U.S. patent application number 16/289054 was filed with the patent office on 2019-12-05 for methods of predicting musculoskeletal disease.
The applicant listed for this patent is ImaTx, Inc.. Invention is credited to Philipp Lang, Siau-Way Liew, Daniel Steines, Rene Vargas-Voracek.
Application Number | 20190370961 16/289054 |
Document ID | / |
Family ID | 46300651 |
Filed Date | 2019-12-05 |
![](/patent/app/20190370961/US20190370961A1-20191205-D00000.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00001.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00002.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00003.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00004.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00005.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00006.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00007.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00008.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00009.png)
![](/patent/app/20190370961/US20190370961A1-20191205-D00010.png)
View All Diagrams
United States Patent
Application |
20190370961 |
Kind Code |
A1 |
Liew; Siau-Way ; et
al. |
December 5, 2019 |
Methods of Predicting Musculoskeletal Disease
Abstract
Methods of predicting bone or joint disease in a subject are
disclosed. Methods of determining the effect of a candidate agent
on any subject's risk of developing bone or joint disease are also
disclosed. A method for generating a parameter map from a bone
image of a subject includes obtaining the bone image of the
subject, defining two or more regions of interest (ROIs) in the
image, analyzing a plurality of positions in the ROIs to obtain
measurements for one or more bone microarchitecture parameters and
one or more bone macro-anatomy parameters, and generating the
parameter map from the measurements.
Inventors: |
Liew; Siau-Way; (Pinole,
CA) ; Steines; Daniel; (Lexington, MA) ; Lang;
Philipp; (Lexington, MA) ; Vargas-Voracek; Rene;
(Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ImaTx, Inc. |
Billerica |
MA |
US |
|
|
Family ID: |
46300651 |
Appl. No.: |
16/289054 |
Filed: |
February 28, 2019 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15809366 |
Nov 10, 2017 |
|
|
|
16289054 |
|
|
|
|
14462760 |
Aug 19, 2014 |
|
|
|
15809366 |
|
|
|
|
12948276 |
Nov 17, 2010 |
8818484 |
|
|
14462760 |
|
|
|
|
10753976 |
Jan 7, 2004 |
7840247 |
|
|
12948276 |
|
|
|
|
10665725 |
Sep 16, 2003 |
|
|
|
10753976 |
|
|
|
|
60411413 |
Sep 16, 2002 |
|
|
|
60438641 |
Jan 7, 2003 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 8/0875 20130101;
A61B 8/5223 20130101; A61B 6/482 20130101; G06T 5/001 20130101;
G06T 2200/04 20130101; A61B 6/5217 20130101; A61B 6/583 20130101;
G06T 2207/30008 20130101; G06T 2207/20104 20130101; G06T 7/0012
20130101; A61B 6/505 20130101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; A61B 6/00 20060101 A61B006/00; A61B 8/08 20060101
A61B008/08; G06T 5/00 20060101 G06T005/00 |
Claims
1. A method for treating a patient with disease affecting the
musculoskeletal system of a subject, the method comprising: (a)
obtaining a bone image of the subject; (b) defining two or more
regions of interest (ROIs) in the image; (c) analyzing a plurality
of positions in the ROIs to obtain measurements for one or more
bone microarchitecture parameters and one or more bone
macro-anatomy parameters; (d) generating and analyzing a parameter
map of the measurements to determine a status of the disease, the
parameter map providing a spatial distribution in matrix form of
the measurements; (e) administering an agent for treating the
disease; (e) repeating steps (a)-(e) until analysis of the
parameter map indicates a desired result is achieved in treating
the subject.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. Ser. No.
15/809,366, filed Nov. 10, 2017, which in turn is a continuation of
U.S. Ser. No. 14/462,760, filed Aug. 19, 2014, which in turn is a
continuation of U.S. Ser. No. 12/948,276, filed Nov. 17, 2010, now
U.S. Pat. No. 8,818,484, which in turn is a continuation of U.S.
Ser. No. 10/753,976, filed Jan. 7, 2004, now U.S. Pat. No.
7,840,247, which in turn is a continuation-in-part of U.S. Ser. No.
10/665,725, filed Sep. 16, 2003, which in turn claims the benefit
of U.S. Provisional Patent Application Ser. No. 60/411,413, filed
on Sep. 16, 2002 and also claims the benefit of U.S. Provisional
Patent Application Ser. No. 60/438,641, filed on Jan. 7, 2003, from
which applications priority is hereby claimed under 35 USC
.sctn..sctn. 119/120, the disclosures of which are incorporated by
reference herein in their entirety.
TECHNICAL FIELD
[0002] This invention relates to using imaging methods for
diagnosis, prognostication, monitoring and management of disease,
particularly where that disease affects the musculoskeletal system.
This invention identifies novel imaging markers for use in
diagnosis, prognostication, monitoring and management of disease,
including musculoskeletal disease.
BACKGROUND
[0003] Osteoporosis and osteoarthritis are among the most common
conditions to affect the musculoskeletal system, as well as
frequent causes of locomotor pain and disability. Osteoporosis can
occur in both human and animal subjects (e.g. horses). Osteoporosis
(OP) and osteoarthritis (OA) occur in a substantial portion of the
human population over the age of fifty. The National Osteoporosis
Foundation estimates that as many as 44 million Americans are
affected by osteoporosis and low bone mass. In 1997 the estimated
cost for osteoporosis related fractures was $13 billion. That
figure increased to $17 billion in 2002 and is projected to
increase to $210-240 billion by 2040. Currently it is expected that
one in two women over the age of 50 will suffer an
osteoporosis-related fracture.
[0004] Imaging techniques are important diagnostic tools,
particularly for bone related conditions such as OP and OA.
Currently available techniques for the noninvasive assessment of
the skeleton for the diagnosis of osteoporosis or the evaluation of
an increased risk of fracture include dual x-ray absorptiometry
(DXA) (Eastell et al. (1998) New Engl J. Med 338:736-746);
quantitative computed tomography (QCT) (Cann (1988) Radiology
166:509-522); peripheral DXA (PDXA) (Patel et al. (1999) J Clin
Densitom 2:397-401); peripheral QCT (PQCT) (Gluer et. al. (1997)
Semin Nucl Med 27:229-247); x-ray image absorptiometry (RA) (Gluer
et. al. (1997) Semin Nucl Med 27:229-247); and quantitative
ultrasound (QUS) (Njeh et al. "Quantitative Ultrasound: Assessment
of Osteoporosis and Bone Status" 1999, Martin-Dunitz, London
England; U.S. Pat. No. 6,077,224, incorporated herein by reference
in its entirety). (See, also, WO 9945845; WO 99/08597; and U.S.
Pat. No. 6,246,745).
[0005] DXA of the spine and hip has established itself as the most
widely used method of measuring BMD. Tothill, P. and D. W. Pye,
(1992) Br J Radiol 65:807-813. The fundamental principle behind DXA
is the measurement of the transmission through the body of x-rays
of 2 different photon energy levels. Because of the dependence of
the attenuation coefficient on the atomic number and photon energy,
measurement of the transmission factors at 2 energy levels enables
the area densities (i.e., the mass per unit projected area) of 2
different types of tissue to be inferred. In DXA scans, these are
taken to be bone mineral (hydroxyapatite) and soft tissue,
respectively. However, it is widely recognized that the accuracy of
DXA scans is limited by the variable composition of soft tissue.
Because of its higher hydrogen content, the attenuation coefficient
of fat is different from that of lean tissue. Differences in the
soft tissue composition in the path of the x-ray beam through bone
compared with the adjacent soft tissue reference area cause errors
in the BMD measurements, according to the results of several
studies. Tothill, P. and D. W. Pye, (1992) Br J Radiol, 65:807-813;
Svendsen, O. L., et al., (1995) J Bone Min Res 10:868-873.
Moreover, D.times.A systems are large and expensive, ranging in
price between $75,000 and $150,000.
[0006] Quantitative computed tomography (QCT) is usually applied to
measure the trabecular bone in the vertebral bodies. Cann (1988)
Radiology 166:509-522. QCT studies are generally performed using a
single kV setting (single-energy QCT), when the principal source of
error is the variable composition of the bone marrow. However, a
dual-kV scan (dual-energy QCT) is also possible. This reduces the
accuracy errors but at the price of poorer precision and higher
radiation dose. Like DXA, however, QCT are very expensive and the
use of such equipment is currently limited to few research
centers.
[0007] Quantitative ultrasound (QUS) is a technique for measuring
the peripheral skeleton. Njeh et al. (1997) Osteoporosis Int
7:7-22; Njeh et al. Quantitative Ultrasound: Assessment of
Osteoporosis and Bone Status. 1999, London, England: Martin Dunitz.
There is a wide variety of equipment available, with most devices
using the heel as the measurement site. A sonographic pulse passing
through bone is strongly attenuated as the signal is scattered and
absorbed by trabeculae. Attenuation increases linearly with
frequency, and the slope of the relationship is referred to as
broadband ultrasonic attenuation (BUA; units: dB/MHz). BUA is
reduced in patients with osteoporosis because there are fewer
trabeculae in the calcaneus to attenuate the signal. In addition to
BUA, most QUS systems also measure the speed of sound (SOS) in the
heel by dividing the distance between the sonographic transducers
by the propagation time (units: m/s). SOS values are reduced in
patients with osteoporosis because with the loss of mineralized
bone, the elastic modulus of the bone is decreased. There remain,
however, several limitations to QUS measurements. The success of
QUS in predicting fracture risk in younger patients remains
uncertain. Another difficulty with QUS measurements is that they
are not readily encompassed within the WHO definitions of
osteoporosis and osteopenia. Moreover, no intervention thresholds
have been developed. Thus, measurements cannot be used for
therapeutic decision-making.
[0008] There are also several technical limitations to QUS. Many
devices use a foot support that positions the patient's heel
between fixed transducers. Thus, the measurement site is not
readily adapted to different sizes and shapes of the calcaneus, and
the exact anatomic site of the measurement varies from patient to
patient. It is generally agreed that the relatively poor precision
of QUS measurements makes most devices unsuitable for monitoring
patients' response to treatment. Gluer (1997) J Bone Min Res
12:1280-1288.
[0009] Radiographic absorptiometry (RA) is a technique that was
developed many years ago for assessing bone density in the hand,
but the technique has recently attracted renewed interest. Gluer et
al. (1997) Semin Nucl Med 27:229-247. With this technique, BMD is
measured in the phalanges. The principal disadvantage of RA of the
hand is the relative lack of high turnover trabecular bone. For
this reason, RA of the hand has limited sensitivity in detecting
osteoporosis and is not very useful for monitoring therapy-induced
changes.
