U.S. patent application number 16/128402 was filed with the patent office on 2019-01-24 for cloud-based medical image processing system with tracking capability.
The applicant listed for this patent is TeraRecon, Inc.. Invention is credited to Chunguang Jia, Gang LI, Robert James TAYLOR, Junnan WU, Tiecheng ZHAO.
Application Number | 20190027244 16/128402 |
Document ID | / |
Family ID | 48945574 |
Filed Date | 2019-01-24 |
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United States Patent
Application |
20190027244 |
Kind Code |
A1 |
WU; Junnan ; et al. |
January 24, 2019 |
CLOUD-BASED MEDICAL IMAGE PROCESSING SYSTEM WITH TRACKING
CAPABILITY
Abstract
A cloud server receives a request for accessing medical image
data from a client device, where the cloud server provides image
processing services to users in image processing steps, resulting
in image views. User privileges of a user are determined for
accessing the medical image data. In response to receiving a
command having a selection of an image view from the client device,
the cloud server provides the medical image data based on the
selected one or more image views. The user interactions of the user
with the medical image data via the selected image views are
tracked, including tracking how long in time the user has spent on
a particular image view. The tracked user interactions are stored
in a persistent storage, and an analysis is performed on the
tracked user interactions stored in the persistent storage to
determine an overall usage trend of the image views.
Inventors: |
WU; Junnan; (Acton, MA)
; TAYLOR; Robert James; (San Francisco, CA) ; LI;
Gang; (Acton, MA) ; ZHAO; Tiecheng; (Concord,
MA) ; Jia; Chunguang; (Acton, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
TeraRecon, Inc. |
Foster City |
CA |
US |
|
|
Family ID: |
48945574 |
Appl. No.: |
16/128402 |
Filed: |
September 11, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15222282 |
Jul 28, 2016 |
10078727 |
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16128402 |
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14174115 |
Feb 6, 2014 |
9430828 |
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15222282 |
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13396548 |
Feb 14, 2012 |
8682049 |
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14174115 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/103 20130101;
G06Q 50/22 20130101; G16H 30/20 20180101; G06T 7/0012 20130101;
G06F 19/00 20130101; G06F 19/321 20130101 |
International
Class: |
G16H 30/20 20060101
G16H030/20; G06F 19/00 20060101 G06F019/00; G06Q 10/10 20060101
G06Q010/10; G06Q 50/22 20060101 G06Q050/22; G06T 7/00 20060101
G06T007/00 |
Claims
1-22. (canceled)
23. An image processing system, comprising: an image capturing
device that captures medical images; a first medical data storage
system that stores medical data, which includes the captured
medical images and patient information; a second medical data
storage system that stores an anonymized version of the medical
data in which the patient information has been removed from the
medical data stored by the first medical data storage system; a
gateway manager, the gateway manager including: an anonymizer, the
anonymizer being configured to anonymize the medical data stored by
the first medical data storage system to produce the anonymized
version of the medical data before the medical data is transmitted
from the first image storage system to the second medical data
storage system; and a data mining system that performs data mining
on aggregated medical data that is stored on the second medical
data storage system, the aggregated medical data including the
anonymized version of the medical data and including additional
medical data from sources other than the first medical data storage
system.
24. An image processing system as in claim 23, wherein the stored
aggregated medical data includes quantitative image data
analysis.
25. An image processing system as in claim 23, wherein the data
mining system retrieves, calculates and correlates data
automatically.
26. An image processing system as in claim 23, wherein the data
mining system retrieves, calculates and correlates data
automatically.
27. An image processing system as in claim 23, additionally
comprising: an image preprocessor that preprocesses medical images
within the anonymized version of the medical data before the
anonymized version of the medical data is stored within the second
medical data storage system.
28. An image processing system as in claim 27, wherein the
preprocessing of the medical images includes at least one of the
following: bone removal; centerline extraction; sphere finding;
registration; parametric map calculation; reformatting;
time-density analysis segmentation of structures; auto-3D
operations.
29. An image processing system as in claim 27, wherein the
preprocessing of the medical images includes: generation of
workflow information to be used by a workflow management
system.
30. An image processing system as in claim 23, additionally
comprising: a tracking system that is configured to monitor and
track user activities with respect to the anonymized version of the
medical data stored on the second medical data storage system.
31. An image processing system as in claim 23, wherein the stored
aggregated medical data pertains to one of the following: clinical
trials; clinical research; trend identification; prediction of
disease progress; diagnosis; artificial intelligence.
32. An image processing system, comprising: an image capturing
device that captures medical images; a first medical data storage
system that stores medical data, which includes the captured
medical images and patient information; a second medical data
storage system that stores an anonymized version of the medical
data in which the patient information has been removed from the
medical data stored by the first medical data storage system; a
gateway manager, the gateway manager including: an anonymizer, the
anonymizer being configured to anonymize the medical data stored by
the first medical data storage system to produce the anonymized
version of the medical data before the medical data is transmitted
from the first image storage system to the second medical data
storage system; and, an image preprocessor that preprocesses
medical images within the anonymized version of the medical data
before the anonymized version of the medical data is stored within
the second medical data storage system.
33. An image processing system as in claim 32, wherein the
preprocessing of the medical images includes: generation of
workflow information to be used by a workflow management
system.
33. An image processing system as in claim 32, additionally
comprising: a data mining system that performs data mining on
aggregated medical data that is stored on the second medical data
storage system, the aggregated medical data including the
anonymized version of the medical data and including additional
medical data from sources other than the first medical data storage
system, wherein the stored aggregated medical data pertains to one
of the following: clinical trials; clinical research; trend
identification; prediction of disease progress; diagnosis;
artificial intelligence.
34. An image processing system as in claim 32, additionally
comprising: a data mining system that performs data mining on
aggregated medical data that is stored on the second medical data
storage system, the aggregated medical data including the
anonymized version of the medical data and including additional
medical data from sources other than the first medical data storage
system, wherein the stored aggregated medical data includes
quantitative image data analysis.
35. An image processing system as in claim 32, additionally
comprising: a data mining system that performs data mining on
aggregated medical data that is stored on the second medical data
storage system, the aggregated medical data including the
anonymized version of the medical data and including additional
medical data from sources other than the first medical data storage
system, wherein the data mining system retrieves, calculates and
correlates data automatically.
36. An image processing system as in claim 32, additionally
comprising: a tracking system that is configured to monitor and
track user activities with respect to the anonymized version of the
medical data stored on the second medical data storage system.
37. An image processing system as in claim 32, wherein the
preprocessing of the medical images includes at least one of the
following: bone removal; centerline extraction; sphere finding;
registration; parametric map calculation; reformatting;
time-density analysis segmentation of structures; auto-3D
operations.
38. An image processing system, comprising: an image capturing
device that captures medical images; a first medical data storage
system that stores medical data, which includes the captured
medical images and patient information; a second medical data
storage system that stores an anonymized version of the medical
data in which the patient information has been removed from the
medical data stored by the first medical data storage system; a
gateway manager, the gateway manager including: an anonymizer, the
anonymizer being configured to anonymize the medical data stored by
the first medical data storage system to produce the anonymized
version of the medical data before the medical data is transmitted
from the first image storage system to the second medical data
storage system; and a tracking system that is configured to monitor
and track user activities with respect to the anonymized version of
the medical data stored on the second medical data storage system.
Description
RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
patent application Ser. No. 13/396,548, filed Feb. 14, 2012, which
is incorporated by reference herein in its entirety.
FIELD OF THE INVENTION
[0002] Embodiments of the present invention relate generally to
image processing systems. More particularly, embodiments of the
invention relate to cloud-based medical image processing systems
with tracking capability.
BACKGROUND
[0003] A computerized axial tomography scan (commonly known as a
CAT scan or a CT scan) is an x-ray procedure, which combines many
x-ray images with the aid of a computer to generate cross-sectional
views of the internal organs and structures of the body. In each of
these views, the body image is seen as an x-ray "slice" of the
body. Typically, parallel slices are taken at different levels of
the body, i.e., at different axial (z-axis) positions. This
recorded image is called a tomogram, and "computerized axial
tomography" refers to the recorded tomogram "sections" at different
axial levels of the body. In multislice CT, a two-dimensional (2D)
array of detector elements replaces the linear array of detectors
used in conventional CT scanners. The 2D detector array permits the
CT scanner to simultaneously obtain tomographic data at different
slice locations and greatly increases the speed of CT image
acquisition. Multislice CT facilitates a wide range of clinical
applications, including three-dimensional (3D) imaging, with a
capability for scanning large longitudinal volumes with high z-axis
resolution.
[0004] Magnetic resonance imaging (MRI) is another method of
obtaining images of the interior of objects, especially the human
body. More specifically, MRI is a non-invasive, non-x-ray
diagnostic technique employing radio-frequency waves and intense
magnetic fields to excite molecules in the object under evaluation.
Like a CAT scan, MRI provides computer-generated image "slices" of
the body's internal tissues and organs. As with CAT scans, MRI
facilitates a wide range of clinical applications, including 3D
imaging, and provides large amounts of data by scanning large
volumes with high resolution.
[0005] Medical image data, which are collected with medical imaging
devices, such as X-ray devices, MRI devices, Ultrasound devices,
Positron Emission Tomography (PET) devices or CT devices in the
diagnostic imaging departments of medical institutions, are used
for an image interpretation process called "reading" or "diagnostic
reading." After an image interpretation report is generated from
the medical image data, the image interpretation report, possibly
accompanied by representative images or representations of the
examination, are sent to the requesting physicians. Today, these
image interpretation reports are usually digitized, stored, managed
and distributed in plain text in a Radiology Information System
(RIS) with accompanying representative images and the original
examination stored in a Picture Archiving Communication System
(PACS) which is often integrated with the RIS.
