, thigh, buttocks, back,
arms, face, chin, knees, etc.) and/or a subcutaneous adipose tissue
thickness. In one embodiment, the thickness of the subcutaneous
adipose tissue is estimated. In other embodiments, the thickness is
measured with one of a plurality of measuring techniques (e.g., a
pinch test, calipers, etc.). In still further embodiments, the
thickness may be determined from one or more imaging techniques
(e.g., ultrasound, MRI, CT, etc.). In some embodiments, the data
acquisition module also receives patient-specific objective
post-treatment data (e.g., desired post-treatment results, etc.)
and creates an objective post-treatment data set (not shown).
[0159] The treatment plan request module receives a request for
generating a patient-specific treatment plan (block 1008). The
predictive modeling module compares the pre-treatment data set to a
plurality of model data sets (block 1010). The model data sets can
include at least one of empirically-derived data and a priori
information. Additionally, the model data sets can correspond to
unique combinations of possible treatment parameters. For example,
possible treatment parameters for use with the treatment system 100
(with reference to FIG. 1) can include size, type and position of
applicator, number of thermoelectric cooling zones, treatment time
duration and target temperature for each respective zone, number of
treatments, etc. When the treatment planning system includes other
or additional treatment systems 404 (with reference to FIG. 4),
such as those delivering laser, radio frequency (RF) and ultrasound
energies, generating positive heat transfer, delivering injectable
materials, etc., the model data sets can correspond to additional
or alternate treatment parameters such as, for example, number of
piezoelectric elements in an HIFU transducer, number of RF
electrodes, transducer size, focus length, ultrasound energy
frequency, pressure, power (e.g., Watts), pulse repetition
frequency, velocity and pattern of transducer movement, wavelength
of laser, and other parameters as discussed previously herein,
etc.
[0160] Following the comparing step, the predictive modeling module
ranks the plurality of model data sets in accordance with a degree
of affinity to the pre-treatment data set (block 1012).
Additionally, the treatment plan generation module determines the
unique combination of treatment parameters corresponding to one or
more model data sets having a highest affinity to the pre-treatment
data set (block 1014). In one embodiment, the one or more model
data sets having the highest affinity to the pre-treatment data set
include model data sets having an affinity over a pre-established
threshold affinity.
[0161] At block 1016, the treatment plan formulation module
calculates a best-fit combination of treatment parameters from the
unique combination of treatment parameters corresponding to the one
or more model data sets having the highest affinity. In one
embodiment, the best-fit combination can be a composite of
treatment parameters corresponding to multiple model data sets. In
another embodiment, the best-fit combination can include the unique
combination of treatment parameters corresponding to a single model
data set. The treatment plan formulation module also generates the
patient-specific treatment plan for implementation by a treatment
system. The treatment plan includes the best-fit combination of
treatment parameters. In some embodiments, the computing device is
in communication with the treatment system, and the treatment plan
can be automatically implemented using the treatment system without
requiring an operator to manually input the treatment parameters
into a treatment system controller. The routine 1000 may then
continue at block 1020, where it ends.
[0162] FIG. 11 is a flow diagram illustrating a routine 1100 for
displaying graphical images invoked by the system in some
embodiments. The routine 1100 can be invoked by the computing
device of FIG. 10. The routine 1100 begins at block 1012 of FIG. 10
and the predictive modeling module displays a first graphical
image, wherein the first graphical image represents the
pre-treatment data set (block 1102). In one embodiment, the first
graphical image is displayed on a user interface screen display
visible to a system operator. In other embodiments, the first
graphical image is printed, projected, emailed, etc., for
visualization by a system operator.
[0163] At decision block 1104, the routine 1100 determines whether
there is a significant difference between the first graphical image
and the actual pre-treated target region of the patient. In various
embodiments, the significance of the difference between the image
of the pre-treated target region and the actual pre-treated target
region can be specified by an operator, by additional
patient-specific pre-treatment data, and so forth.