[0010] Peripheral x-ray absorptiometry methods such as those
described above are substantially cheaper than DXA and QCT with
system prices ranging between $15,000 and $35,000. However,
epidemiologic studies have shown that the discriminatory ability of
peripheral BMD measurements to predict spine and hip fractures is
lower than when spine and hip BMD measurements are used. Cummings
et al. (1993) Lancet 341:72-75; Marshall et al. (1996) Br Med J
312:1254-1259. The main reason for this is the lack of trabecular
bone at the measurement sites used with these techniques. In
addition, changes in forearm or hand BMD in response to hormone
replacement therapy, bisphosphonates, and selective estrogen
receptor modulators are relatively small, making such measurements
less suitable than measurements of principally trabecular bone for
monitoring response to treatment. Faulkner (1998) J Clin Densitom
1:279-285; Hoskings et al. (1998) N Engl J Med 338:485-492.
Although attempts to obtain information on bone mineral density
from dental x-rays have been attempted (See, e.g., Shrout et al.
(2000) J. Periodonol. 71:335-340; Verhoeven et al. (1998) Clin Oral
Implants Res 9(5):333-342), these have not provided accurate and
reliable results.
[0011] Furthermore, current methods and devices do not generally
take into account bone structure analyses. See, e.g., Ruttimann et
al. (1992) Oral Surg Oral Med Oral Pathol 74:98-110; Southard &
Southard (1992) Oral Surg Oral Med Oral Pathol 73:751-9; White
& Rudolph, (1999) Oral Surg Oral Med Oral Pathol Oral Radiol
Endod 88:628-35.
[0012] The present invention discloses novel methods and techniques
for predicting musculoskeletal disease, particularly methods and
compositions that result in the ability to obtain accurate
predictions about disease based on bone mineral density and/or bone
structure information obtained from images (e.g., radiographic
images) and data.
SUMMARY OF THE EMBODIMENTS
[0013] The invention discloses a method for analyzing at least one
of bone mineral density, bone structure and surrounding tissue. The
method typically comprises: (a) obtaining an image of a subject;
(b) locating a region of interest on the image; (c) obtaining data
from the region of interest; and (d) deriving data selected from
the group of qualitative and quantitative from the image data
obtained at step c.
[0014] A system is also provided for predicting a disease. Any of
these systems can include the steps of: (a) obtaining image data of
a subject; (b) obtaining data from the image data wherein the data
obtained is at least one of quantitative and qualitative data; and
(c) comparing the at least one of quantitative and qualitative data
in step b to at least one of: a database of at least one of
quantitative and qualitative data obtained from a group of
subjects; at least one of quantitative and qualitative data
obtained from the subject; and at least one of a quantitative and
qualitative data obtained from the subject at time Tn.
[0015] In certain aspects, described herein are methods of
diagnosing, monitoring and/or predicting bone or articular disease
(e.g., the risk of fracture) in a subject, the method comprising
the steps of: determining one or more micro-structural parameters,
one or more macroanatomical parameters or biomechanical parameters
of a joint in said subject; and combining at least two of said
parameters to predict the risk of bone or articular disease. The
micro-structural, macroanatomical and/or biomechanical parameters
may be, for example, one or more of the measurements/parameters
shown in Tables 1, 2 and/or 3. In certain embodiments, one or more
micro-structural parameters and one or more macro-anatomical
parameters are combined. In other embodiments, one or more
micro-structural parameters and one or more biomechanical
parameters are combined. In further embodiments, one or more
macroanatomical parameters and one or more biomechanical parameters
are combined. In still further embodiments, one or more
macroanatomical parameters, one or more micro-structural parameters
and one or more biomechanical parameters are combined.
[0016] In any of the methods described herein, the comparing may be
comprise univariate, bivariate and/or multivariate statistical
analysis of one or more of the parameters. In certain embodiments,
the methods may further comprise comparing said parameters to data
derived from a reference database of known disease parameters.
[0017] In any of the methods described herein, the parameters are
determined from an image obtained from the subject. In certain
embodiments, the image comprises one or more regions of bone (e.g.,
patella, femur, tibia, fibula, pelvis, spine, etc). The image may
be automatically or manually divided into two or more regions of
interest. Furthermore, in any of the methods described herein, the
image may be, for example, an x-ray image, a CT scan, an MRI or the
like and optionally includes one or more calibration phantoms.
[0018] In any of the methods described herein, the predicting
includes performing univariate, bivariate or multivariate
statistical analysis of the analyzed data and referencing the
statistical analysis values to a fracture risk model. Fracture risk
models can comprise, for example, data derived from a reference
database of known fracture loads with their corresponding values of
macro-anatomical, micro-anatomical parameters, and/or clinical risk
factors.
[0019] In another aspect, the invention includes a method of
determining the effect of a candidate agent on a subject's
prognosis for musculoskeletal disease comprising: predicting a
first risk of musculoskeletal disease in subject according to any
of the predictive methods described herein; administering a
candidate agent to the subject; predicting a second risk of the
musculoskeletal disease in the subject according to any of the
predictive methods described herein; and comparing the first and
second risks, thereby determining the effect of the candidate on
the subject's prognosis for the disease. In any of these methods,
the candidate agent can be administered to the subject in any
modality, for example, by injection (intramuscular, subcutaneous,
intravenous), by oral administration (e.g., ingestion), topical
administration, mucosal administration or the like. Furthermore,
the candidate agent may be a small molecule, a pharmaceutical, a
biopharmaceutical, an agropharmaceuticals and/or combinations
thereof.
[0020] In other aspects, the invention includes a kit that is
provided for aiding in the prediction of musculoskeletal disease
(e.g., fracture risk). The kit typically comprises a software
program that uses information obtained from an image to predict the
risk or disease (e.g., fracture). The kit can also include a
database of measurements for comparison purposes. Additionally, the
kit can include a subset of a database of measurements for
comparisons.
[0021] In any of these methods, systems or kits, additional steps
can be provided. Such additional steps include, for example,
enhancing image data.
[0022] Suitable subjects for these steps include for example
mammals, humans and horses. Suitable anatomical regions of subjects
include, for example, dental, spine, hip, knee and bone core
x-rays.
[0023] A variety of systems can be employed to practice the
inventions. Typically at least one of the steps of any of the
methods is performed on a first computer. Although, it is possible
to have an arrangement where at least one of the steps of the
method is performed on a first computer and at least one of the
steps of the method is performed on a second computer. In this
scenario the first computer and the second computer are typically
connected. Suitable connections include, for example, a peer to
peer network, direct link, intranet, and internet.
[0024] It is important to note that any or all of the steps of the
inventions disclosed can be repeated one or more times in series or
in parallel with or without the repetition of other steps in the
various methods. This includes, for example repeating the step of
locating a region of interest, or obtaining image data.
[0025] Data can also be converted from 2D to 3D to 4D and back; or
from 2D to 4D. Data conversion can occur at multiple points of
processing the information. For example, data conversion can occur
before or after pattern evaluation and/or analysis.
[0026] Any data obtained, extracted or generated under any of the
methods can be compared to a database, a subset of a database, or
data previously obtained, extracted or generated from the subject.
For example, known fracture load can be determined for a variety of
subjects and some or all of this database can be used to predict
fracture risk by correlating one or more macro-anatomical or
structural parameters (Tables 1, 2 and/or 3) with data from a
reference database of fracture load for age, sex, race, height and
weight matched individuals.
[0027] The present invention provides methods that allow for the
analysis of bone mineral density, bone and/or cartilage structure
and morphology and/or surrounding tissue from images including
electronic images and, accordingly, allows for the evaluation of
the effect(s) of an agent (or agents) on bone and/or cartilage. It
is important to note that an effect on bone and/or cartilage can
occur in agents intended to have an effect, such as a therapeutic
effect, on bone and/or cartilage as well as agents intended to
primarily effect other tissues in the body but which have a
secondary, or tangential, effect on bone and/or cartilage. The
images (e. g., x-ray images) can be, for example, dental, hip,
spine or other radiographs and can be taken from any mammal. The
images can be in electronic format.
[0028] The invention includes a method to derive quantitative
information on bone structure and/or bone mineral density from an
image comprising (a) obtaining an image, wherein the image
optionally includes an external standard for determining bone
density and/or structure; and (b) analyzing the image obtained in
step (a) to derive quantitative information on bone structure. The
image is taken of a region of interest (ROI). Suitable ROI include,
for example, a hip radiograph or a dental x-ray obtained on dental
x-ray film, including the mandible, maxilla or one or more teeth.
In certain embodiments, the image is obtained digitally, for
example using a selenium detector system, a silicon detector system
or a computed radiography system. In other embodiments, the image
can be digitized from film, or another suitable source, for
analysis.
[0029] A method is included where one or more candidate agents can
be tested for its effects on bone. Again, the effect can be a
primary effect or a secondary effect. For example, images obtained
from the subject can be evaluated prior to administration of a
candidate agent to predict the risk of disease in the absence of
the agent. After administration of the candidate agent(s), an
electronic image of the same portion of a bone of the subject can
be obtained and analyzed as described herein to predict the risk of
musculoskeletal disease. The risk of disease prior to
administration of the candidate agent and after administration can
then be compared to determine if the agent had any effect on
disease prognosis. Information on bone structure can relate to a
variety of parameters, including the parameters shown in Table 1,
Table 2 and Table 3, infra. The images or data may also be compared
to a database of images or data (e.g., "known" images or data). The
candidate agent can, for example, be molecules, proteins, peptides,
naturally occurring substances, chemically synthesized substances,
or combinations and cocktails thereof. Typically, an agent includes
one or more drugs. Further, the agent can be evaluated for the
ability to effect bone diseases such as the risk of bone fracture
(e.g., osteoporotic fracture).
[0030] In any of the methods described herein, the analysis can
comprise using one or more computer programs (or units).
Additionally, the analysis can comprise identifying one or more
regions of interest (ROI) in the image, either prior to,
concurrently or after analyzing the image, e.g. for information on
bone mineral density and/or bone structure. The bone density
information can be, for example, areas of highest, lowest or median
density. Bone structural information can be, for example, one or
more of the parameters shown in Table 1, Table 2 and Table 3. The
various analyses can be performed concurrently or in series.
Further, when using two or more indices each of the indices can be
weighted equally or differently, or combinations thereof where more
than two indices are employed. Additionally, any of these methods
can also include analyzing the image for bone mineral density
information using any of the methods described herein.
[0031] Any of the methods described herein can further comprise
applying one or more correction factors to the data obtained from
the image. For example, correction factors can be programmed into a
computer unit. The computer unit can be the same one that performs
the analysis of the image or can be a different unit. In certain
embodiments, the correction factors account for the variation in
soft-tissue thickness in individual subjects.