[0006] Typically, prior to the interpretation or reading, medical
images may be processed or rendered using a variety of imaging
processing or rendering techniques. Recent developments in
multi-detector computed tomography (MDCT) scanners and other
scanning modalities provide higher spatial and temporal resolutions
than the previous-generation scanners.
[0007] Advanced image processing was first performed using computer
workstations. However, there are several limitations to a
workstation-based advanced image processing system. The hardware
and software involved with these systems are expensive, and require
complicated and time consuming installations. Because the
workstation can only reside in one location, users must physically
go to the workstation to use the advanced image processing software
and tools. Also, only one person can use the workstation at a
time.
[0008] Some have improved on this system by converting the
workstation-based advanced image processing system to a
client-server-based system. These systems offer some improvements
over the workstation-based systems in that a user can use the
client remotely, meaning the user does not have to be physically
located near the server, but can use his/her laptop or computer
elsewhere to use the software and tools for advanced image
processing. Also, more than one client can be used with a given
server at one time. This means that more than one user can
simultaneously and remotely use the software that is installed on
one server. The computational power of the software in a
client-server-based system is distributed between the server and
the client. In a "thin client" system, the majority of the
computational capabilities exist at the server. In a "thick client"
system, more of the computational capabilities, and possibly data,
exist on the client.
[0009] The hardware software installation and maintenance costs and
complexity of a client-server based system are still drawbacks.
Also, there can be limitations on the number of simultaneous users
that can be accommodated. Hardware and software must still be
installed and maintained. Generally the information technology (IT)
department of the center which purchased the system must be heavily
involved, which can strain resources and complicate the
installation and maintenance process.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Embodiments of the invention are illustrated by way of
example and not limitation in the figures of the accompanying
drawings in which like references indicate similar elements.
[0011] FIGS. 1A and 1B are block diagrams illustrating a
cloud-based image processing system according to certain
embodiments of the invention.
[0012] FIG. 2 is a block diagram illustrating a cloud-based image
processing system according to another embodiment of the
invention.
[0013] FIGS. 3A-3D are diagrams illustrating examples of access
control data structures according to certain embodiments of the
invention.
[0014] FIGS. 4A-4C are screenshots illustrating certain graphical
user interfaces (GUIs) of a cloud-based image processing system
according to one embodiment of the invention.
[0015] FIG. 5 is a block diagram illustrating a cloud-based image
collaboration system according to one embodiment of the
invention.
[0016] FIGS. 6A-6D are screenshots illustrating certain GUIs of a
medical image collaboration system according certain embodiments of
the invention.
[0017] FIG. 7 is a flow diagram illustrating a method for
processing medical images in a collaboration environment according
to one embodiment of the invention.
[0018] FIG. 8 is a flow diagram illustrating a method for
processing medical images in a collaboration environment according
to another embodiment of the invention.
[0019] FIG. 9 is a block diagram illustrating a cloud-based image
processing system according to another embodiment of the
invention.
[0020] FIGS. 10A and 10B are screenshot illustrating examples of
graphical user interfaces for configuring data gateway management
according to certain embodiments of the invention.
[0021] FIG. 11 is a screenshot illustrating examples of GUIs for
configuring anonymous data gateway management according to certain
embodiments of the invention.
[0022] FIGS. 12A-12C are block diagrams illustrating certain system
configurations according to some embodiments of the invention.
[0023] FIG. 13 is a flow diagram illustrating a method for
anonymizing medical data according to another embodiment of the
invention.
[0024] FIG. 14 is a block diagram of a data processing system,
which may be used with one embodiment of the invention.
DETAILED DESCRIPTION
[0025] Various embodiments and aspects of the inventions will be
described with reference to details discussed below, and the
accompanying drawings will illustrate the various embodiments. The
following description and drawings are illustrative of the
invention and are not to be construed as limiting the invention.
Numerous specific details are described to provide a thorough
understanding of various embodiments of the present invention.
However, in certain instances, well-known or conventional details
are not described in order to provide a concise discussion of
embodiments of the present inventions.
[0026] Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in conjunction with the embodiment can be
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification do not necessarily all refer to the same
embodiment.
[0027] According to some embodiments, advanced image processing
systems are provided as cloud-based systems, particularly, for
processing medical images. According to one embodiment, a cloud
server is configured to provide advanced image processing services
to a variety of clients, such as physicians from medical
institutes, sole practitioners, agents from insurance companies,
patients, medical researchers, regulating bodies, etc. A cloud
server, also referred to as an image processing server, has the
capability of processing one or more medical images to allow
multiple participants to view and process the images either
independently or in a collaborated manner or conferencing
environment. Different participants may participate in different
stages of a discussion session or a workflow process of the images.
Dependent upon the privileges associated with their roles (e.g.,
doctors, insurance agents, patients, or third party data analysts
or researchers), different participants may be limited to access
only a portion of information relating to the images or a subset of
the processing tools without compromising the privacy of the
patients associated with the images.
[0028] According to some embodiments, a cloud-based medical image
processing system includes a data gateway manager to automatically
and/or manually transfer medical data to/from data providers such
as medical institutes. Such data gateway management may be
performed based on a set of rules or policies, which may be
configured by an administrator or authorized personnel. In one
embodiment, in response to updates to medical image data during an
image discussion session or image processing operations performed
at the cloud, the data gateway manager is configured to transmit
over a network (e.g., Internet or intranet) the updated image data
or data representing the difference between the updated image data
and the original image data to a data provider that provided the
original medical images. Similarly, the data gateway manager may be
configured to transfer any new images from the data provider and
store them in a data store of the cloud-based system. In addition,
the data gateway manager may further transfer data amongst multiple
data providers that are associated with the same entity (e.g.,
multiple facilities of a medical institute). Furthermore, the
cloud-based system may automatically perform certain image
pre-processing operations of the received images using certain
advanced image processing resources provided by the cloud systems.
The gateway manager may comprise a router, a computer, software or
any combination of these components.
[0029] FIGS. 1A and 1B are block diagrams illustrating a
cloud-based image processing system according to certain
embodiments of the invention. Referring to FIG. 1A, according to
one embodiment, system 100 includes one or more entities or
institutes 101-102 communicatively coupled to cloud 103 over a
network. Entities 101-102 may represent a variety of organizations
such as medical institutes having a variety of facilities residing
all over the world. For example, entity 101 may include or be
associated with image capturing device or devices 104, image
storage system (e.g., PACS) 105, router 106, and/or data gateway
manager 107. Image storage system 105 may be maintained by a third
party entity that provides archiving services to entity 101, which
may be accessed by workstation 108 such as an administrator or user
associated with entity 101. Note that throughout this application,
a medical institute is utilized as an example of an organization
entity. However, it is not so limited; other organizations or
entities may also be applied.
[0030] In one embodiment, cloud 103 may represent a set of servers
or clusters of servers associated with a service provider and
geographically distributed over a network. For example, cloud 103
may be associated with a medical image processing service provider
such as TeraRecon of Foster City, Calif. A network may be a local
area network (LAN), a metropolitan area network (MAN), a wide area
network (WAN) such as the Internet or an intranet, or a combination
thereof. Cloud 103 can be made of a variety of servers and devices
capable of providing application services to a variety of clients
such as clients 113-116 over a network. In one embodiment, cloud
103 includes one or more cloud servers 109 to provide image
processing services, one or more databases 110 to store images and
other medical data, and one or more routers 112 to transfer data
to/from other entities such as entities 101-102. If the cloud
server consists of a server cluster, or more than one server, rules
may exist which control the transfer of data between the servers in
the cluster. For example, there may be reasons why data on a server
in one country should not be placed on a server in another
country.
[0031] Server 109 may be an image processing server to provide
medical image processing services to clients 113-116 over a
network. For example, server 109 may be implemented as part of a
TeraRecon AquariusNET.TM. server and/or a TeraRecon AquariusAPS
server. Data gateway manager 107 and/or router 106 may be
implemented as part of a TeraRecon AquariusGATE device. Medical
imaging device 104 may be an image diagnosis device, such as X-ray
CT device, MRI scanning device, nuclear medicine device, ultrasound
device, or any other medical imaging device. Medical imaging device
104 collects information from multiple cross-section views of a
specimen, reconstructs them, and produces medical image data for
the multiple cross-section views. Medical imaging device 104 is
also referred to as a modality.
[0032] Database 110 may be a data store to store medical data such
as digital imaging and communications in medicine (DICOM)
compatible data or other image data. Database 110 may also
incorporate encryption capabilities. Database 110 may include
multiple databases and/or may be maintained by a third party vendor
such as storage providers. Data store 110 may be implemented with
relational database management systems (RDBMS), e.g., Oracle.TM.
database or Microsoft.RTM. SQL Server, etc. Clients 113-116 may
represent a variety of client devices such as a desktop, laptop,
tablet, mobile phone, personal digital assistant (PDA), etc. Some
of clients 113-116 may include a client application (e.g., thin
client application) to access resources such as medical image
processing tools or applications hosted by server 109 over a
network. Examples of thin clients include a web browser, a phone
application and others.