[0164] If there is a significant difference, the optimization
module can receive an optimization command and/or additional
pre-treatment data (block 1106). The optimization module also
updates the pre-treatment data set (block 1108). In some
embodiments, updating the pre-treatment data set includes addition
of data to the data set. In other embodiments, updating the
pre-treatment data set can include rewriting data in the
pre-treatment data set to more accurately reflect the actual target
region. Following update of the pre-treatment data set, the
predictive modeling module re-ranks the model data sets (block
1110) and modifies the first graphical image (block 1112). If the
first graphical image is modified in block 1112, the routine 1100
continues at block 1102 wherein the predictive modeling model
displays the first graphical image. The routine 1100 may continue
as before until no significant difference is detected between the
first graphical image and the actual pre-treatment target region.
The routine 1100 may then return to routine 1000 (FIG. 10) at block
1014.
[0165] As an alternative to returning to routine 1000 at block
1014, routine 1100 may continue with additional routine 1200. FIG.
12 is a flow diagram illustrating a routine 1200 for displaying
graphical images invoked by the system in some embodiments. FIG. 11
logic steps have been illustrated in FIG. 12 in dotted lines. The
routine 1200 can be invoked by the computing device of FIG. 10. The
routine 1200 begins at decision block 1104 of FIG. 11. If the
routine 1100 determines there is not a significant difference
between the first graphical image and the actual pre-treated target
region of the patient, the predictive modeling module displays a
second graphical image, wherein the second graphical image
represents a predicted post-treatment result (block 1202). In one
embodiment, the second graphical image can be based on the first
graphical image and other data such as objective post-treatment
data. In other embodiments, the second graphical image can be
generated by the predicative modeling module for representing a
recommended and/or likely post-treatment outcome (e.g., based on a
priori and/or empirically derived information). In one embodiment,
the second graphical image is displayed on a user interface screen
display visible to a system operator. In other embodiments, the
second graphical image is printed, projected, emailed, etc., for
visualization by a system operator.
[0166] At decision block 1204, the routine 1200 determines whether
there is a significant difference between the second graphical
image and a desired post-treatment result. In various embodiments,
the significance of the difference between the image of the desired
post-treatment result and the actual desired post-treatment result
can be specified by an operator, by additional patient-specific
objective post-treatment data and/or re-writing previously received
objected post-treatment data, and so forth. In some embodiments,
the operator may indicate that additional adipose tissue reduction
is desired and/or different body curvature changes are desired.
[0167] As such, if there is a significant difference, the
optimization module can receive an optimization command and/or
additional objective post-treatment data (block 1206). The
optimization module also updates the objective post-treatment data
set (block 1208). In some embodiments, updating the objective
post-treatment data set includes addition of data to the data set.
In other embodiments, updating the objective post-treatment data
set can include rewriting data in the objective post-treatment data
set to more accurately reflect the desired post-treatment result.
Following update of the objective post-treatment data set, the
predictive modeling module re-ranks the model data sets (block
1210) and modifies the second graphical image (block 1212). If the
second graphical image is modified in block 1212, the routine 1200
continues at block 1202 wherein the predictive modeling model
displays the second graphical image. The routine 1200 may continue
as before until no significant difference is detected between the
second graphical image and the desired post-treatment result. The
routine 1200 may then return to routine 1000 (FIG. 10) at block
1014.
[0168] In some embodiments, the first graphical image and the
second graphical image can be displayed simultaneously, or in
another embodiment, sequentially. As such, an operator may
visualize and/or communicate to the patient the likely effect of
treatment. For example, the first graphical image can represent a
"before" image, and the second graphical image can represent an
"after" image.
[0169] FIG. 13 is a flow diagram illustrating a routine 1300 for
modifying a treatment plan in real-time invoked by the system in
some embodiments. The routine 1300 can be invoked by the computing
device of FIG. 10. In one embodiment, the routine 1300 is invoked
by a computing device in communication with a treatment system,
such as the treatment system 100. Additionally, the routine 1300
can be invoked by the computing device for ensuring that treatment
administration will achieve the desired post-treatment outcome as
predicted by routines 1000 and 1200, for example.