[0032] These and other embodiments of the subject invention will
readily occur to those of skill in the art in light of the
disclosure herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] FIGS. 1A AND B are block diagrams showing the steps for
extracting data from an image and then deriving quantitative and/or
qualitative data from the image.
[0034] FIGS. 2A-C are diagrams showing an image taken of a region
of anatomical interest further illustrating possible locations of
regions of interest for analysis.
[0035] FIGS. 3A-J illustrate various abnormalities that might occur
including, for example, cartilage defects, bone marrow edema,
subchondral sclerosis, osteophytes and cysts.
[0036] FIGS. 4A AND B are block diagrams of the method of FIG. 1A
showing that the steps can be repeated.
[0037] FIGS. 5A-E are block diagrams illustrating steps involved in
evaluating patterns in an image of a region of interest.
[0038] FIG. 6A-E are block diagrams illustrating steps involved in
deriving quantitative and qualitative data from an image in
conjunction with administering candidate molecules or drugs for
evaluation.
[0039] FIGS. 7A-D are block diagrams illustrating steps involved in
comparing derived quantitative and qualitative information to a
database or to information obtained at a previous time.
[0040] FIGS. 8A-D are block diagrams illustrating steps involved in
comparing converting an image to a pattern of normal and diseased
tissue
[0041] FIG. 9 is a diagram showing the use one or more devices in
the process of developing a degeneration pattern and using a
database for degeneration patterns.
[0042] FIG. 10 depicts regions of interest (ROIs) analyzed in
Example 1.
[0043] FIG. 11 depicts results of biomechanical testing of 15
cadaveric hips and femurs.
[0044] FIG. 12A-B, are reproductions of x-ray images depicting an
exemplary induced fracture in cadaveric femur resulting from
biomechanical testing and load.
[0045] FIG. 13 is a graph depicting correlation of DXA femoral neck
bone mineral density (BMD) versus biochemical fracture load as
evaluated in 15 fresh cadaveric hip samples.
[0046] FIG. 14A-C are graphs depicting correlation of bone
structure versus mechanical fracture load. FIG. 14A depicts
correlation of maximum marrow spacing v. fracture load.
[0047] FIG. 14B depicts correlation of maximum marrow spacing (log)
v. fracture load. FIG. 14C depicts correlation of percentage of
trabecular area v. fracture load.
[0048] FIG. 15A-C are graphs depicting correlation of
macro-anatomical features versus biomechanical fracture load. FIG.
15A depicts correlation of cortical thickness v. fracture load.
FIG. 15B depicts correlation of hip axis length (HAL) V. fracture
load. FIG. 15C depicts correlation of cortical thickness (standard
deviation) versus fracture load.
[0049] FIG. 16 is a graph depicting multivariate analysis using a
combination of bone structural and macro-anatomical parameters and
shows the correlation of predicted fracture load to actual fracture
load.
DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
[0050] The following description is presented to enable any person
skilled in the art to make and use the invention. Various
modifications to the embodiments described will be readily apparent
to those skilled in the art, and the generic principles defined
herein can be applied to other embodiments and applications without
departing from the spirit and scope of the present invention as
defined by the appended claims. Thus, the present invention is not
intended to be limited to the embodiments shown, but is to be
accorded the widest scope consistent with the principles and
features disclosed herein. To the extent necessary to achieve a
complete understanding of the invention disclosed, the
specification and drawings of all issued patents, patent
publications, and patent applications cited in this application are
incorporated herein by reference.
[0051] The practice of the present invention employs, unless
otherwise indicated, currently conventional methods of imaging and
image processing within the skill of the art. Such techniques are
explained fully in the literature. See, e.g., WO 02/22014, X-Ray
Structure Determination: A Practical Guide, 2.sup.nd Edition,
editors Stout and Jensen, 1989, John Wiley & Sons, publisher;
Body CT: A Practical Approach, editor Slone, 1999, McGraw-Hill
publisher; The Essential Physics of Medical Imaging, editors
Bushberg, Seibert, Leidholdt Jr & Boone, 2002, Lippincott,
Williams & Wilkins; X-ray Diagnosis: A Physician's Approach,
editor Lam, 1998 Springer-Verlag, publisher; Dental Radiology:
Understanding the X-Ray Image, editor Laetitia Brocklebank 1997,
Oxford University Press publisher; and Digital Image Processing,
editor Kenneth R. Castleman, 1996 Prentice Hall, publisher; The
Image Processing Handbook, editor John C. Russ, 3.sup.rd Edition,
1998, CRC Press; Active Contours: The Application of Techniques
from Graphics, Vision, Control Theory and Statistics to Visual
Tracking of Shapes in Motion, Editors Andrew Blake, Michael Isard,
1999 Springer Verlag. As will be appreciated by those of skill in
the art, as the field of imaging continues to advance methods of
imaging currently employed can evolve over time. Thus, any imaging
method or technique that is currently employed is appropriate for
application of the teachings of this invention as well as
techniques that can be developed in the future. A further detailed
description of imaging methods is not provided in order to avoid
obscuring the invention.
[0052] As shown in FIG. 1A, the first step is to locate a part of
the body of a subject, for example in a human body, for study 98.
The part of the body located for study is the region of anatomical
interest (RAI). In locating a part of the body for study, a
determination is made to, for example, take an image or a series of
images of the body at a particular location, e.g. hip, dental,
spine, etc. Images include, for example, conventional x-ray images,
x-ray tomosynthesis, ultrasound (including A-scan, B-scan and
C-scan) computed tomography (CT scan), magnetic resonance imaging
(MRI), optical coherence tomography, single photon emission
tomography (SPECT), and positron emission tomography, or such other
imaging tools that a person of skill in the art would find useful
in practicing the invention. Once the image is taken, a region of
interest (ROI) can be located within the image 100. Algorithms can
be used to automatically place regions of interest in a particular
image. See, e.g., Example 1 describing automatic placement of ROIs
in femurs. Image data is extracted from the image 102. Finally,
quantitative and/or qualitative data is extracted from the image
data 120. The quantitative and/or qualitative data extracted from
the image includes, for example, the parameters and measurements
shown in Table 1, Table 2 or 5 Table 3.
[0053] Each step of locating a part of the body for study 98,
optionally locating a region of interest 100, obtaining image data
102, and deriving data 120, can be repeated one or more times
99,101, 103, 121, respectively, as desired.
[0054] As shown in FIG. 1B image data can be optionally enhanced
104 by applying image processing techniques, such as noise
filtering or diffusion filtering, to facilitate further analysis.
Similar to the process shown in FIG. 1A, locating a part of the
body for study 98, optionally locating a region of interest 100,
obtaining image data 102, enhancing image data 104, and deriving
data 120, can be repeated one or more times 99,101, 103, 105, 121,
respectively, as desired.
[0055] As will be appreciated by those of skill in the art, the
parameters and measurements shown in Table 1 are provided for
illustration purposes. It will be apparent that the terms
micro-structural parameters, micro-architecture, micro-anatomic
structure, micro-structural and trabecular architecture may be used
interchangably. In addition, other parameters and measurements,
ratios, derived values or indices can be used to extract
quantitative and/or qualitative information about the ROI without
departing from the scope of the invention. Additionally, where
multiple ROI or multiple derivatives of data are used, the
parameter measured can be the same parameter or a different
parameter without departing from the scope of the invention.
Additionally, data from different ROIs can be combined or compared
as desired.
[0056] Additional measurements can be performed that are selected
based on the anatomical structure to be studied as described
below.
TABLE-US-00001 TABLE 1 Representative Parameters Measured with
Quantitative and Qualitative Image Analysis Methods PARAMETER
MEASUREMENTS Bone density and Calibration phantom equivalent
thickness microstructural (Average intensity value of the region of
interest expressed as parameters thickness of calibration phantom
that would produce the equivalent intensity) Trabecular contrast
Standard deviation of background subtracted ROI Coefficient of
Variation of ROI (Standard deviation/mean) (Trabecular equivalent
thickness/Marrow equivalent thickness) Fractal dimension Hough
transform Fourier spectral analysis (Mean transform coefficient
absolute value and mean spatial first moment) Predominant
orientation of spatial energy spectrum Trabecular area (Pixel count
of extracted trabeculae) Trabecular area/Total area Trabecular
perimeter (Count of trabecular pixels with marrow pixels in their
neighborhood, proximity or vicinity) Trabecular distance transform
(For each trabecular pixel, calculation of distance to closest
marrow pixel) Marrow distance transform (For each marrow pixel,
calculation of distance to closest trabecular pixel) Trabecular
distance transform regional maximal values (mean, min., max, std.