[0033] According to one embodiment, server 109 is configured to
provide advanced image processing services to clients 113-116,
which may represent physicians from medical institutes, agents from
insurance companies, patients, medical researchers, etc. Cloud
server 109, also referred to as an image processing server, has the
capability of hosting one or more medical images and data
associated with the medical images to allow multiple participants
such as clients 113-116, to participate in a discussion/processing
forum of the images in a collaborated manner or conferencing
environment. Different participants may participate in different
stages and/or levels of a discussion session or a workflow process
of the images. Dependent upon the privileges associated with their
roles (e.g., doctors, insurance agents, patients, or third party
data analysts or researchers), different participants may be
limited to access only a portion of the information relating to the
images or a subset of the tools and functions without compromising
the privacy of the patients associated with the images.
[0034] According to some embodiments, data gateway manager 107 is
configured to automatically or manually transfer medical data
to/from data providers (e.g., PACS systems) such as medical
institutes. Such data gateway management may be performed based on
a set of rules or policies, which may be configured by an
administrator or authorized personnel. In one embodiment, in
response to updates of medical images data during an image
discussion session or image processing operations performed in the
cloud, the data gateway manager is configured to transmit over a
network (e.g., Internet) the updated image data or the difference
between the updated image data and the original image data to a
data provider such as PACS 105 that provided the original medical
image data. Similarly, data gateway manager 107 can be configured
to transmit any new images and/or image data from the data
provider, where the new images may have been captured by an image
capturing device such as image capturing device 104 associated with
entity 101. In addition, data gateway manager 107 may further
transfer data amongst multiple data providers that is associated
with the same entity (e.g., multiple facilities of a medical
institute). Furthermore, cloud 103 may include an advanced
preprocessing system (not shown) to automatically perform certain
pre-processing operations of the received images using certain
advanced image processing resources provided by the cloud systems.
In one embodiment, gateway manager 107 is configured to communicate
with cloud 103 via certain Internet ports such as port 80 or 443,
etc. The data being transferred may be encrypted and/or compressed
using a variety of encryption and compression methods. The term
"Internet port" in this context could also be an intranet port, or
a private port such as port 80 or 443 etc. on an intranet.
[0035] Thus, using a cloud-based system for advanced image
processing has several advantages. A cloud system refers to a
system which is server-based, and in which the software clients are
very thin--possibly just a web browser, a web browser with a
plug-in, or a mobile or phone application, etc. The server or
server cluster in the cloud system is very powerful computationally
and can support several users simultaneously. The server may reside
anywhere and can be managed by a third party so that the users of
the software in the cloud system do not need to concern themselves
with software and hardware installation and maintenance.
[0036] A cloud system also allows for dynamic provisioning. For
example, if facility X needs to allow for a peak of 50 users, they
currently need a 50 user workstation or client-server system. If
there are 10 such facilities, then a total of 500 users must be
provided for with workstations, or client-server equipment, IT
staff, etc. Alternatively, if these same facilities use a cloud
service, and for example, the average number of simultaneous users
at each place is 5 users, then the cloud service only needs to
provide enough resource to handle the average (5 simultaneous
users) plus accommodations for some peaks above that. For the 10
facilities, this would mean 50 simultaneous users to cover the
average and conservatively 100 simultaneous users to cover the
peaks in usage. This equates to a 150-user system on the cloud
system vs. a 500-user system using workstations or a client-server
model, resulting in a 70% saving in cost of equipment and resources
etc. This allows lower costs and removes the need for the
individual sites to have to manage the asset.
[0037] Cloud computing provides computation, software, data access,
and storage services that do not require end-user knowledge of the
physical location and configuration of the system that delivers the
services. Cloud computing providers deliver applications via the
Internet, which are accessed from Web browsers, desktop and mobile
apps, while the business software and data are stored on servers at
a remote location. Cloud application services deliver software as a
service over the Internet, eliminating the need to install and run
the application on the customer's own computers and simplifying
maintenance and support.
[0038] A cloud system can be implemented in a variety of
configurations. For example, a cloud system can be a public cloud
system as shown in FIG. 1A, a community cloud system, a hybrid
cloud system, a private cloud system as shown in FIG. 1B, or a
combination thereof. Public cloud describes cloud computing in the
traditional mainstream sense, whereby resources are dynamically
provisioned to the general public on a self-service basis over the
Internet, via Web applications/Web services, or other internet
services, from an off-site third-party provider who bills on a
utility computing basis. Community cloud shares infrastructure
between several organizations from a specific community with common
concerns (security, compliance, jurisdiction, etc.), whether
managed internally or by a third-party and hosted internally or
externally. The costs are spread over fewer users than a public
cloud (but more than a private cloud), so only some of the benefits
of cloud computing are realized. Hybrid cloud is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together, offering the benefits of
multiple deployment models. Briefly it can also be defined as a
multiple cloud systems which are connected in a way that allows
programs and data to be moved easily from one deployment system to
another. Private cloud is infrastructure operated solely for a
single organization, whether managed internally or by a third-party
and hosted internally or externally. Generally, access to a private
cloud is limited to that single organization or its affiliates.
[0039] With cloud computing, users of clients such as clients
113-116 do not have to maintain the software and hardware
associated with the image processing. The users only need to pay
for usage of the resources provided from the cloud as and when they
need them, or in a defined arrangement, such as a monthly or annual
contract. There is minimal or no setup and users can sign up and
use the software immediately. In some situations, there may be a
small software installation, like a Citrix or java or plug-in. Such
a configuration lowers up-front and maintenance costs for the users
and there is no or lower hardware, software, or maintenance costs.
The cloud servers can handle backups and redundancies and security
so the users do not have to worry about these issues. The users can
have access to all and the newest clinical software without having
to install the same. Tools and software are upgraded (automatically
or otherwise) at the servers to the latest versions. Access to
tools is driven by access level, rather than by software
limitations. Cloud servers can have greater computational power to
preprocess and process images and they can be larger and more
powerful with better backup, redundancy, security options. For
example, a cloud server can employ volume rendering techniques
available from TeraRecon to render large volume of medical images.
Further detailed information concerning the volume rendering
techniques can be found in U.S. Pat. Nos. 6,008,813 and 6,313,841,
which are incorporated by reference herein.
[0040] According to one embodiment, image processing services
provided by cloud 103 can be provided based on a variety of
licensing models, such as, for example, based on the number of
users, case uploads (e.g., number of cases, number of images or
volume of image data), case downloads (e.g., number of cases,
number of images or volume of image data), number of cases
processed and/or viewed, image processing requirements, type of
user (e.g., expert, specialty or general user), by clinical trial
or by research study, type of case, bandwidth requirements,
processing power/speed requirements, priority to processing
power/speed (e.g., system in ER may pay for higher priority),
reimbursement or billing code (e.g., user may only pay to perform
certain procedures that are reimbursed by insurance), time using
software (e.g., years, months, weeks, days, hours, even minutes),
time of day using software, number of concurrent users, number of
sessions, or any combination thereof.
[0041] FIG. 2 is a block diagram illustrating a cloud-based image
processing system according to another embodiment of the invention.
For example, system 200 may be implemented as part of the system as
shown in FIGS. 1A and 1B. Referring to FIG. 2, system 200 includes
server 109 communicatively coupled to one or more clients 202-203
over network 201, which may be a LAN, MAN, WAN, or a combination
thereof. Server 109 is configured to provide cloud-based image
processing services to clients 202-203 based on a variety of usage
licensing models. Each of clients 202-203 includes a client
application such as client applications 211-212 to communicate with
a server counterpart 209, respectively, to access resources
provided by server 109. Server application 209 may be implemented
as a virtual server or instance of the server application 209, one
for each client.
[0042] According to one embodiment, server 109 includes, but is not
limited to, workflow management system 205, medical data store 206,
image processing system 207, medical image collaboration system
208, and access control system 210. Medical data store 206 may be
implemented as part of database 110 of FIGS. 1A and 1B. Medical
data store 206 is utilized to store medical images and image data
received from medical data center (e.g., PACS systems) 105 or other
image storage systems 215 (e.g., CD-ROMs, or hard drives) and
processed by image processing system 207 and/or image preprocessing
systems 204. Image processing system 207 includes a variety of
medical imaging processing tools or applications that can be
invoked and utilized by clients 202-203 via their respective client
applications 211-212, respectively, according to a variety of
licensing terms or agreements. It is possible that in some medical
institutes that the image storage system 215 and the image
capturing device 104 may be combined.
[0043] In response to image data received from medical data center
105 or from image capturing devices (not shown) or from another
image source, such as a CD or computer desktop, according to one
embodiment, image preprocessing system 204 may be configured to
automatically perform certain preprocesses of the image data and
store the preprocessed image data in medical data store 206. For
example, upon receipt of image data from PACS 105 or directly from
medical image capturing devices, image preprocessing system 204 may
automatically perform certain operations, such as bone removal,
centerline extraction, sphere finding, registration, parametric map
calculation, reformatting, time-density analysis, segmentation of
structures, and auto-3D operations, and other operations. Image
preprocessing system 204 may be implemented as a separate server or
alternatively, it may be integrated with server 109. Furthermore,
image preprocessing system 204 may perform image data preprocesses
for multiple cloud servers such as server 109.
[0044] In one embodiment, a client/server image data processing
architecture is installed on system 200. The architecture includes
client partition (e.g., client applications 211-212) and server
partition (e.g., server applications 209). The server partition of
system 200 runs on the server 109, and communicates with its client
partition installed on clients 202-203, respectively. In one
embodiment, server 109 is distributed and running on multiple
servers. In another embodiment, the system is a Web-enabled
application operating on one or more servers. Any computer or
device with Web-browsing application installed may access and
utilize the resources of the system without any, or with minimal,
additional hardware and/or software requirements.