[0170] The routine 1300 begins at block 1302 and the real-time
optimization module receives real-time feedback data during
treatment administration (block 1304). In one embodiment, the
treatment system can be administering treatment according to a
previously generated patient-specific treatment plan. In another
embodiment, the treatment system can be administering treatment
without a patient-specific treatment plan. In such an embodiment,
the treatment system can administer a preliminary and/or generic
treatment plan and the real-time optimization module can receive
real-time feedback data to determine actual target region
pre-treatment data. In one embodiment, the treatment system is
configured to non-invasively and transdermally remove heat from
subcutaneous lipid-rich cells of a patient. Feedback data can
include heat flux measurements, for example, as detected by heat
flux sensors in an applicator associated with the treatment
system.
[0171] In other embodiments, the treatment system is configured to
deliver positive heat transfer to subcutaneous lipid rich target
regions of a patient. Such treatment systems may provide feedback
data such as skin and/or other tissue and properties (such as,
e.g., temperature, epidermal and dermal thickness, optical
transmissivity, electrical conductivity/resistivity, thermal
conductivity/resistivity, heat capacity, elasticity, tensile and
shear strength, relative composition of various components such as
lipids, water, collagen, etc.), data relating to the device used,
such as, e.g., device position coordinates, device velocity
measurements, pressure measurements, etc., as detected by, for
example, temperature sensors, tracking sensors, accelerometers,
and, e.g., hepatic sensors associated with the treatment system,
and as generally described, e.g., in U.S. patent and Publication
Nos. U.S. Pat. No. 7,258,674, U.S. Pat. No. 7,347,855, U.S. Pat.
No. 7,532,201, 2005/0154431, 2009/0024023, 2009/0076488, the
disclosures of which are incorporated by reference herein in their
entirety.
[0172] Following block 1304, the real-time optimization module
compares the real-time feedback data to an anticipated feedback
data set. In one embodiment, the anticipated feedback data set is
based on the one or more model data sets having a highest affinity
to the patient-specific pre-treatment and/or objective
post-treatment data sets, as well as the best-fit combination of
treatment parameters. At decision block 1306, the routine 1300
determines if there is a difference between the real-time feedback
data and the anticipated feedback data set. If no significant
difference is detected, the routine 1300 can end at block 1310. In
this embodiment, treatment administration can continue without
altering treatment parameters and/or treatment routines invoked by
the treatment system. In some embodiments, the difference between
the real-time feedback data and the anticipated feedback data set
must exceed a pre-determined threshold difference for the routine
1300 to modify a treatment plan.
[0173] If the difference is significant (e.g., exceeds a
pre-determined threshold level), the real-time optimization module
calculates the difference between the real-time feedback data and
anticipated feedback data to identify treatment parameters that can
be modified (block 1312). At block 1314, the real-time optimization
module modifies the best-fit combination of treatment parameters to
generate a modified treatment plan. In some embodiments, the
modified treatment plan can be administered in real-time. The
routine 1300 may continue as before at block 1304 until no
significant difference is detected between the real-time feedback
data and the anticipated feedback data. The routine 1300 may then
end at block 1310.
[0174] FIG. 14 is a flow diagram illustrating a routine 1400 for
providing a user interface relating to generating a treatment plan
invoked by the system in some embodiments. In some embodiments, the
treatment plan can be for cooling a subcutaneous lipid-rich target
region of a patient. The routine 1400 can be invoked by the
computing device of FIG. 10. The routine 1400 begins at block 1402
and the system can receive patient-specific data (block 1404). At
block 1406, the system can display a first graphical image. In one
embodiment, the first graphical display represents the
patient-specific data (e.g., pre-treatment target region data).
Displaying the first graphical image can include displaying the
image on a user interface display screen, for example. In one
embodiment, the first graphical image includes visual
representation of the patient-specific data in three-dimensions.
The system can receive objective post-treatment data (block 1408).