Dev). (Describes thickness and thickness variation of trabeculae)
Marrow distance transform regional maximal values (mean, min., max,
std. Dev) Star volume (Mean volume of all the parts of an object
which can be seen unobscured from a random point inside the object
in all possible directions) Trabecular Bone Pattern Factor (TBPf =
(P1 - P2)/(A1 - A2) where P1 and A1 are the perimeter length and
trabecular bone area before dilation and P2 and A2 corresponding
values after a single pixel dilation, measure of connectivity)
Connected skeleton count or Trees (T) Node count (N) Segment count
(S) Node-to-node segment count (NN) Node-to-free-end segment count
(NF) Node-to-node segment length (NNL) Node-to-free-end segment
length (NFL) Free-end-to-free-end segment length (FFL) Node-to-node
total struts length (NN.TSL) Free-end-to-free-ends total struts
length(FF.TSL) Total struts length (TSL) FF.TSL/TSL NN.TSL/TSL Loop
count (Lo) Loop area Mean distance transform values for each
connected skeleton Mean distance transform values for each segment
(Tb.Th) Mean distance transform values for each node-to-node
segment (Tb.Th.NN) Mean distance transform values for each
node-to-free-end segment (Tb.Th.NF) Orientation (angle) of each
segment Angle between segments Length-thickness ratios
(NNL/Tb.Th.NN) and (NFL/Tb.Th.NF) Interconnectivity index (ICI) ICI
= (N * NN)/(T * (NF + 1)) Cartilage and Total cartilage volume
cartilage Partial/Focal cartilage volume defect/diseased Cartilage
thickness distribution (thickness map) cartilage parameters Mean
cartilage thickness for total region or focal region Median
cartilage thickness for total region or focal region Maximum
cartilage thickness for total region or focal region Minimum
cartilage thickness for total region or focal region 3D cartilage
surface information for total region or focal region Cartilage
curvature analysis for total region or focal region Volume of
cartilage defect/diseased cartilage Depth of cartilage
defect/diseased cartilage Area of cartilage defect/diseased
cartilage 2D or 3D location of cartilage defect/diseased cartilage
in articular surface 2D or 3D location of cartilage defect/diseased
cartilage in relationship to weight-bearing area Ratio: diameter of
cartilage defect or diseased cartilage/thickness of surrounding
normal cartilage Ratio: depth of cartilage defect or diseased
cartilage/thickness of surrounding normal cartilage Ratio: volume
of cartilage defect or diseased cartilage/thickness of surrounding
normal cartilage Ratio: surface area of cartilage defect or
diseased cartilage/total joint or articular surface area Ratio:
volume of cartilage defect or diseased cartilage/total cartilage
volume Other articular Presence or absence of bone marrow edema
parameters Volume of bone marrow edema Volume of bone marrow edema
normalized by width, area, size, volume of femoral
condyle(s)/tibial plateau/patella - other bones in other joints
Presence or absence of osteophytes Presence or absence of
subchondral cysts Presence or absence of subchondral sclerosis
Volume of osteophytes Volume of subchondral cysts Volume of
subchondral sclerosis Area of bone marrow edema Area of osteophytes
Area of subchondral cysts Area of subchondral sclerosis Depth of
bone marrow edema Depth of osteophytes Depth of subchondral cysts
Depth of subchondral sclerosis Volume, area, depth of osteophytes,
subchondral cysts, subchondral sclerosis normalized by width, area,
size, volume of femoral condyle(s)/tibial plateau/patella - other
bones in other joints Presence or absence of meniscal tear Presence
or absence of cruciate ligament tear Presence or absence of
collateral ligament tear Volume of menisci Ratio of volume of
normal to torn/damaged or degenerated meniscal tissue Ratio of
surface area of normal to torn/damaged or degenerated meniscal
tissue Ratio of surface area of normal to torn/damaged or
degenerated meniscal tissue to total joint or cartilage surface
area Ratio of surface area of torn/damaged or degenerated meniscal
tissue to total joint or cartilage surface area Size ratio of
opposing articular surfaces Meniscal subluxation/dislocation in mm
Index combining different articular parameters which can also
include Presence or absence of cruciate or collateral ligament tear
Body mass index, weight, height 3D surface contour information of
subchondral bone Actual or predicted knee flexion angle during gait
cycle (latter based on gait patterns from subjects with matching
demographic data retrieved from motion profile database) Predicted
knee rotation during gait cycle Predicted knee displacement during
gait cycle Predicted load bearing line on cartilage surface during
gait cycle and measurement of distance between load bearing line
and cartilage defect/diseased cartilage Predicted load bearing area
on cartilage surface during gait cycle and measurement of distance
between load bearing area and cartilage defect/diseased cartilage
Predicted load bearing line on cartilage surface during standing or
different degrees of knee flexion and extension and measurement of
distance between load bearing line and cartilage defect/diseased
cartilage Predicted load bearing area on cartilage surface during
standing or different degrees of knee flexion and extension and
measurement of distance between load bearing area and cartilage
defect/diseased cartilage Ratio of load bearing area to area of
cartilage defect/diseased cartilage Percentage of load bearing area
affected by cartilage disease Location of cartilage defect within
load bearing area Load applied to cartilage defect, area of
diseased cartilage Load applied to cartilage adjacent to cartilage
defect, area of diseased cartilage
[0057] Once the data is extracted from the image it can be
manipulated to assess the severity of the disease and to determine
disease staging (e.g., mild, moderate, severe or a numerical value
or index). The information can also be used to monitor progression
of the disease and/or the efficacy of any interventional steps that
have been taken. Finally, the information can be used to predict
the progression of the disease or to randomize patient groups in
clinical trials.
[0058] FIG. 2A illustrates an image 200 taken of an RAI, shown as
202. As shown in FIG. 2A, a single region of interest (ROI) 210 has
been identified within the image. The ROI 210 can take up the
entire image 200, or nearly the entire image. As shown in FIG. 2B
more than one ROI can be identified in an image. In this example, a
first ROI 220 is depicted in one region of the image 200, and a
second ROI 222 is depicted within the image. In this instance,
neither of these ROI overlap or abut. As will be appreciated by a
person of skill in the art, the number of ROI identified in an
image 200 is not limited to the two depicted. Turning now to FIG.
2C another embodiment showing two ROI for illustration purposes is
shown. In this instance, the first ROI 230 and the second ROI 232,
are partially overlapping. As will be appreciated by those of skill
in the art, where multiple ROI are used any or all of the ROI can
be organized such that it does not overlap, it abuts without
overlapping, it overlaps partially, it overlaps completely (for
example where a first ROI is located completely within a second
identified ROI), and combinations thereof. Further the number of
ROI per image 200 can range from one (ROI.sub.1) to n (ROI.sub.n)
where n is the number of ROI to be analyzed.
[0059] Bone density, microarchitecture, macro-anatomic and/or
biomechanical (e.g. derived using finite element modeling) analyses
can be applied within a region of predefined size and shape and
position. This region of interest can also be referred to as a
"window." Processing can be applied repeatedly within the window at
different positions of the image. For example, a field of sampling
points can be generated and the analysis performed at these points.
The results of the analyses for each parameter can be stored in a
matrix space, e.g., where its position corresponds to the position
of the sampling point where the analysis occurred, thereby forming
a map of the spatial distribution of the parameter (a parameter
map). The sampling field can have regular intervals or irregular
intervals with varying density across the image. The window can
have variable size and shape, for example to account for different
patient size or anatomy.
[0060] The amount of overlap between the windows can be determined,
for example, using the interval or density of the sampling points
(and resolution of the parameter maps). Thus, the density of
sampling points is set higher in regions where higher resolution is
desired and set lower where moderate resolution is sufficient, in
order to improve processing efficiency. The size and shape of the
window would determine the local specificity of the parameter.
Window size is preferably set such that it encloses most of the
structure being measured. Oversized windows are generally avoided
to help ensure that local specificity is not lost.
[0061] The shape of the window can be varied to have the same
orientation and/or geometry of the local structure being measured
to minimize the amount of structure clipping and to maximize local
specificity. Thus, both 2D and/or 3D windows can be used, as well
as combinations thereof, depending on the nature of the image and
data to be acquired.
[0062] In another embodiment, bone density, microarchitecture,
macro-anatomic and/or biomechanical (e.g. derived using finite
element modeling) analyses can be applied within a region of
predefined size and shape and position. The region is generally
selected to include most, or all, of the anatomic region under
investigation and, preferably, the parameters can be assessed on a
pixel-by-pixel basis (e.g., in the case of 2D or 3D images) or a
voxel-by-voxel basis in the case of cross-sectional or volumetric
images (e.g., 3D images obtained using MR and/or CT).
Alternatively, the analysis can be applied to clusters of pixels or
voxels wherein the size of the clusters is typically selected to
represent a compromise between spatial resolution and processing
speed. Each type of analysis can yield a parameter map.
[0063] Parameter maps can be based on measurement of one or more
parameters in the image or window; however, parameter maps can also
be derived using statistical methods. In one embodiment, such
statistical comparisons can include comparison of data to a
reference population, e.g. using a z-score or a T-score. Thus,
parameter maps can include a display of z-scores or T-scores.
[0064] Additional measurements relating to the site to be measured
can also be taken. For example, measurements can be directed to
dental, spine, hip, knee or bone cores. Examples of suitable site
specific measurements are shown in Table 2.
TABLE-US-00002 TABLE 2 Site specific measurement of bone parameters
Parameters All microarchitecture parameters on structures parallel
to specific to stress lines hip images All microarchitecture
parameters on structures perpendic- ular to stress lines Geometry
Shaft angle Neck angle Average and minimum diameter of femur neck
Hip axis length CCD (caput-collum-diaphysis) angle Width of
trochanteric region Largest cross-section of femur head Standard
deviation of cortical bone thickness within ROI Minimum, maximum,
mean and median thickness of cortical bone within ROI Hip joint
space width Parameters All microarchitecture parameters on vertical
structures specific to All microarchitecture parameters on
horizontal structures spine images Geometry Superior endplate
cortical thickness (anterior, center, posterior) Inferior endplate
cortical thickness (anterior, center, posterior) Anterior vertebral
wall cortical thickness (superior, center, inferior) Posterior
vertebral wall cortical thickness (superior, center, inferior)
Superior aspect of pedicle cortical thickness inferior aspect of
pedicle cortical thickness Vertebral height (anterior, center,
posterior) Vertebral diameter (superior, center, inferior), Pedicle
thickness (supero-inferior direction). Maximum vertebral height
Minimum vertebral height Average vertebral height Anterior
vertebral height Medial vertebral height Posterior vertebral height
Maximum inter-vertebral height Minimum inter-vertebral height
Average inter-vertebral height Parameters Average medial joint
space width specific to Minimum medial joint space width knee
images Maximum medial joint space width Average lateral joint space
width Minimum lateral joint space width Maximum lateral joint space
width
[0065] As will be appreciated by those of skill in the art,
measurement and image processing techniques are adaptable to be
applicable to both microarchitecture and macro-anatomical
structures. Examples of these measurements are shown in Table
3.
TABLE-US-00003 TABLE 3 Measurements applicable on Microarchitecture
and Macro-anatomical Structures Average density Calibrated density
of ROI measurement Measurements on micro- The following parameters
are derived from the extracted structures: anatomical structures of
Calibrated density of extracted structures dental, spine, hip, knee
or Calibrated density of background bone cores images Average
intensity of extracted structures Average intensity of background
(area other than extracted structures) Structural contrast (average
intensity of extracted structures/ average intensity of background)
Calibrated structural contrast (calibrated density extracted
structures/calibrated density of background) Total area of
extracted structures Total area of ROI Area of extracted structures
normalized by total area of ROI Boundary lengths (perimeter) of
extracted normalized by total area of ROI Number of structures
normalized by area of ROI Trabecular bone pattern factor; measures
concavity and convexity of structures Star volume of extracted
structures Star volume of background Number of loops normalized by
area of ROI Measurements on The following statistics are measured
from the distance transform Distance transform of regional maximum
values: extracted structures Average regional maximum thickness
Standard deviation of regional maximum thickness Largest value of
regional maximum thickness Median of regional maximum thickness
Measurements on Average length of networks (units of connected
segments) skeleton of extracted Maximum length of networks
structures Average thickness of structure units (average distance
transform values along skeleton) Maximum thickness of structure
units (maximum distance transform values along skeleton) Number of
nodes normalized by ROI area Number of segments normalized by ROI
area Number of free-end segments normalized by ROI area Number of
inner (node-to-node) segments normalized ROI area Average segment
lengths Average free-end segment lengths Average inner segment
lengths Average orientation angle of segments Average orientation
angle of inner segments Segment tortuosity; a measure of
straightness Segment solidity; another measure of straightness
Average thickness of segments (average distance transform values
along skeleton segments) Average thickness of free-end segments
Average thickness of inner segments Ratio of inner segment lengths
to inner segment thickness Ratio of free-end segment lengths to
free-end segment thickness Interconnectivity index; a function of
number of inner segments, free-end segments and number of networks.