[0045] In one embodiment, server 109 may operate as a data server
for medical image data received from medical image capturing
devices. The received medical image data is then stored into
medical data store 206. In one embodiment, for example, when client
202 requests for unprocessed medical image data, server application
209 retrieves the data from the medical data store 206 and renders
the retrieved data on behalf of client 202.
[0046] Image preprocessing system 204 may further generate workflow
information to be used by workflow management system 205. Workflow
management system 205 may be a separate server or integrated with
server 109. Workflow management system 205 performs multiple
functions according to some embodiments of the invention. For
example, workflow management system 205 performs a data server
function in acquiring and storing medical image data received from
the medical image capturing devices. It may also act as a graphic
engine or invoke image processing system 207 in processing the
medical image data to generate 2D or 3D medical image views.
[0047] In one embodiment, workflow management system 205 invokes
image processing system 207 having a graphics engine to perform 2D
and 3D image generating. When a client (e.g., clients 202-203)
requests for certain medical image views, workflow management
system 205 retrieves medical image data stored in medical data
store 206, and renders 2D or 3D medical image views from the
medical image data. The end results for medical image views are
sent to the client.
[0048] In one embodiment, when a user making adjustments to the
medical image views received from server 109, these user adjustment
requests are sent back to the workflow management system 205.
Workflow management system 205 then performs additional graphic
processing based on the user requests, and the newly generated,
updated medical image views are returned to the client. This
approach is advantageous because it eliminates the need to
transport large quantity of unprocessed medical image data across
network, while providing 2D or 3D image viewing to client computers
with minimal or no 2D or 3D image processing capacity.
[0049] As described above, when implemented as a cloud based
application, system 200 includes a client-side partition and a
server-side partition. Functionalities of system 200 are
distributed to the client-side or server-side partitions. When a
substantial amount of functionalities are distributed to the
client-side partition, system 200 may be referred to as a "thick
client" application. Alternatively, when a limited amount of
functionalities are distributed to the client-side partition, while
the majority of functionalities are performed by the server-side
partition, system 200 may be referred to as a "thin client"
application. In another embodiment, functionalities of system 200
may be redundantly distributed both in client-side and server-side
partitions. Functionalities may include processing and data. Server
109 may be implemented as a web server. The web server may be a
third-party web server (e.g., Apache.TM. HTTP Server,
Microsoft.RTM. Internet Information Server and/or Services,
etc).
[0050] In one embodiment, workflow management system 205 manages
the creation, update and deletion of workflow templates. It also
performs workflow scene creation when receiving user requests to
apply a workflow template to medical image data. A workflow is
defined to capture the repetitive pattern of activities in the
process of generating medical image views for diagnosis. A workflow
arranges these activities into a process flow according to the
order of performing each activity. Each of the activities in the
workflow has a clear definition of its functions, the resource
required in performing the activity, and the inputs received and
outputs generated by the activity. Each activity in a workflow is
referred to as a workflow stage, or a workflow element. With
requirements and responsibilities clearly defined, a workflow stage
of a workflow is designed to perform one specific task in the
process of accomplishing the goal defined in the workflow. For many
medical image studies, the patterns of activities to produce
medical image views for diagnosis are usually repetitive and
clearly defined. Therefore, it is advantageous to utilize workflows
to model and document real life medical image processing practices,
ensuring the image processing being properly performed under the
defined procedural rules of the workflow. The results of the
workflow stages can be saved for later review or use.
[0051] In one embodiment, a workflow for a specific medical image
study is modeled by a workflow template. A workflow template is a
template with a predefined set of workflow stages forming a logical
workflow. The order of processing an activity is modeled by the
order established among the predefined set of workflow stages. In
one embodiment, workflow stages in a workflow template are ordered
sequentially, with lower order stages being performed before the
higher order stages. In another embodiment, dependency
relationships are maintained among the workflow stages. Under such
arrangement, a workflow stage cannot be performed before the
workflow stages it is depending on being performed first. In a
further embodiment, advanced workflow management allows one
workflow stage depending on multiple workflow stages, or multiple
workflow stages depending on one workflow stage, etc.
[0052] The image processing operations receive medical image data
collected by the medical imaging devices as inputs, process the
medical image data, and generate metadata as outputs. Metadata,
also known as metadata elements, broadly refers to parameters
and/or instructions for describing, processing, and/or managing the
medical image data. For instance, metadata generated by the image
processing operations of a workflow stage includes image processing
parameters that can be applied to medical image data to generate
medical image views for diagnostic purpose. Further, various
automatic and manual manipulations of the medical image views can
also be captured as metadata. Thus, metadata allows the returning
of the system to the state it was in when the metadata was
saved.
[0053] After a user validates the results generated from processing
a workflow stage predefined in the workflow template, workflow
management system 205 creates a new scene and stores the new scene
to the workflow scene. Workflow management system 205 also allows
the updating and saving of scenes during user adjustments of the
medical image views generated from the scenes. Further detailed
information concerning workflow management system 205 can be found
in co-pending U.S. patent application Ser. No. 12/196,099, entitled
"Workflow Template Management for Medical Image Data Processing,"
filed Aug. 21, 2008, which is incorporated by reference herein in
its entirety.
[0054] Referring back to FIG. 2, according to one embodiment,
server 109 further includes access control system 210 to control
access of resources (e.g., image processing tools) and/or medical
data stored in medical data store 206 from clients 202-203. Clients
202-203 may or may not access certain portions of resources and/or
medicate data stored in medical data store 206 dependent upon their
respective access privileges. The access privileges may be
determined or configured based on a set of role-based rules or
policies, as shown in FIGS. 3A-3D. For example, some users with
certain roles can only access some of the tools provided by the
system as shown in FIG. 3A. Examples of some of the tools available
are listed at the end of this document, and include vessel
centerline extraction, calcium scoring and others. Some users with
certain roles are limited to some patient information as shown in
FIG. 3B. Some users with certain roles can only perform certain
steps or stages of the medical image processes as shown in FIG. 3C.
Steps or stages are incorporated into the tools (listed at the end
of this document) and might include identifying and/or measuring
instructions, validation of previously performed steps or stages
and others. Some users with certain roles are limited to certain
types of processes as shown in FIG. 3D.
[0055] Note that the rules or policies as shown in FIGS. 3A-3D are
described for the purpose of illustration only; other rules and
formats may also be applied. According to some embodiments, access
levels can be configured based on a variety of parameters, such as,
for example, types of tools or steps within a tool, functions
(e.g., uploading, downloading, viewing, manipulating, auditing,
validating, etc.), ability to give others access (e.g., second
opinion, referrals, experts, family, friend etc.), patients, volume
(e.g., may only have access to certain volume of images/month for
example, dependent upon a licensing agreement), medical
institution, specialty, reimbursement or billing code (e.g., may
only have access to perform certain procedures that are reimbursed
by insurance), admin access level, clinical trial or research
project, and way of viewing data--some may only be able to see
individual patients, some aggregate data which can be sliced
different ways, etc.
[0056] FIGS. 4A and 4B are screenshots illustrating certain
graphical user interfaces (GUIs) of a cloud-based image processing
system according to one embodiment of the invention. For example,
GUI 400 of FIGS. 4A and 4B may be presented by workflow management
system 205 at clients 202-203 as part of client applications
211-212 of FIG. 2. Referring to FIGS. 4A and 4B, GUI 400 includes
one or more controls or buttons 401 to configure certain settings
of the application, such as preferences, help and others. GUI 400
further includes display area 402 to display a certain patient
study list, where the list may be obtained via a search that is
configured based on one or more search options 403, such as by
patient ID, patient name, date, modality or others. GUI 400 further
includes display area 404 to display a list of tasks or workflows
to be handled by different personnel. The task list may be
presented as a table as shown in FIG. 4A or alternatively as a
timeline as shown in FIG. 4B. GUI 400 further includes image
preview area 405 to display a preview of an image of a particular
patient in question, optionally including patient information 407
and a set of one or more imaging viewing tools 406, such as
brightness, contrast and others. The availability of the patient
information 407, certain detailed information of image preview 405,
and tools 406 may be determined based on access privileges of a
particular user, which may be controlled by access control system
210. Furthermore, GUI 400 further includes a set of one or more
file management tools 408 for managing image files, such as import,
load, send, upload, etc. Note that GUIs described throughout this
application are shown for the purposes of illustration only; other
formats or layouts may also be implemented. Certain GUI control or
button can be activated via a variety of mechanisms, such as
keyboard, keypad, touch screen, touch pad, PDA controls, phone
controls, mouse click, an interactive voice command, or a
combination thereof.
[0057] Referring back to FIG. 2, according to one embodiment,
server 109 may further include a tracking system (not shown), which
may be integrated with server 109 or alternatively maintained by a
third party vendor and accessible by server 109. The tracking
system is configured to monitor and track user activities with
respect to medical data stored in medical data store 206. Because
of certain FDA requirements, there is a need to track what users
have accessed the software, when, and the steps they have used
within the software. There is also a need to track overall trends
in software use, for example how long it takes a user to complete a
certain type of case, or certain steps, billing trends etc.
According to some embodiments, the tracking system is configured to
track users who log in and utilize the software, steps the users
perform, date and time of the accesses, etc. The tracking system
can be used to analyze volumes used on, and performance of, the
system. The tracking system can be used to track FDA/HIPAA
compliance. It can also be utilized by insurance companies to track
billing codes and costs. It can be used to determine trends (time
to analyze certain types of cases etc.) Analysis of tracked data
can also be used to identify different user types, for example
expert users, casual users, technicians, physicians, etc. This
information can then be used to improve the software product,
upsell, improve customer service, improve billing models, etc. The
tracking system can be used to track aggregate data as well as
detailed data.