The objective post-treatment data can include data relating to a
desired treatment result (e.g., percent reduction in subcutaneous
adipose tissue layer, degree of change in target region contours,
etc.).
[0175] At block 1410, the system displays a second graphical image
representing the desired post-treatment result. The second
graphical image can be based upon the patient-specific data and the
objective post-treatment data. In one embodiment, displaying the
second graphical image can include displaying the image on a user
interface display screen. In some embodiments, the second graphical
image includes visual representation of the desired post-treatment
outcome in three-dimensions. The routine 1400 may then continue at
block 1412, where it ends.
[0176] In one embodiment, the first graphical image and the second
graphical image can represent "before" and "after" images enabling
the system to communicate with the operator and/or patient
anticipated post-treatment results. In some embodiment, the system
can receive additional patient-specific data for modifying the
first graphical image and/or the second graphical image.
[0177] The computing device can receive the information collected
at the user interface, information that the data acquisition device
component collects, images collected by medical imaging devices,
and information transmitted via the computer network (e.g., from
servers, treatment planning generator, database(s), etc.), and take
various actions, such as by querying a user interface to request
user input, commanding the controller, transmitting data to
networked servers and/or database(s).
F. Conclusion
[0178] Various embodiments of the technology are described above.
It will be appreciated that details set forth above are provided to
describe the embodiments in a manner sufficient to enable a person
skilled in the relevant art to make and use the disclosed
embodiments. Several of the details and advantages, however, may
not be necessary to practice some embodiments. Additionally, some
well-known structures or functions may not be shown or described in
detail, so as to avoid unnecessarily obscuring the relevant
description of the various embodiments. Although some embodiments
may be within the scope of the claims, they may not be described in
detail with respect to the Figures. Furthermore, features,
structures, or characteristics of various embodiments may be
combined in any suitable manner. Moreover, one skilled in the art
will recognize that there are a number of other technologies that
could be used to perform functions similar to those described above
and so the claims should not be limited to the devices or routines
described herein. While processes or blocks are presented in a
given order, alternative embodiments may perform routines having
steps, or employ systems having blocks, in a different order, and
some processes or blocks may be deleted, moved, added, subdivided,
combined, and/or modified. Each of these processes or blocks may be
implemented in a variety of different ways. Also, while processes
or blocks are at times shown as being performed in series, these
processes or blocks may instead be performed in parallel, or may be
performed at different times. The headings provided herein are for
convenience only and do not interpret the scope or meaning of the
claims.
[0179] The terminology used in the description is intended to be
interpreted in its broadest reasonable manner, even though it is
being used in conjunction with a detailed description of identified
embodiments.
[0180] Unless the context clearly requires otherwise, throughout
the description and the claims, the words "comprise," "comprising,"
and the like are to be construed in an inclusive sense as opposed
to an exclusive or exhaustive sense; that is to say, in a sense of
"including, but not limited to." Words using the singular or plural
number also include the plural or singular number, respectively.
When the claims use the word "or" in reference to a list of two or
more items, that word covers all of the following interpretations
of the word: any of the items in the list, all of the items in the
list, and any combination of the items in the list.
[0181] Any patents, applications and other references, including
any that may be listed in accompanying filing papers, are
incorporated herein by reference. Aspects of the described
technology can be modified, if necessary, to employ the systems,
functions, and concepts of the various references described above
to provide yet further embodiments.
[0182] These and other changes can be made in light of the above
Detailed Description. While the above description details certain
embodiments and describes the best mode contemplated, no matter how
detailed, various changes can be made. Implementation details may
vary considerably, while still being encompassed by the technology
disclosed herein. As noted above, particular terminology used when
describing certain features or aspects of the technology should not
be taken to imply that the terminology is being redefined herein to
be restricted to any specific characteristics, features, or aspects
of the technology with which that terminology is associated. In
general, the terms used in the following claims should not be
construed to limit the claims to the specific embodiments disclosed
in the specification, unless the above Detailed Description section
explicitly defines such terms. Accordingly, the actual scope of the
claims encompasses not only the disclosed embodiments, but also all
equivalents.
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