Directional skeleton All measurement of skeleton segments can be
constrained by segment one or more desired orientation by measuring
only skeleton measurements segments within ranges of angle.
Watershed Watershed segmentation is applied to gray level images.
segmentation Statistics of watershed segments are: Total area of
segments Number of segments normalized by total area of segments
Average area of segments Standard deviation of segment area
Smallest segment area Largest segment area
[0066] As noted above, analysis can also include one or more
additional techniques include, for example, Hough transform, mean
pixel intensity analysis, variance of pixel intensity analysis,
soft tissue analysis and the like. See, e.g., co-owned
International Application WO 02/30283.
[0067] Calibrated density typically refers to the measurement of
intensity values of features in images converted to its actual
material density or expressed as the density of a reference
material whose density is known. The reference material can be
metal, polymer, plastics, bone, cartilage, etc., and can be part of
the object being imaged or a calibration phantom placed in the
imaging field of view during image acquisition.
[0068] Extracted structures typically refer to simplified or
amplified representations of features derived from images. An
example would be binary images of trabecular patterns generated by
background subtraction and thresholding. Another example would be
binary images of cortical bone generated by applying an edge filter
and thresholding. The binary images can be superimposed on gray
level images to generate gray level patterns of structure of
interest.
[0069] Distance transform typically refers to an operation applied
on binary images where maps representing distances of each 0 pixel
to the nearest 1 pixel are generated. Distances can be calculated
by the Euclidian magnitude, city-block distance, La Place distance
or chessboard distance.
[0070] Distance transform of extracted structures typically refer
to distance transform operation applied to the binary images of
extracted structures, such as those discussed above with respect to
calibrated density.
[0071] Skeleton of extracted structures typically refer to a binary
image of 1 pixel wide patterns, representing the centerline of
extracted structures. It is generated by applying a skeletonization
or medial transform operation, by mathematical morphology or other
methods, on an image of extracted structures.
[0072] Skeleton segments typically are derived from skeleton of
extracted structures by performing pixel neighborhood analysis on
each skeleton pixel. This analysis classifies each skeleton pixel
as a node pixel or a skeleton segment pixel. A node pixel has more
than 2 pixels in its 8-neighborhood. A skeleton segment is a chain
of skeleton segment pixels continuously 8-connected. Two skeleton
segments are separated by at least one node pixel.
[0073] Watershed segmentation as it is commonly known to a person
of skill in the art, typically is applied to gray level images to
characterize gray level continuity of a structure of interest. The
statistics of dimensions of segments generated by the process are,
for example, those listed in Table 3 above. As will be appreciated
by those of skill in the art, however, other processes can be used
without departing from the scope of the invention.
[0074] Turning now to FIG. 3A, a cross-section of a cartilage
defect is shown 300. The cross-hatched zone 302 corresponds to an
area where there is cartilage loss. FIG. 3B is a top view of the
cartilage defect shown in FIG. 3A.
[0075] FIG. 3C illustrates the depth of a cartilage defect 310 in a
first cross-section dimension with a dashed line illustrating a
projected location of the original cartilage surface 312. By
comparing these two values a ratio of cartilage defect depth to
cartilage defect width can be calculated.
[0076] FIG. 3D illustrated the depth of the cartilage 320 along
with the width of the cartilage defect 322. These two values can be
compared to determine a ratio of cartilage depth to cartilage
defect width.
[0077] FIG. 3E shows the depth of the cartilage defect 310 along
with the depth of the cartilage 320. A dashed line is provided
illustrating a projected location for the original cartilage
surface 312. Similar to the measurements made above, ratios between
the various measurements can be calculated.
[0078] Turning now to FIG. 3F, an area of bone marrow edema is
shown on the femur 330 and the tibia 332. The shaded area of edema
can be measured on a T2-weighted MRI scan. Alternatively, the area
can be measured on one or more slices. These measurements can then
be extended along the entire joint using multiple slices or a 3D
acquisition. From these measurements volume can be determined or
derived.
[0079] FIG. 3G shows an area of subchondral sclerosis in the
acetabulum 340 and the femur 342. The sclerosis can be measured on,
for example, a T1 or T2-weighted MRI scan or on a CT scan. The area
can be measured on one or more slices. Thereafter the measurement
can be extended along the entire joint using multiple slices or a
3D acquisition. From these values a volume can be derived of the
subchondral sclerosis. For purposes of illustration, a single
sclerosis has been shown on each surface. However, a person of
skill in the art will appreciate that more than one sclerosis can
occur on a single joint surface.
[0080] FIG. 3H shows osteophytes on the femur 350 and the tibia
352. The osteophytes are shown as cross-hatched areas. Similar to
the sclerosis shown in FIG. 3G, the osteophytes can be measured on,
for example, a T1 or T2-weighted MRI scan or on a CT scan. The area
can be measured on one or more slices. Thereafter the measurement
can be extended along the entire joint using multiple slices or a
3D acquisition. From these values a volume can be derived of the
osteophytes. Additionally, a single osteophyte 354 or osteophyte
groups 356 can be included in any measurement. Persons of skill in
the art will appreciate that groups can be taken from a single
joint surface or from opposing joint surfaces, as shown, without
departing from the scope of the invention.
[0081] Turning now to FIG. 3I an area of subchondral cysts 360,
362, 364 is shown. Similar to the sclerosis shown in FIG. 3G, the
cysts can be measured on, for example, a T1 or T2-weighted MRI scan
or on a CT scan. The area can be measured on one or more slices.
Thereafter the measurement can be extended along the entire joint
using multiple slices or a 3D acquisition. From these values a
volume can be derived of the cysts. Additionally, single cysts 366
or groups of cysts 366' can be included in any measurement. Persons
of skill in the art will appreciate that groups can be taken from a
single joint surface, as shown, or from opposing joint surfaces
without departing from the scope of the invention.
[0082] FIG. 3J illustrates an area of torn meniscal tissue
(cross-hatched) 372, 374 as seen from the top 370 and in
cross-section 371. Again, similar to the sclerosis shown in FIG.
3G, the torn meniscal tissue can be measured on, for example, a T1
or T2-weighted MRI scan or on a CT scan. The area can be measured
on one or more slices. Thereafter the measurement can be extended
along the entire joint using multiple slices or a 3D acquisition.
From these values a volume can be derived of the tear. Ratios such
as surface or volume of torn to normal meniscal tissue can be
derived as well as ratios of surface of torn meniscus to surface of
opposing articulating surface.
[0083] As shown in FIG. 4A, the process of optionally locating a
ROI 100, extracting image data from the ROI 102, and deriving
quantitative and/or qualitative image data from the extracted image
data 120, can be repeated 122. Alternatively, or in addition, the
process of locating a ROI 100, can be repeated 124. A person of
skill in the art will appreciate that these steps can be repeated
one or more times in any appropriate sequence, as desired, to
obtain a sufficient amount of quantitative and/or qualitative data
on the ROI or to separately extract or evaluate parameters.
Further, the ROI used can be the same ROI as used in the first
process or a newly identified ROI in the image. Additionally, as
with FIG. 1A the steps of locating a region of interest 100,
obtaining image data 102, and deriving quantitative and/or
qualitative image data can be repeated one or more times, as
desired, 101, 103, 121, respectively. Although not depicted here,
as discussed above with respect to FIG. 1A, the additional step of
locating a part of the body for study 98 can be performed prior to
locating a region of interest 100 without departing from the
invention. Additionally that step can be repeated 99.
[0084] FIG. 4B illustrates the process shown in FIG. 4A with the
additional step enhancing image data 104. Additionally, the step of
enhancing image data 104 can be repeated one or more times 105, as
desired. The process of enhancing image data 104 can be repeated
126 one or more times as desired.
[0085] Turning now to FIG. 5A, a process is shown whereby a region
of interest is optionally located 100. Although not depicted here,
as discussed above with respect to FIG. 1A, the step of locating a
part of the body for study 98 can be performed prior to locating a
region of interest 100 without departing from the invention.
Additionally that step can be repeated 99. Once the region of
interest is located 100, and image data is extracted from the ROI
102, the extracted image data can then be converted to a 2D pattern
130, a 3D pattern 132 or a 4D pattern 133, for example including
velocity or time, to facilitate data analyses. Following conversion
to 2D 130, 3D 132 or 4D pattern 133 the images are evaluated for
patterns 140. Additionally images can be converted from 2D to 3D
131, or from 3D to 4D 131', if desired. Although not illustrated to
avoid obscuring the figure, persons of skill in the art will
appreciate that similar conversions can occur between 2D and 4D in
this process or any process illustrated in this invention.
[0086] As will be appreciated by those of skill in the art, the
conversion step is optional and the process can proceed directly
from extracting image data from the ROI 102 to evaluating the data
pattern 140 directly 134. Evaluating the data for patterns,
includes, for example, performing the measurements described in
Table 1, Table 2 or Table 3, above.
[0087] Additionally, the steps of locating the region of interest
100, obtaining image data 102, and evaluating patterns 141 can be
performed once or a plurality of times, 101, 103, 141, respectively
at any stage of the process. As will be appreciated by those of
skill in the art, the steps can be repeated. For example, following
an evaluation of patterns 140, additional image data can be
obtained 135, or another region of interest can be located 137.
These steps can be repeated as often as desired, in any combination
desirable to achieve the data analysis desired.
[0088] FIG. 5B illustrates an alternative process to that shown in
FIG. 5A which 5A THAT includes the step of enhancing image data 104
prior to converting an image or image data to a 2D 130, 3D 132, or
4D 133 pattern. The process of enhancing image data 104, can be
repeated 105 if desired. FIG. 5C illustrates an alternative
embodiment to the process shown in FIG. 5B. In this process, the
step of enhancing image data 104 occurs after converting an image
or image data to a 2D 130, 3D 132, or 4D 133 pattern. Again, the
process of enhancing image data 104, can be repeated 105 if
desired.
[0089] FIG. 5D illustrates an alternative process to that shown in
FIG. 5A. After locating a part of the body for study 98 and
imaging, the image is then converted to a 2D pattern 130, 3D
pattern 132 or 4D pattern 133. The region of interest 100 is
optionally located within the image after conversion to a 2D, 3D or
4D image and data is then extracted 102. Patterns are then
evaluated in the extracted image data 140. As with the process of
FIG. 5A, the conversion step is optional. Further, if desired,
images can be converted between 2D, 3D 131 and 4D 131' if
desired.