[0058] According to one embodiment, the browser/client/mobile
application standards allow easier integration with an electronic
health record. The integration can be done as seamless as possible,
so one does not have to open separate applications or repeatedly
enter login information. Integration may be based on patient ID, or
other common parameter which automatically links different types of
records. It can also be used to link anonymous cases to online
publications--allowing 3D or advanced views of case images. The
cloud-based system is flexible so it can adapt integration
standards as they develop and as they evolve.
[0059] As described above, advanced image processing in the cloud
model allows users from anywhere to access and contribute to the
same case or cases. An extension of these concepts is a clinical or
research trial. In a clinical or research trial, patient data from
different geographical locations are grouped and tracked over time
to identify trends. In the case of a clinical trial, the trends may
be related to a particular treatment or drug. Advanced image
processing is useful in tracking these trends. For example, a drug
for cancer can be assessed by tracking patients with cancer who
have taken the drug or a placebo. The software can be used to
measure the size of the tumor, or other aspects of the patient,
such as side effects. For example, a drug to treat liver cancer may
have a potential adverse effect on the function of the heart or
other organs. A clinical trial can use advanced image processing to
track not only the health of the liver, but also the health of the
heart and other organs over time.
[0060] The cloud model allows for doctors or other participants all
over the world to participate. It controls what tools are used and
how and by whom. It allows data to be aggregated because all data
is stored on the same server or cluster of servers. It allows
easier training for the doctors and technicians and others involved
in analyzing data for clinical trial or research study. The
cloud-based model easily supports the role of auditor/quality
control person and supports an FDA or company oversight role. It
monitors data trends as trial is progressing and performs data
analysis at the end of a trial/study and also during a trial/study.
It also integrates with data analysis software, giving access to
third parties such as a sponsor or the FDA, and controls access and
level of access.
[0061] Cloud-Based Medical Data Mining Services
[0062] The cloud based system as shown in FIG. 2 allows data from
several different medical centers and geographies to be located on
one server or one group of servers. Because the data is all in one
place (geographically or virtually), the data can be combined and
used in the aggregate. This aggregation can be for one patient,
across patients, across time, or any combination of these.
According to one embodiment, server 109 further includes a data
mining component or system (not shown) configured to perform data
mining on medical data received from a variety of data sources. The
data mining component may be integrated with server 109 or
alternatively maintained by a third party service provider over a
network and invoked by server 109. The data mining system is
configured to provide cloud-based data mining or analysis services
on medical data stored in medical data store 206 to a variety of
clients over the Internet. The data mining system can perform a
variety of mining and analysis operations on demand from a client,
or automatically, and generate and deliver the results to the
client based on a variety of service or licensing agreements.
[0063] Some of the reasons one would want to mine aggregated data
relating to quantitative image processing include clinical trials,
clinical research, trend identification, prediction of disease
progress, diagnosis, artificial intelligence, and for use by
insurance companies. Quantitative image processing refers to image
processing that results in a value such as a measurement of a tumor
diameter. According to one embodiment, the data mining system has
the ability to do massive, anonymized, automatic, and continual
analysis and trending on all the data from multiple sources. The
data mining system can perform quantitative image data analysis
that can be performed before a user requires the data (in the
background, at night, etc.) The data mining system has the ability
to access and use this information quickly and in real time since
the user does not have to download all the images and then do the
analysis every time he/she needs to use the results. It can provide
more flexible licensing, geographies and access, including
controlling access by teams, specialties and access levels.
[0064] For example, a patient may come into a medical center to
have a CT scan done to assess the growth of nodules in his lung.
Currently, data relating to the size of his lung nodule can be
collected over time, but it is difficult to put that data into
context. Context in this example could be either time or population
context. Advanced image processing techniques described herein can
be used to measure the location, size, shape and/or volume, or
other parameter of the nodules, but there is not a good way to
determine the growth rate (time context) or how this patient
compares to other patients with lung nodules (population context).
Having access to this information could aide with diagnoses (e.g.,
whether the nodule is likely cancerous) and treatment (other
patients with similarly growing lung nodules have responded well to
medication x), among other things.
[0065] Over time, the patient image data from several
geographically dispersed medical centers can be stored on a
server-based system, data can be mined and analyzed to determine
for example: 1) whether an aneurysm is likely to rupture based on
certain quantitative characteristics of that aneurysm; 2) whether a
tumor is likely to be malignant based on certain quantitative
characteristics of the tumor; and 3) whether a growth is growing
more quickly or more slowly than average and what that difference
might mean to the patient clinically.
[0066] A clinical trial involving a new hip implant can use imaging
data to determine whether the implant is remaining secure over
time. The participants in this clinical trial can be geographically
dispersed allowing for much more data and therefore a quicker and
better study conclusion. Research around a rare type of brain tumor
can advance more quickly because the imaging data can be obtained
from any medical center in the world, thus allowing more of the
rare patients to enroll in the study. New ways of evaluating aortic
aneurysms may be discovered 10 years from now and previous imaging
data can be re-analyzed retrospectively using the newly discovered
information. Or, in the future, a doctor may receive a message from
the cloud server saying "we have just developed the ability to
detect tumors with more sensitivity and hence please be alerted
that we found a possible precursor to a tumor in the scan from 5
years ago that patient X had. Follow-up recommended". If enough
data can be aggregated and analyzed, the system will be able to
suggest treatments for various diseases which are more likely to be
successful, based on the data analysis. Standardizing analysis of
patient images may be desirable if the data will be used in the
aggregate. Standardized analysis tools can control what steps are
done by whom or how steps are done, narrow ranges on steps, or
limit steps, and pre-process on server either outside of or within
users' control.
[0067] Using data mining, data may be analyzed from different
perspectives and summarized into useful or relevant information.
Data mining allows users to analyze data from many different
dimensions or angles, categorize the data, and summarize the
relationships identified. Clinical data mining may be used, for
example, to identify correlations or patterns among fields in
relational and/or other databases. The data mining system may
include a capability to compare data across modalities and/or data
sources for a particular patient, for example.
[0068] In certain embodiments, system 109 may include a portal or
interface (e.g., application programming interface or API) in which
information for a patient may be accessed. Once a patient is
identified, a user interface is presented. The user interface
includes patient demographics, current order information, current
patient information (e.g., medication, allergies, chief complaint,
labs, etc.), historical information (e.g., renal failure, family
history, previous invasive and/or non-invasive procedures, etc.),
dynamic measurement analysis, and/or configurable normal
values.
[0069] The data mining system provides a dynamic snapshot of vital
measurements and relevant findings across all studies in the
medical data store 206 for a particular patient. The data mining
system supports access to multiple data sources, cross-modality
comparison, cross-data source comparisons and the like. In some
embodiments, the data mining system allows data elements to be
registered or subscribed so that a user, administrator and/or
system setting may specify how to retrieve certain data through a
variety of communications protocols (e.g., SQL, extensible markup
language (XML), etc.), what functions can be applied to certain
data, in which modality(ies) and/or data source(s) can a certain
data element be found, whether data is enumerated and/or numeric
data, etc.
[0070] The data mining in conjunction with the system 109 helps
improve efficiency by reducing steps involving users, such as
medical staff, to retrieve historical data and compare findings
from previous procedures. This has previously been done manually,
semi-manually or not at all. The data mining system retrieves,
calculates and correlates data automatically. The data mining
system may provide visual indicators of data relationships along
with the data. In addition, the data mining system helps providing
a more efficient workflow to compare and trend data, which allows a
physician or other healthcare practitioner to track disease
progression and/or disease regression within the scope of
evidence-based medicine. FIG. 4C is a screenshot illustrating a GUI
presenting a result of data mining operations performed by a cloud
server according to one embodiment of the invention. In the example
represented in FIG. 4C, the current patient has a tumor, the
diameter of which is represented by the triangle on the graph. The
other data on the graph represent mined data of similar patients
and/or similar tumors. The current patient's tumor diameter can be
seen in context of the mined data. Note that in this example, an
alert is displayed that says the current patient's tumor diameter
is within the malignant range of data. Similar alerts would be
generated depending on how the data is interpreted by the advanced
image processing system.
[0071] Some embodiments provide intelligent clinical data mining.
In intelligent data mining, data sets are generated based on
relevant study information. The data mining system may mine data
across all studies for a particular patient which includes
different modalities and potentially multiple data sources. The
data mining system provides real-time (or substantially real-time)
analysis of vital measurements and relevant findings across all
studies for a particular patient that helps improve a clinical
ability to predict, diagnose and/or treat the patient.
[0072] In some embodiments, the data mining system provides
interactive graphing capabilities for mined data elements. For
example, a user can select different data points to be included in
the graph, indicate a type of graph (e.g., line or bar), and select
a size of the graph. A user may select different function(s) to be
applied to a specific data element such as change or difference,
average, min, max, range, etc. A user may utilize the data mining
system to compare qualitative and/or quantitative data. The data
mining system may be applicable to a wide variety of clinical areas
and specialties, such as cardiology, disease progression and
regression, evidence-based medicine, radiology, mammography (e.g.,
track mass growth/reduction), interventional neurology, radiology,
cardiology (e.g., measure stenosis progression of carotid artery
disease), hematology, oncology, etc.
[0073] In some embodiments the data mining system provides
real-time or substantially real-time analysis that helps improve a
clinical ability to predict, diagnose and treat. Providing better
tools and better access to improved information leads to better
decisions. Through trending and comparing of clinical data, the
data mining system has the ability to generate graphs to give a
user a visual representation of different data elements.
[0074] For example, a cardiac physician may want to review findings
from previous cardiac cases in order to compare and trend relevant
data. Comparing and trending the data allows the physician to track
disease progression and/or disease regression. Pre-procedurally,
the physician is provided with an ability to be better prepared and
informed of relevant clinical data that is pertinent to an upcoming
procedure. Post-procedurally, the physician is provided with an
ability to compare and trend the findings within the scope of
evidence-based medicine to track disease progression or regression
and potentially recommend other therapies. Access to the aggregated
data can also be licensed to the user separately from the use of
the software itself.
[0075] Cloud-Based Medical Image Collaboration System
[0076] Referring back to FIG. 2, according to one embodiment,
server 109 further includes medical image collaboration system 208
capable of hosting a medical image discussion and processing
session amongst participants such as clients 202-203 discussing and
processing medical images retrieved from medical data store 206 in
a collaboration fashion. Each participant can participate (e.g.,
view and/or modify images) in the discussion to a certain degree
dependent upon his/her respective access privileges controlled by
access control system 210. Different participants may participate
in different stages of a discussion session or a workflow process
of the images. Dependent upon the access or user privileges
associated with their roles (e.g., doctors, insurance agents,
patients, or third party data analysts or researchers), different
participants may be limited to access only a portion of the
information relating to the images or processing tools without
compromising the privacy of the patients associated with the
images. Participants may also communicate with each other in a
collaborative manner, including via chat, instant messaging, voice
or other means. This communication may be in either real time or
recorded.
[0077] FIG. 5 is a block diagram illustrating a cloud-based image
collaboration system according to one embodiment of the invention.
Referring to FIG. 5, system 500 includes a medical image
collaboration system 208 configured to host a discussion and
processing forum in the cloud concerning a medical image, which is
accessible by clients 202-203 in a collaborated manner from
anywhere in the world through the Internet. In one embodiment,
collaboration system 208 includes a collaboration module 501 to
coordinate communications and actions amongst clients 202-203
discussing, viewing and/or manipulating host image and/or image
data 502. In response to an image manipulation or rendering command
received from one of clients 202-203 on image 502, collaboration
module 501 is configured to invoke image processing system 207 to
render image 502 according to the command. Collaboration module 501
is configured to update image and/or image data 502 based on the
rendering result received from image processing system 207.
[0078] In addition, collaboration module 501 generates, via image
processing system 207, client images and/or image data 504-505 and
transmits client images and/or image data 504-505 to clients
202-203, respectively. Client images and/or image data 504-505 may
be viewed based on access privileges (e.g., part of access control
list or ACL 503) of clients 202-203. Certain information associated
with host image and/or image data 502 may not be visible based on
the access privileges of clients 202-203. For example, if a user of
client 202 is an auditor, client image 504 may not include patient
information, based on the ACL as shown in FIG. 3B. However, if a
user of client 203 is a physician, client image 505 may include the
patient information based on the ACL as shown in FIG. 3B. Note that
throughout this application, a client image referred to herein
represents a client image file or files that may include the actual
image (e.g., same or similar to the host image) and other
associated data such as metadata (e.g., DICOM tags or headers),
description or notes of the image, and/or access control data
configuring client applications during processing the image, etc.
In one embodiment, GUIs or controls for invoking certain graphics
rendering tools to manipulate host image and/or image data 502 via
the corresponding client images 504-505 may or may not be enabled
or available at client applications 211-212 dependent upon the
access privileges of users associated with clients 202-203, such as
the one shown in FIG. 3A. Furthermore, host image and/or image data
502 may be part of a particular stage of a workflow process managed
by workflow management system 205. Some of clients 202-203 may
participate in one stage and others may participate in another
stage dependent upon the corresponding ACL such as the one shown in
FIG. 3C. In one embodiment, collaboration module 501 is configured
to coordinate the image processing stages amongst clients 202-203.
When a first client has completed one stage, collaboration module
501 may send a notification to a second client such that the second
client can take over the control of the image data and processing
the image data of the next stage, etc.
[0079] FIGS. 6A-6D are screenshots illustrating certain GUIs of a
medical image collaboration system according to certain embodiments
of the invention. GUIs of FIGS. 6A-6D may be presented by a client
application (e.g., thin client) of various clients operated by
various users. For example, FIG. 6A represents a GUI page by a
client application operated by a user that has a high level of
access privileges. Referring to FIG. 6A, in this example, the user
can view most of the information presented by the image
collaboration system including most of the image processing tools
605 and 608 that can be utilized to manipulate images and/or image
data 601-604, settings 606, workflow templates 607, image viewing
tools 609, such as different orientations (anterior, head,
posterior, right, foot, left), different views (axial, sagittal,
coronal), different screen orientations, etc.], and patient
information 610-613. FIG. 6B represents a GUI page that can be
viewed by another user. Referring to FIG. 6B, this user may not
have the necessary privileges to view the patient information. As a
result, the patient information is not displayed. FIG. 6C represent
a GUI in which a user can only view the image without the
capability of manipulating the images. FIG. 6D represent a GUI in
which a user has a limited capability of manipulating the images
and/or image data.
[0080] FIG. 7 is a flow diagram illustrating a method for
processing medical image data according to one embodiment of the
invention. Method 700 may be performed by cloud server 109 of FIG.
1. Referring to FIG. 7, at block 701, a cloud server receives over
a network a request for accessing three-dimensional (3D) medical
image data from a first user. The cloud server provides image
processing services to a variety of users over the network such as
the Internet. At block 702, the cloud server determines first user
privileges of the first user for accessing the 3D medical image
data. The first user privileges may be related to the 3D medical
image data and may be configured by an owner of the 3D medical
image data. Based on the first user privileges, the cloud server is
configured to limit the image processing tools available to the
first user to process the 3D medical image data.
[0081] FIG. 8 is a flow diagram illustrating a method for
processing medical image data in a collaboration environment
according to another embodiment of the invention. Method 800 may be
performed by a client application such as client applications
211-212 of FIG. 5. Referring to FIG. 8, at block 801, image data is
received at a client from an image processing cloud server over a
network, including information relating to user privileges of a
user of the client device. At block 802, the image data is
displayed via a client application running at the client device. At
block 803, one or more image processing interfaces (e.g., buttons
of a toolbar) of the client application is configured (e.g.,
enabled/activated or disabled/deactivated) based on the user
privileges. In response to a command received via one of the
enabled image processing interface, information representing a
rendering command is transmitted to the image processing server
over the network. In response to updated image data received from
the image processing server, the updated image data is represented
at the local client device. This type of access control may occur
with or without conferencing.
[0082] Conferencing/collaboration includes more than one user
looking at and/or using the advanced imaging software for a
particular study, image or series of images as services provided by
a cloud at the same time or at different times. This might include
a physician consulting with a patient in another location, or a
physician consulting with another physician, or other users. One
user may be using the software to manipulate, measure, analyze
images while other user(s) observe. More than one user may be
actively using the software at the same time. Another example of
collaboration is when more than one user is contributing to a case
or cases at different times. For example one physician may perform
certain steps or stages relating to patient image data, such as the
bone removal step, and then another physician might perform
different steps at a later time, such as vessel
identification/labeling and/or measurement. Another user might
review and validate certain previously performed steps, etc.
[0083] A cloud-based software system allows
conferencing/collaboration to be done on a level not possible
before, using the techniques described throughout this application.
Since the software and the data reside centrally (e.g., a single
server, server farm or redundant servers), it is simply a matter of
providing access to image data and access to the advanced image
processing software. The image data may relate to a subset of one
procedure, one patient, or more than one patient/procedure. The
software and image data can be accessed from anywhere and at any
time, by anybody whom has been provided access, without extensive
software are hardware installation. There are several situations
where it would be desirable to have more than one user access the
images and data relating to a procedure, patient or group of
patients.
[0084] One such situation is when a user seeks a second opinion.
For example, a patient, or physician, or insurer, may want to
obtain a second opinion concerning a procedure, patient or group of
patients. By allowing more than one physician access to the images
and data of a case, a user (e.g., patient, physician, insurer,
etc.) may request and obtain more than one opinion concerning the
case. The physicians may access the data at different times, or at
the same time. Each physician may want to view the steps and views
that the other physician went through to come up with his or her
diagnosis/conclusion. So not only would two physicians be able to
view the same case at either the same time or different times, but
one or more of the physicians, or other users, may be able to view
a record of the steps that the other physician went through to come
up with his or her conclusion. In this scenario, users may
simultaneously or independently utilize the software. Different
access privileges may be applied to different users.
[0085] Similar to the second opinion situation, a patient,
physician or insurer may desire the opinion of an expert in a
particular field. For example, a patient may receive a heart scan
which reveals a rare condition. The patient, his/her physician
and/or insurer may request the opinion of an expert in the field of
that rare condition. Similar to the second opinion scenario, the
users may view and manipulate, measure etc. the images of the case
simultaneously; user history can be tracked as in the second
opinion scenario. In this scenario, users may simultaneously or
independently utilize the software.
[0086] In certain cases, for example in the case of a clinical
trial, the United States Food and Drug Administration (FDA) may
want to monitor the progress and results. In this case, an
individual or individuals at the FDA would be a user and may want
to observe or monitor other users using the software to view or
manipulate images and/or data. The representative from the FDA may
also be interested in looking at anonymous aggregated data. FDA
users may only need to view the anonymous data without the option
of manipulating the data.
[0087] In another example, a representative from an insurer may
want to monitor the results from a patient or group of patients or
doctor(s). In this case, an individual or individuals at the
insurer would be a user and would observe or monitor the results of
other users using the software. The representative from the insurer
may also be interested in looking at anonymous aggregated data.