[0090] Similar to FIG. 5A, some or all the processes can be
repeated one or more times as desired. For example, locating a part
of the body for study 98, locating a region of interest 100,
obtaining image data 102, and evaluating patterns 140, can be
repeated one or more times if desired, 99, 101, 103, 141,
respectively. Again steps can be repeated. For example, following
an evaluation of patterns 140, additional image data can be
obtained 135, or another region of interest can be located 137
and/or another portion of the body can be located for study 139.
These steps can be repeated as often as desired, in any combination
desirable to achieve the data analysis desired.
[0091] FIG. 5E illustrates an alternative process to that shown in
FIG. 5D. In this process image data can be enhanced 104. The step
of enhancing image data can occur prior to conversion 143, prior to
locating a region of interest 145, prior to obtaining image data
102, or prior to evaluating patterns 149.
[0092] Similar to FIG. 5A, some or all the processes can be
repeated one or more times as desired, including the process of
enhancing image data 104, which is shown as 105.
[0093] The method also comprises obtaining an image of a bone or a
joint, optionally converting the image to a two-dimensional or
three-dimensional or four-dimensional pattern, and evaluating the
amount or the degree of normal, diseased or abnormal tissue or the
degree of degeneration in a region or a volume of interest using
one or more of the parameters specified in Table 1, Table 2 and/or
Table 3. By performing this method at an initial time T.sub.1,
information can be derived that is useful for diagnosing one or
more conditions or for staging, or determining, the severity of a
condition. This information can also be useful for determining the
prognosis of a patient, for example with osteoporosis or arthritis.
By performing this method at an initial time T.sub.1, and a later
time T.sub.2, the change, for example in a region or volume of
interest, can be determined which then facilitates the evaluation
of appropriate steps to take for treatment. Moreover, if the
subject is already receiving therapy or if therapy is initiated
after time T.sub.1, it is possible to monitor the efficacy of
treatment. By performing the method at subsequent times,
T.sub.2-T.sub.n. additional data ca be acquired that facilitate
predicting the progression of the disease as well as the efficacy
of any interventional steps that have been taken. As will be
appreciated by those of skill in the art, subsequent measurements
can be taken at regular time intervals or irregular time intervals,
or combinations thereof. For example, it can be desirable to
perform the analysis at T.sub.1 with an initial follow-up, T.sub.2,
measurement taken one month later. The pattern of one month
follow-up measurements could be performed for a year (12 one-month
intervals) with subsequent follow-ups performed at 6 month
intervals and then 12 month intervals. Alternatively, as an
example, three initial measurements could be at one month, followed
by a single six month follow up which is then followed again by one
or more one month follow-ups prior to commencing 12 month follow
ups. The combinations of regular and irregular intervals are
endless, and are not discussed further to avoid obscuring the
invention.
[0094] Moreover, one or more of the parameters listed in Tables 1,
2 and 3 can be measured. The measurements can be analyzed
separately or the data can be combined, for example using
statistical methods such as linear regression modeling or
correlation. Actual and predicted measurements can be compared and
correlated. See, also, Example 1.
[0095] The method for assessing the condition of a bone or joint in
a subject can be fully automated such that the measurements of one
or more of the parameters specified in Table 1, Table 2 or Table 3
are done automatically without intervention. The automatic
assessment then can include the steps of diagnosis, staging,
prognostication or monitoring the disease or diseases, or to
monitor therapy. As will be appreciated by those of skill in the
art, the fully automated measurement is, for example, possible with
image processing techniques such as segmentation and registration.
This process can include, for example, seed growing, thresholding,
atlas and model based segmentation methods, live wire approaches,
active and/or deformable contour approaches, contour tracking,
texture based segmentation methods, rigid and non-rigid surface or
volume registration, for example based on mutual information or
other similarity measures. One skilled in the art will readily
recognize other techniques and methods for fully automated
assessment of the parameters and measurements specified in Table 1,
Table 2 and Table 3.
[0096] Alternatively, the method of assessing the condition of a
bone or joint in a subject can be semi-automated such that the
measurements of one or more of the parameters, such as those
specified in Table 1, are performed semi-automatically, i.e., with
intervention. The semi-automatic assessment then allows for human
interaction and, for example, quality control, and utilizing the
measurement of said parameter(s) to diagnose, stage, prognosticate
or monitor a disease or to monitor a therapy. The semi-automated
measurement is, for example, possible with image processing
techniques such as segmentation and registration. This can include
seed growing, thresholding, atlas and model based segmentation
methods, live wire approaches, active and/or deformable contour
approaches, contour tracking, texture based segmentation methods,
rigid and non-rigid surface or volume registration, for example
base on mutual information or other similarity measures. One
skilled in the art will readily recognize other techniques and
methods for semi-automated assessment of the parameters specified
in Table 1, Table 2 or Table 3.
[0097] Turning now to FIG. 6A, a process is shown whereby the user
locates a ROI 100, extracts image data from the ROI 102, and then
derives quantitative and/or qualitative image data from the
extracted image data 120, as shown above with respect to FIG. 1.
Following the step of deriving quantitative and/or qualitative
image data, a candidate agent is administered to the patient 150.
The candidate agent can be any agent the effects of which are to be
studied. Agents can include any substance administered or ingested
by a subject, for example, molecules, pharmaceuticals,
biopharmaceuticals, agropharmaceuticals, or combinations thereof,
including cocktails, that are thought to affect the quantitative
and/or qualitative parameters that can be measured in a region of
interest. These agents are not limited to those intended to treat
disease that affects the musculoskeletal system but this invention
is intended to embrace any and all agents regardless of the
intended treatment site. Thus, appropriate agents are any agents
whereby an effect can be detected via imaging. The steps of
locating a region of interest 100, obtaining image data 102,
obtaining quantitative and/or qualitative data from image data 120,
and administering a candidate agent 150, can be repeated one or
more times as desired, 101, 103, 121, 151, respectively.
[0098] FIG. 6B shows the additional step of enhancing image data
104, which can also be optionally repeated 105 as often as
desired.
[0099] As shown in FIG. 6C these steps can be repeated one or more
times 152 to determine the effect of the candidate agent. As will
be appreciated by those of skill in the art, the step of repeating
can occur at the stage of locating a region of interest 152 as
shown in FIG. 6B or it can occur at the stage obtaining image data
153 or obtaining quantitative and/or qualitative data from image
data 154 as shown in FIG. 6D.
[0100] FIG. 6E shows the additional step of enhancing image data
104, which can optionally be repeated 105, as desired.
[0101] As previously described, some or all the processes shown in
FIGS. 6A-E can be repeated one or more times as desired. For
example, locating a region of interest 100, obtaining image data
102, enhancing image data 104, obtaining quantitative and/or
qualitative data 120, evaluating patterns 140, and administering
candidate agent 150 can be repeated one or more times if desired,
101, 103, 105, 121, 141, 151 respectively.
[0102] In the scenario described in relation to FIG. 6, an image is
taken prior to administering the candidate agent. However, as will
be appreciated by those of skill in the art, it is not always
possible to have an image prior to administering the candidate
agent. In those situations, progress is determined over time by
evaluating the change in parameters from extracted image to
extracted image.
[0103] Turning now to FIG. 7A, the process is shown whereby the
candidate agent is administered first 150. Thereafter a region of
interest is located in an image taken 100 and image data is
extracted 102. Once the image data is extracted, quantitative
and/or qualitative data is extracted from the image data 120. In
this scenario, because the candidate agent is administered first,
the derived quantitative and/or qualitative data derived is
compared to a database 160 or a subset of the database, which
database that, includes data for subjects having similar tracked
parameters. As shown in FIG. 7B following the step of obtaining
image data, the image data can be enhanced 104. This process can
optionally be repeated 105, as desired.
[0104] Alternatively, as shown in FIG. 7C the derived quantitative
and/or qualitative information can be compared to an image taken at
T1 162, or any other time, if such image is available. As shown in
FIG. 7D the step of enhancing image data 104 can follow the step of
obtaining image data 102. Again, the process can be repeated 105,
as desired.
[0105] As previously described, some or all the processes
illustrated in FIGS. 7A-D can be repeated one or more times as
desired. For example, locating a region of interest 100, obtaining
image data 102, enhancing image data 104, obtaining quantitative
and/or qualitative data 120, administering candidate agent 150,
comparing quantitative and/or qualitative information to a database
160, comparing quantitative and/or qualitative information to an
image taken at a prior time, such as T1, 162, monitoring therapy
170, monitoring disease progress 172, predicting disease course 174
can be repeated one or more times if desired, 101, 103, 105, 121,
151, 161, 163, 171, 173, 175 respectively. Each of these steps can
be repeated in one or more loops as shown in FIG. 7B, 176, 177,
178, 179, 180, as desired or appropriate to enhance data
collection.
[0106] Turning now to FIG. 8A, following the step of extracting
image data from the ROI 102, the image can be transmitted 180.
Transmission can be to another computer in the network or via the
World Wide Web to another network. Following the step of
transmitting the image 180, the image is converted to a pattern of
normal and diseased tissue 190. Normal tissue includes the
undamaged tissue located in the body part selected for study.
Diseased tissue includes damaged tissue located in the body part
selected for study. Diseased tissue can also include, or refer to,
a lack of normal tissue in the body part selected for study. For
example, damaged or missing cartilage would be considered diseased
tissue. Once the image is converted, it is analyzed 200. FIG. 8B
illustrates the process shown in FIG. 8A with the additional step
of enhancing image data 104. As will be appreciated by those of
skill in the art, this process can be repeated 105 as desired.
[0107] As shown in FIG. 8C, the step of transmitting the image 180
illustrated in FIG. 8A is optional and need not be practiced under
the invention. As will be appreciated by those of skill in the art,
the image can also be analyzed prior to converting the image to a
pattern of normal and diseased. FIG. 8D illustrates the process
shown in FIG. 8C with the additional step of enhancing image data
104 that is optionally repeated 105, as desired.
[0108] As previously described, some or all the processes in FIGS.
8A-D can be repeated one or more times as desired. For example,
locating a region of interest 100, obtaining image data 102,
enhancing image data 104, transmitting an image 180, converting the
image to a pattern of normal and diseased 190, analyzing the
converted image 200, can be repeated one or more times if desired,
101, 103, 105, 181, 191, 201 respectively.
[0109] FIG. 9 shows two devices 900, 920 that are connected. Either
the first or second device can develop a degeneration pattern from
an image of a region of interest 905. Similarly, either device can
house a database for generating additional patterns or measurements
915. The first and second devices can communicate with each other
in the process of analyzing an image, developing a degeneration
pattern from a region of interest in the image, and creating a
dataset of patterns or measurements or comparing the degeneration
pattern to a database of patterns or measurements. However, all
processes can be performed on one or more devices, as desired or
necessary.