Insurance users may only need to view the anonymous data without
the option of manipulating the data. They may use the information
for billing purposes or cost reductions/discounts.
[0088] Another potential use for collaborative advanced imaging
software is during a procedure, such as a surgical procedure. For
example, a surgeon in a rural town may want the help of an expert
at a major medical center. Collaborative use of the software in the
actual operating room would allow the surgeon to benefit from the
guidance of the expert in real time. The expert could also help
plan the surgical procedure beforehand using the advanced imaging
software collaboratively. As with any of the embodiments, different
access privileges may be applied to different users.
[0089] In another scenario, a medical center might outsource all or
part of its advanced image processing operations to an outside
company. Or it might outsource only certain types of cases. Or it
might outsource certain steps in cases which are more complex. The
usage of the software can be licensed in a variety of licensing
models. A patient may control the process--this opens up the
opportunity for patients to have more control over their care. For
example, the patient might control a username and password for
their case which they can then give to anyone, including a second
opinion doctor, etc. The username and password may be temporary,
expiring after a configured time frame or a number of uses or other
parameter. Different users such as doctors or technicians, or
experts can do different steps or stages in image processing, for
example, in a workflow process.
[0090] Training is another scenario in which the conferencing and
collaboration can be utilized. In a training environment, there
tends to be a large number of users. Some features of a testing
environment include testing, lectures, certification and others. In
a testing situation, several users will use the advanced image
processing software to view and manipulate or analyze image data on
the same case or different cases, either simultaneously, or
independently. Test scoring can be performed automatically, or by
an instructor viewing the result and process of the various
students. The results may be quantitative or qualitative. In a
lecture situation, there may be a need to present cases to students
in a fairly controlled manner, by limiting what the students can
see on any given case, or by magnifying certain aspects of a case
for closer viewing and/or emphasis. In some situations, there may
be a need to have the same case presented in two windows on the
student's computer so that the student can see what the instructor
is doing in one window, but can also use the advanced imaging
software independently on the same case in the other window. There
may be a need to certify a user to do certain types of cases using
the advanced image processing software or to use certain aspects of
the advanced image processing software. This would involve a fairly
structured course with a test or tests which must be passed in
order for the user to be certified. This type of training could be
done live, or as an online course which is self-paced.
[0091] Traditionally, advanced image processing software has been
used primarily by radiologists. This is largely because
radiologists have access to the workstation with the software
installed at the medical center. But other physicians and
technicians in other specialties, such as cardiology, orthopedics,
dentistry, neurology, pathology, etc., would also benefit from
using this type of advanced image processing system. Since the
cloud-based system effectively eliminates the need for expensive
and extensive hardware and software installation and maintenance,
access to advanced image processing software in the cloud becomes
possible for any type of physician or technician, whether or not
he/she is associated with a medical institution. An individual
physician in private practice can use the software at even its most
advanced level immediately and without up-front costs and
installation delays.
[0092] Since advanced image processing software is complex,
training may be required before a user is proficient using the
software. However, different levels of software can be created for
different user levels. A "dumbed down" version, which does not
include the more complex tools, can be created for basic users,
such as a primary care physician. More advanced versions of the
software can be created for more advanced users such as
radiologists who have been trained to use the more advanced tools.
Different specialties can also have different versions of the
software. For example, cardiologist may only need and want access
to the advanced image processing tools relating to the heart.
[0093] Cloud-Based Medical Data Anonymous Gateway Management
[0094] In order to truly use advanced image processing software in
a cloud model, it is necessary to receive the slice image data from
the modality, or scanner, onto a server in the cloud. Different
types of medical image capturing devices include CT, MRI, PET,
single photon emission computed tomography (SPECT), ultrasound,
tomography, etc. Traditionally, the lack of efficient and effective
methods for moving large volumes of image data has prevented the
transition from workstation and server-based systems to a
cloud-based system. In the past, fewer scans were performed and
there were fewer image slices per scan. The volume of data is
increasing quickly creating the need for an easy way of
transferring significant numbers of large images efficiently and
without extensive setup.
[0095] Currently, most image data is transferred from a medical
image capturing device to a PACS system. A PACS system generally
exists at a medical institution behind a firewall to protect the
data. The existence of a firewall can make the transfer of files in
either direction challenging because of security policies and
rules. A virtual private network (VPN) has been used to transfer
files, but installation of a VPN requires extensive involvement
from the hospital or medical centers IT department to make sure
that all security policies are considered and that the system
integrates seamlessly with their current system. For this reason,
the setup of a VPN is time consuming, unpredictable, and sometimes
impossible or impractical.
[0096] According to some embodiments, a cloud-based medical image
processing system includes a data gateway manager to automatically
transfer medical data to/from data providers such as medical
institutes. Such data gateway management may be performed based on
a set of rules or policies, which may be configured by an
administrator or authorized personnel. In one embodiment, in
response to updates of medical image data during an image
discussion session or image processing operations performed at the
cloud, the data gateway manager is configured to transmit over a
network (e.g., Internet) the updated image data or the difference
between the updated image data and the original image data to a
data provider that provided the original medical images. Similarly,
the data gateway manager is configured to transmit any new images
from the data provider. In addition, the data gateway manager may
further transfer data amongst multiple data providers that is
associated with the same entity (e.g., multiple facilities of a
medical institute). Furthermore, the cloud-based system may
automatically perform certain pre-processing operations of the
received image data using certain advanced image processing
resources provided by the cloud systems.
[0097] FIG. 9 is a block diagram illustrating a cloud-based image
processing system according to another embodiment of the invention.
Referring to FIG. 9, system 900 includes cloud 103 having one or
more cloud servers 109 to provide image processing services to a
variety of clients for processing images stored in medical data
store 206. Image data stored in data store 206 may be received over
network 201 (e.g., Internet) from a variety of data sources such as
data centers 101-102. Server 109 may include some or all of the
functionalities described above. Each of data centers 101-102
includes a data store such as data stores 905-906 to store or
archive medical image data captured by a variety of image capturing
devices, such as CT, MRI, PET, SPECT, ultrasound, tomography, etc.
The data centers 101-102 may be associated with different
organization entities such as medical institutes. Alternatively,
data centers 101-102 may be associated with different facilities of
the same organization entity.
[0098] In one embodiment, each of data centers 101-102 includes a
data gateway manager (also referred to as an uploader and/or
downloader) such as data gateway managers 901-902 to communicate
with cloud 103 to transfer medical data amongst data stores 905-906
and 206, respectively. Data gateway managers 901-902 can
communicate with the cloud server according to a variety of
communications protocols, such as hypertext transfer protocol
secure (HTTPS) (e.g., HTTP with transport layer security/secure
sockets layer (TLS/SSL)), etc., using a variety of encryption
and/or compression techniques.
[0099] In one embodiment, for the purpose of illustration, when new
image data is received from an image capturing device and stored in
data store 905, data gateway manager 901 is configured to
automatically transmit the new image data to cloud 103 to be stored
in data store 206. The new image data may be transmitted to cloud
103 and stored in a specific area or directory of data store 206
based on the configuration or profile set forth in a set of rules
903. Rules 903 may be configured by a user or an administrator via
an API such as a Web interface. Similarly, if new or updated image
data is received from a user and stored in data store 206, for
example, during a Web conferencing session, data gateway manager
907 may be configured to automatically transmit the new or updated
image data to data center 101 according to a set of rules 903,
which may also be configured by a user or administrator. According
to some embodiments, data gateway managers 901-902 may also
communicate with each other to transfer data stored in data stores
905-906, particularly, when data centers 101-102 are associated
with the same organization, or associated in some other way, as in
a clinical or research trial.
[0100] According to one embodiment, each of data gateway managers
901-902 includes a data anonymizer such as anonymizers 909-910,
prior to transmitting medical data to cloud 103 or amongst data
centers 101-102, configured to anonymize certain information from
the medical data, such as patient information including, but is not
limited to, patient name, address, social security number, credit
card information, etc. Such an anonymization can be performed based
on rules which are preconfigured, or configured at the time of data
transfer and/or in an anonymization configuration file (e.g.,
anonymization configuration files 911-912), which can be configured
by a user or an administrator via an administration user interface
associated with the data gateway manager. Note that data gateway
management may also be performed with other data sources 922 (e.g.,
image storage systems, CD-ROMS, computer hard drives etc.) via the
respective data gateway manager 921 and anonymizer 920.
[0101] According to some embodiments, the data gateway managers
901-902 allow a user (e.g., clinic, hospital, private practice,
physician, insurer, etc.) to easily, and in some cases,
automatically, upload image data (and/or other data) to server 109
and stored in medical data store 206 in cloud 103. The data gateway
manager can be configured using a Web browser interface, where the
configuration may be stored as a set of rules 903-904 or the rules
can be determined at the time of data transfer by a user. Certain
internet ports such as ports 80 and 443 can be used for such data
transfers. Using these protocols allows the user (institution,
hospital etc.) to use ports and protocols that in most cases are
already set up for secure transfer of data without setting up a
separate VPN.
[0102] The Web interface allows a user to configure the file
transfer using a Web browser. This can be done each time image data
is transferred to the cloud, or can be set up to automatically
upload certain image data, cases or potions of cases based on
rules. Some examples of rules can be configured based on modality,
patient, dates, doctors, times, flags, clinical trial
qualifications, multiple criteria, etc. Generally, patient
identifying data needs to be removed from the image data before
they are transferred or during transfer. This is referred to as
`anonymization." This can be done in a number of ways and can also
be automated using rules such as based on birth date, upload date,
institution, etc.