[0110] In this method the electronically generated, or digitized
image or portions of the image can be electronically transferred
from a transferring device to a receiving device located distant
from the transferring device; receiving the transferred image at
the distant location; converting the transferred image to a pattern
of normal or diseased or abnormal tissue using one or more of the
parameters specified in Table 1, Table 2 or Table 3; and optionally
transmitting the pattern to a site for analysis. As will be
appreciated by those of skill in the art, the transferring device
and receiving device can be located within the same room or the
same building. The devices can be on a peer-to-peer network, or an
intranet. Alternatively, the devices can be separated by large
distances and the information can be transferred by any suitable
means of data transfer, including the World Wide Web and ftp
protocols.
[0111] Alternatively, the method can comprise electronically
transferring an electronically-generated image or portions of an
image of a bone or a joint from a transferring device to a
receiving device located distant from the transferring device;
receiving the transferred image at the distant location; converting
the transferred image to a degeneration pattern or a pattern of
normal or diseased or abnormal tissue using one or more of the
parameters specified in Table 1, Table 2 or Table 3; and optionally
transmitting the degeneration pattern or the pattern of normal or
diseased or abnormal tissue to a site for analysis.
[0112] Thus, the invention described herein includes methods and
systems for prognosis of musculoskeletal disease, for example
prognosis of fracture risk and the like. (See, also, Example 1).
FIG. 10 is a schematic depiction of an image of a femur showing
various ROIs that were analyzed to predict fracture risk based on
assessment of one or more parameters shown in Tables 1, 2 and
3.
[0113] In order to make more accurate prognoses, it may be
desirable in certain instances to compare data obtained from a
subject to a reference database. For example, when predicting
fracture risk, it may be useful to compile data of actual (known)
fracture load in a variety of samples and store the results based
on clinical risk factors such as age, sex and weight (or other
characteristics) of the subject from which the sample is obtained.
The images of these samples are analyzed to obtain parameters shown
in Tables 1, 2 and 3. A fracture risk model correlated with
fracture load may be developed using univariate, bivariate and/or
multivariate statistical analysis of these parameters and is stored
in this database. A fracture risk model may include information
that is used to estimate fracture risk from parameters shown in
Tables 1, 2 and 3. An example of a fracture risk model is the
coefficients of a multivariate linear model derived from
multivariate linear regression of these parameters (Tables 1, 2, 3,
age, sex, weight, etc.) with fracture load. A person skilled in the
art will appreciate that fracture risk models can be derived using
other methods such as artificial neural networks and be represented
by other forms such as the coefficients of artificial neural
networks. Patient fracture risk can then be determined from
measurements obtain from bone images by referencing to this
database.
[0114] Methods of determining actual fracture load are known to
those in the field. FIG. 11 is a schematic depiction of
biomechanical testing of an intact femur. As shown, cross-sectional
images may be taken throughout testing to determine at what load
force a fracture occurs. FIG. 12B is a reproduction of an x-ray
image depicting an example of an induced fracture in a fresh
cadaveric femur.
[0115] The analysis techniques described herein can then be applied
to a subject and the risk of fracture (or other disease) predicted
using one or more of the parameters described herein. As shown in
FIGS. 13 to 16, the prognostication methods described herein are as
(or more) accurate than known techniques in predicting fracture
risk. FIG. 13 is a graph depicting linear regression analysis of
DXA bone mineral density correlated to fracture load. Correlations
of individual parameters to fracture load are comparable to DXA
(FIGS. 14 and 15). However, when multiple structural parameters are
combined, the prediction of load at which fracture will occur is
more accurate. (FIG. 16). Thus, the analyses of images as described
herein can be used to accurately predict musculoskeletal disease
such as fracture risk.
[0116] Another aspect of the invention is a kit for aiding in
assessing the condition of a bone or a joint of a subject, which
kit comprises a software program, which when installed and executed
on a computer reads a degeneration pattern or a pattern of normal
or diseased or abnormal tissue derived using one or more of the
parameters specified in Table 1, Table 2 or Table 3 presented in a
standard graphics format and produces a computer readout. The kit
can further include a database of measurements for use in
calibrating or diagnosing the subject. One or more databases can be
provided to enable the user to compare the results achieved for a
specific subject against, for example, a wide variety of subjects,
or a small subset of subjects having characteristics similar to the
subject being studied.
[0117] A system is provided that includes (a) a device for
electronically transferring a degeneration pattern or a pattern of
normal, diseased or abnormal tissue for the bone or the joint to a
receiving device located distant from the transferring device; (b)
a device for receiving said pattern at the remote location; (c) a
database accessible at the remote location for generating
additional patterns or measurements for the bone or the joint of
the human wherein the database includes a collection of subject
patterns or data, for example of human bones or joints, which
patterns or data are organized and can be accessed by reference to
characteristics such as type of joint, gender, age, height, weight,
bone size, type of movement, and distance of movement; (d)
optionally a device for transmitting the correlated pattern back to
the source of the degeneration pattern or pattern of normal,
diseased or abnormal tissue.
[0118] Thus, the methods and systems described herein make use of
collections of data sets of measurement values, for example
measurements of bone structure and/or bone mineral density from
images (e.g., x-ray images). Records can be formulated in
spreadsheet-like format, for example including data attributes such
as date of image (x-ray), patient age, sex, weight, current
medications, geographic location, etc. The database formulations
can further comprise the calculation of derived or calculated data
points from one or more acquired data points, typically using the
parameters listed in Tables 1, 2 and 3 or combinations thereof. A
variety of derived data points can be useful in providing
information about individuals or groups during subsequent database
manipulation, and are therefore typically included during database
formulation. Derived data points include, but are not limited to
the following: (1) maximum value, e.g. bone mineral density,
determined for a selected region of bone or joint or in multiple
samples from the same or different subjects; (2) minimum value,
e.g. bone mineral density, determined for a selected region of bone
or joint or in multiple samples from the same or different
subjects; (3) mean value, e.g. bone mineral density, determined for
a selected region of bone or joint or in multiple samples from the
same or different subjects; (4) the number of measurements that are
abnormally high or low, determined by comparing a given measurement
data point with a selected value; and the like. Other derived data
points include, but are not limited to the following: (1) maximum
value of a selected bone structure parameter, determined for a
selected region of bone or in multiple samples from the same or
different subjects; (2) minimum value of a selected bone structure
parameter, determined for a selected region of bone or in multiple
samples from the same or different subjects; (3) mean value of a
selected bone structure parameter, determined for a selected region
of bone or in multiple samples from the same or different subjects;
(4) the number of bone structure measurements that are abnormally
high or low, determined by comparing a given measurement data point
with a selected value; and the like. Other derived data points will
be apparent to persons of ordinary skill in the art in light of the
teachings of the present specification. The amount of available
data and data derived from (or arrived at through analysis of) the
original data provides an unprecedented amount of information that
is very relevant to management of bone-related diseases such as
osteoporosis. For example, by examining subjects over time, the
efficacy of medications can be assessed.
[0119] Measurements and derived data points are collected and
calculated, respectively, and can be associated with one or more
data attributes to form a database. The amount of available data
and data derived from (or arrived at through analysis of) the
original data provide provides an unprecedented amount of
information that is very relevant to management of
musculoskeletal-related diseases such as osteoporosis or arthritis.
For example, by examining subjects over time, the efficacy of
medications can be assessed.
[0120] Data attributes can be automatically input with the
electronic image and can include, for example, chronological
information (e.g., DATE and TIME). Other such attributes can
include, but are not limited to, the type of imager used, scanning
information, digitizing information and the like. Alternatively,
data attributes can be input by the subject and/or operator, for
example subject identifiers, i.e. characteristics associated with a
particular subject. These identifiers include but are not limited
to the following: (1) a subject code (e.g., a numeric or
alpha-numeric sequence); (2) demographic information such as race,
gender and age; (3) physical characteristics such as weight, height
and body mass index (BMI); (4) selected aspects of the subject's
medical history (e.g., disease states or conditions, etc.); and (5)
disease-associated characteristics such as the type of bone
disorder, if any; the type of medication used by the subject. In
the practice of the present invention, each data point would
typically be identified with the particular subject, as well as the
demographic, etc. characteristic of that subject.
[0121] Other data attributes will be apparent to persons of
ordinary skill in the art in light of the teachings of the present
specification. (See, also, WO 02/30283, incorporated by reference
in its entirety herein).
[0122] Thus, data (e.g., bone structural information or bone
mineral density information or articular information) is obtained
from normal control subjects using the methods described herein.
These databases are typically referred to as "reference databases"
and can be used to aid analysis of any given subject's image, for
example, by comparing the information obtained from the subject to
the reference database. Generally, the information obtained from
the normal control subjects will be averaged or otherwise
statistically manipulated to provide a range of "normal"
measurements. Suitable statistical manipulations and/or evaluations
will be apparent to those of skill in the art in view of the
teachings herein. The comparison of the subject's information to
the reference database can be used to determine if the subject's
bone information falls outside the normal range found in the
reference database or is statistically significantly different from
a normal control.
[0123] Data obtained from images, as described above, can be
manipulated, for example, using a variety of statistical analyses
to produce useful information. Databases can be created or
generated from the data collected for an individual, or for a group
of individuals, over a defined period of time (e.g., days, months
or years), from derived data, and from data attributes.
[0124] For example, data can be aggregated, sorted, selected,
sifted, clustered and segregated by means of the attributes
associated with the data points. A number of data mining software
exist which can be used to perform the desired manipulations.
[0125] Relationships in various data can be directly queried and/or
the data analyzed by statistical methods to evaluate the
information obtained from manipulating the database.
[0126] For example, a distribution curve can be established for a
selected data set, and the mean, median and mode calculated
therefor. Further, data spread characteristics, e.g., variability,
quartiles, and standard deviations can be calculated.
[0127] The nature of the relationship between any variables of
interest can be examined by calculating correlation coefficients.
Useful methods for doing so include, but are not limited to:
Pearson Product Moment Correlation and Spearman Rank Correlation.
Analysis of variance permits testing of differences among sample
groups to determine whether a selected variable has a discernible
effect on the parameter being measured.
[0128] Non-parametric tests can be used as a means of testing
whether variations between empirical data and experimental
expectancies are attributable to chance or to the variable or
variables being examined. These include the Chi Square test, the
Chi Square Goodness of Fit, the 2.times.2 Contingency Table, the
Sign Test and the Phi Correlation Coefficient. Other tests include
z-scores, T-scores or lifetime risk for arthritis, cartilage loss
or osteoporotic fracture.
[0129] There are numerous tools and analyses available in standard
data mining software that can be applied to the analyses of the
databases that can be created according to this invention. Such
tools and analysis include, but are not limited to, cluster
analysis, factor analysis, decision trees, neural networks, rule
induction, data driven modeling, and data visualization. Some of
the more complex methods of data mining techniques are used to
discover relationships that are more empirical and data-driven, as
opposed to theory driven, relationships.
[0130] Statistical significance can be readily determined by those
of skill in the art. The use of reference databases in the analysis
of images facilitates that diagnosis, treatment and monitoring of
bone conditions such as osteoporosis.
[0131] For a general discussion of statistical methods applied to
data analysis, see Applied Statistics for Science and Industry, by
A. Romano, 1977, Allyn and Bacon, publisher.
[0132] The data is preferably stored and manipulated using one or
more computer programs or computer systems. These systems will
typically have data storage capability (e.g., disk drives, tape
storage, optical disks, etc.). Further, the computer systems can be
networked or can be stand-alone systems. If networked, the computer
system would be able to transfer data to any device connected to
the networked computer system for example a medical doctor or
medical care facility using standard e-mail software, a central
database using database query and update software (e.g., a data
warehouse of data points, derived data, and data attributes
obtained from a large number of subjects). Alternatively, a user
could access from a doctor's office or medical facility, using any
computer system with Internet access, to review historical data
that can be useful for determining treatment.
[0133] If the networked computer system includes a World Wide Web
application, the application includes the executable code required
to generate database language statements, for example, SQL
statements. Such executables typically include embedded SQL
statements. The application further includes a configuration file
that contains pointers and addresses to the various software
entities that are located on the database server in addition to the
different external and internal databases that are accessed in
response to a user request. The configuration file also directs
requests for database server resources to the appropriate hardware,
as can be necessary if the database server is distributed over two
or more different computers.
[0134] As a person of skill in the art will appreciate, one or more
of the parameters specified in Table 1, Table and Table 3 can be
used at an initial time point T.sub.1 to assess the severity of a
bone disease such as osteoporosis or arthritis. The patient can
then serve as their own control at a later time point T.sub.2, when
a subsequent measurement using one or more of the same parameters
used at T.sub.1 is repeated.
[0135] A variety of data comparisons can be made that will
facilitate drug discovery, efficacy, dosing, and comparisons. For
example, one or more of the parameters specified in Table 1, Table
2 and Table 3 may be used to identify lead compounds during drug
discovery. For example, different compounds can be tested in animal
studies and the lead compounds with regard to highest therapeutic
efficacy and lowest toxicity, e.g. to the bone or the cartilage,
can be identified. Similar studies can be performed in human
subjects, e. g. FDA phase I, II or III trials. Alternatively, or in
addition, one or more of the parameters specified in Table 1, Table
2 and Table 3 can be used to establish optimal dosing of a new
compound. It will be appreciated also that one or more of the
parameters specified in Table 1, Table 2 and Table 3 can be used to
compare a new drug against one or more established drugs or a
placebo. The patient can then serve as their own control at a later
time point T.sub.2.
EXAMPLES
Example 1
[0136] Correlation of Macro-Anatomical and Structural Parameters to
Fracture Load
[0137] Using 15 fresh cadaveric femurs, the following analyses were
performed to determine the correlation of macro-anatomical and
structural parameters to fracture load.
[0138] Standardization of Hip radiographs: Density and
magnification calibration on the x-ray radiographs was achieved
using a calibration phantom. The reference orientation of the hip
x-rays was the average orientation of the femoral shaft.
[0139] Automatic Placement of Regions of Interest. An algorithm was
developed and used to consistently and accurately place 7 regions
of interest based on the geometric and position of proximal femur.
FIG. 10. In brief, the algorithm involved the detection of femoral
boundaries, estimation of shaft and neck axes, and construction of
ROI based on axes and boundary intercept points. This approach
ensured that the size and shape of ROIs placed conformed to the
scale and shape of the femur, and thus were consistent relative to
anatomic features on the femur.
[0140] Automatic Segmentation of the proximal femur: A global gray
level thresholding using bi-modal histogram segmentation
algorithm(s) was performed on the hip images and a binary image of
the proximal femur was generated. Edge-detection analysis was also
performed on the hip x-rays, including edge detection of the
outline of the proximal femur that involved breaking edges detected
into segments and characterizing the orientation of each segment.
Each edge segment was then referenced to a map of expected proximal
femur edge orientation and to a map of the probability of edge
location. Edge segments that did not conform to the expected
orientation or which were in low probability regions were removed.
Morphology operations were applied to the edge image(s) to connect
any discontinuities. The edge image formed an enclosed boundary of
the proximal femur. The region within the boundary was then
combined with the binary image from global thresholding to form the
final mask of the proximal femur.
[0141] Automatic Segmentation and Measurement of the Femoral
Cortex: Within a region of interest (ROI), edge detection was
applied. Morphology operations were applied to connect edge
discontinuities. Segments were formed within enclosed edges. The
area and the major axis length of each segment were then measured.
The regions were also superimposed on the original gray level image
and average gray level within each region was measured. The cortex
was identified as those segments connected to the boundary of the
proximal femur mask with the greatest area, longest major axis
length and a mean gray level about the average gray level of all
enclosed segments within the proximal femur mask.
[0142] The segment identified as cortex was then skeletonized. The
orientation of the cortex skeleton was verified to conform to the
orientation map of the proximal femur edge. Euclidean distance
transform was applied to the binary image of the segment. The
values of distance transform value along the skeleton were sampled
and their average, standard deviation, minimum, maximum and mod
determined.
[0143] Watershed Segmentation for Characterizing Trabecular
Structure: Marrow spacing was characterized by determining
watershed segmentation of gray level trabecular structures on the
hip images; essentially as described in Russ "The Image Processing
Handbook," 3.sup.rd. ed. pp. 494-501. This analysis takes the gray
level contrast between the marrow spacing and adjacent trabecular
structures into account. The segments of marrow spacing generated
using watershed segmentation were measured for the area,
eccentricity, orientation, and the average gray level on the x-ray
image within the segment. Mean, standard deviation, minimum,
maximum and mod. were determined for each segment. In addition,
various structural and/or macro-anatomical parameters were assessed
for several ROIs (FIG. 10).
[0144] Measurement of Femoral Neck BMD: DXA analysis of bone
mineral density was performed in the femoral neck region of the
femurs.
[0145] Biomechanical Testing of Intact Femur Each cadaveric femur
sample (n=15) was tested for fracture load as follows. First, the
femur was placed at a 15.degree. angle of tilt and an 8.degree.
external rotation in an Instron 1331 Instrument (Instron, Inc.) and
a load vector at the femoral head simulating single-leg stance was
generated, essentially as described in Cheal et al. (1992) J.
Orthop. Res. 10(3):405-422. Second, varus/valgus and torsional
resistive movements simulating passive knee ligaments restraints
were applied. Next, forces and movement at failure were measured
using a six-degree of freedom load cell. Subsequently, a single
ramp, axial compressive load was applied to the femoral head of
each sample at 100 mm/s until fracture. (FIG. 12). Fracture load
and resultant equilibrium forces and moments at the distal end of
the femur were measured continuously. FIG. 11 shows various results
of biomechanical testing.
[0146] The correlation between (1) DXA femoral next BMD and facture
load; (2) bone structure and fracture load; and (3)
macro-anatomical analyses and fracture load was determined and
shown in FIG. 13-15, respectively.
[0147] Multivariate linear regression analysis was also performed,
combining several structural and macro-anatomical parameters,
including local maximum marrow spacing (r=0.6 linearized); standard
deviation of cortical thickness of ROI3 (r=0.57); maximum cortical
thickness of ROI5 (r=0.56); and mean node-free end length for ROI3
(r=0.50). Results are shown in FIG. 16 and demonstrate that, using
analyses, described herein there is a good correlation between
predicted fracture load and actual fracture load (r=0.81,
p<0.001). The mean fracture load was 5.4 kiloNewton with a
standard deviation of 2.3 kiloNewton. These statistics and the
coefficients of multivariate linear regression were stored as data
of the fracture load reference database.
Example 2
[0148] Correlation of 2D and 3D Measurements
[0149] To demonstrate that methods using 2D x-ray technology to
quantitatively assess trabecular architecture is as effective as
3D.mu. CT, which serves as a gold standard for such measurements,
the following experiments were performed. Bone cores (n=48) were
harvested from cadaveric proximal femora. Specimen radiographs were
obtained and 2D structural parameters were measured on the
radiographs. Cores were then subjected to 3D .mu.CT and
biomechanical testing. The .mu.CT images were analyzed to obtained
3D micro-structural measurements. Digitized 2D x-ray images of
these cores were also analyzed as described herein to obtain
comparative micro-structural measurements.
[0150] Results showed very good correlation among the numerous 2D
parameters and 3D .mu.CT measurements, including for example
correlation between 2D Trabecular Perimeter/Trabecular Area
(Tb.P/Tb.A) with 3D Bone Surface/Bone Volume (r=0.92, p<0.001),
and 2D Trabecular Separation (Tb.Sp) with 3D Trabecular Separation
(r=0.88, p<0.001). The 2D Tb. P/Tb.A and 2D Tb.Sp also function
correlate very well as predictive parameters for the mechanical
loads required to fracture the cores, with r=-0.84 (p<0.001) and
r=-0.83 (p<0.001), respectively, when logarithmic and
exponential transformations were used in the regression.
[0151] These results demonstrate that 2D micro-structural
measurements of trabecular bone from digitized radiographs are
highly correlated with 3D measurements obtained from .mu.-CT
images. Therefore, the mechanical characteristics of trabecular
bone microstructure from digitized radiographic images can be
accurately determined from 2D images.
Example 3
[0152] Prediction of Fracture Risk using Fracture Load Reference
Database
[0153] A hip x-ray of cadaver pelvis was exposed using standard
clinical procedure and equipment. The radiograph film was developed
and digitized. The image was then analyzed to obtain
micro-structure, and macro-anatomical parameters. The local maximum
spacing, standard deviation of cortical thickness of ROI3, maximum
cortical thickness of ROI5, and mean node-free end length for ROI3
were used to predict load required to fracture the cadaver hip
using the coefficients of multivariate linear regression stored in
the fracture load reference database. The predicted fracture load
was 7.5 kiloNewton. This fracture load is 0.98 standard deviation
above the average of the fracture load reference database (or
z-score=0.98). This result may suggest that the subject had a
relatively low risk of sustaining a hip fracture as compared to the
population of the reference database.
[0154] The foregoing description of embodiments of the present
invention has been provided for the purposes of illustration and
description. It is not intended to be exhaustive or to limit the
invention to the precise forms disclosed. Many modifications and
variations will be apparent to the practitioner skilled in the art.
The embodiments were chosen and described in order to best explain
the principles of the invention and its practical application,
thereby enabling others skilled in the art to understand the
invention and the various embodiments and with various
modifications that are suited to the particular use contemplated.
It is intended that the scope of the invention be defined by the
following claims and its equivalence.
* * * * *