[0103] The anonymization can be done in a number of ways including,
but not limited to, blanking or masking out characters in the DICOM
header, replacing characters in the DICOM header with
non-identifying characters, substitution, encryption,
transformation, etc. Depending on the anonymization methods,
de-anonymization, or partial de-anonymization, may be possible. For
example in a clinical trial, if a patient is experiencing
unacceptable side effects, it would be desirable to de-anonymize
their clinical trial data to determine whether the patient was
taking a placebo or a drug. The access control would be necessary
so that only those users with certain privileges would be allowed
to de-anonymize the data.
[0104] FIGS. 10A and 10B are screenshots illustrating examples of
graphical user interfaces for configuring the gateway manager
according to certain embodiments of the invention. For example, the
GUIs as shown in FIGS. 10A and 10B may be presented by client
applications 211-212 of FIG. 2, which may be a client application
or Web browser interface. The GUI for the gateway manager may also
be an application separate from client applications 211-212.
Referring to FIG. 10A, in one embodiment, GUI 1000 allows a user to
configure the data files to be transferred to/from a server such as
server 109 of FIG. 2. A user can add one or more files into the GUI
1000 via link 1006 or alternatively, by drag-and-dropping one or
more individual files and/or a folder of files into GUI 1000. Each
file to be transmitted to/from the server is associated with an
entry shown in GUI 1000. Each entry includes, but is not limited,
status field indicating a file transfer status, time field 1002
indicating the time when the file was added, progress indication
1003, source data path 1004 from which the file is retrieved, and
security indicator 1005 indicating whether the transfer is
conducted in a secure manner, etc.
[0105] A file can be manually selected, for example, by
drag-and-dropping the file into a specific folder. The file is then
anonymized, compressed, and/or encrypted, and uploaded to the
cloud, as shown in FIG. 12A. Alternatively, a router and/or gateway
manager can be configured to automatically upload to the cloud,
with optional anonymization, compression, and/or encryption, image
data captured by an imaging device and stored in a medical data
store, as shown in FIG. 12B. Furthermore, a router and/or gateway
manager can also be configured to automatically download from the
cloud, with optional anonymization, compression, and/or encryption,
image data and to store the image data in a medical data store, as
shown in FIG. 12C. The protocol used for transferring data may be a
DICOM transmission protocol or other appropriate protocol or
protocols. DICOM transmission protocol may use the following TCP
and UDP port numbers: Port 104 is a well-known port for DICOM over
TCP or UDP. Since port 104 is in the reserved subset, many
operating systems require special privileges to use it. Port 2761
is a registered port for DICOM using Integrated Secure
Communication Layer (ISCL) over TCP or UDP. Port 2762 is a
registered port for DICOM using Transport Layer Security (TLS) over
TCP or UDP. Port 11112 is a registered port for DICOM using
standard, open communication over TCP or UDP.
[0106] Once files associated with a study have been uploaded to the
cloud server, the user may be able to see a list which studies are
in that account (not shown). The list identifies the status of the
studies and/or files, including whether it has been downloaded
previously and/or whether it has been read or changed. The user has
the option of automatically or manually downloading any changed
studies which have not been previously downloaded, or choosing
which studies to download. The user also has the option of showing
only studies that have been changed and not downloaded in the
account or sort the list so that the non-downloaded studies are
listed at the top of the list. When files and/or studies are
specified for download, either automatically via link 1013 or
manually via link 1012, the files/studies can be downloaded to a
specific location in a local computer hard drive and/or the
files/studies can be downloaded to a non-local computer hard
drive.
[0107] According to one embodiment, a file whose transfer fails due
to an error can be resent via link 1007. An alert can also be sent
via email, displayed on a screen pop-up or added to a database for
listing. The software associated with GUI 1000 may be automatically
updated via link 1010 or manually updated via link 2011. The
destination or server to receive the files can also be configured
via link 1009. When link 1009 is activated, GUI 1050 is presented
as shown in FIG. 10B. Referring now to FIG. 10B, in GUI 1050, a
user can specify the username via field 1051 and password in field
1052 to access the server specified via field 1054. The user can
also specify a group or directory to which the file or files to be
transferred via field 1053. The user can also specify whether a
secured connection is needed via field or checkbox 1055 and a data
compression method is utilized via field or checkbox 1056.
Compression can be either lossless or lossy. Further, the user can
provide an email address via field 1057 and the email system via
field 1058 to allow the system to send an email to the user
regarding the data transfer. The user can also specify whether the
data will be anonymized via field or checkbox 1059, as well, as the
data logging detailed level via field 1060. Furthermore, a user can
specify by enabling checkbox 1061 that the data is to be stored in
an encrypted form. The user can also provide a key via fields
1062-1063 to a recipient to decrypt the data.
[0108] FIG. 11 is a screenshot illustrating examples of GUIs for
configuring anonymous data gateway management according to certain
embodiments of the invention. GUI 1100 can be presented by
activating link 1008 of FIG. 10A. Referring to FIG. 11, GUI 1100
allows a user to specify items to be anonymized, where each item is
specified in one of the entries listed in GUI 1100. Each item is
referenced by its DICOM tag 1101, name 1102, original value 1103 to
be replaced, and replacement value 1104 to replace the
corresponding original value. In this example, a user can set the
new value by clicking column 1104 and enter the new value. If the
DICOM tag is a date type, a date selector will be displayed to
allow the user to select the date. If the values allowed are
predefined, a drop down list is displayed for selecting one of the
predefined values or strings. If there is a mask defined for the
tag, a masked edit GUI is displayed to allow the user to change the
value according to the displayed mask. The user input maybe
examined by the system based on the type of the DICOM tag. If the
information is incorrect, the user may be prompted to reenter the
correct value. After all user inputs are collected, a new anonymous
template or configuration file is created and stored. Note that the
GUIs as shown in FIGS. 10A-10B and 11 can be presented and operated
via a variety of user interactions such as keystrokes, clicking,
touching, voice interactive commands, or a combination thereof.
Also note that the formats or configurations of the GUIs in FIGS.
10A-10B and 11 are described for the purpose of illustration only;
other formats or layouts may also be utilized. The GUI may be in
the form of a browser or a phone or other mobile device
application.
[0109] FIG. 13 is a flow diagram illustrating a method for
anonymizing medical data according to another embodiment of the
invention. Method 1300 may be performed by any of data gateway
managers 901-902, 921 of FIG. 9. Referring to FIG. 13, at block
1301, a local device (e.g., gateway manager/router/computer)
receives a 3D medical image data captured by a medical imaging
device. At block 1302, the 3D medical image data is anonymized
including removing certain metadata associated with the 3D medical
image data based on an anonymization template. At block 1303, the
anonymized 3D medical image data is then automatically uploaded to
a cloud server, using a network connection established via an
internet port of the local device.
[0110] Applications of Cloud-Based Services
[0111] The embodiments described above can be applied to a variety
of medical areas. For example, the techniques described above can
be applied to vessel analysis (including Endovascular Aortic Repair
(EVAR) and electrophysiology (EP) planning). Such vessel analysis
is performed for interpretation of both coronary and general vessel
analysis such as carotid and renal arteries, in addition to aortic
endograft and electro-physiology planning. Tools provided as cloud
services include auto-centerline extraction, straightened view,
diameter and length measurements, Curved Planar Reformation (CPR)
and axial renderings, as well as charting of the vessel diameter
vs. distance and cross-sectional views. The vessel track tool
provides a Maximum Intensity Projection (MIP) view in two
orthogonal planes that travels along and rotates about the vessel
centerline for ease of navigation and deep interrogation. Plaque
analysis tools provide detailed delineation of non luminal
structure such as soft plaque, calcified plaque and intra-mural
lesions.
[0112] In addition, the techniques described above can be utilized
in the area of endovascular aortic repair. According to some
embodiments, vascular analysis tools provided as cloud services
support definition of report templates which captures measurements
for endograft sizing. Multiple centerlines can be extracted to
allow for planning of EVAR procedures with multiple access points.
Diameters perpendicular to the vessel may be measured along with
distances along the two aorto-iliac paths. Custom workflow
templates may be used to enable the major aortic endograft
manufactures' measurement specifications to be made as required for
stent sizing. Sac segmentation and volume determination with a
"clock-face" overlay to aid with documenting the orientation and
location of branch vessels for fenestrated and branch device
planning, may also be used. Reports containing required
measurements and data may be generated.
[0113] The techniques described above can also be applied in the
left atrium analysis mode, in which semi-automated left atrium
segmentation of each pulmonary vein ostium is supported with a
single-click distance pair tool, provided as cloud services, for
assessment of the major and minor vein diameter. Measurements are
automatically detected and captured into the integrated reporting
system. These capabilities can be combined with other vessel
analysis tools to provide a comprehensive and customized EP
planning workflow for ablation and lead approach planning.
[0114] The techniques described above can also be utilized in
calcium scoring. Semi-automated identification of coronary calcium
is supported with Agatston, volume and mineral mass algorithms
being totaled and reported on-screen. Results may be stored in an
open-format database along with various other data relating to the
patient and their cardiovascular history and risk factors. A
customized report can be automatically generated, as part of cloud
services, based upon these data. Also includes report generation as
defined by the Society of Cardiovascular Computed Tomography (SCCT)
guidelines.
[0115] The techniques described above can also be utilized in a
time-volume analysis (TVA), which may include fully-automated
calculation of left ventricular volume, ejection fraction,
myocardial volume (mass) and wall thickening from multi-phasic
data. A fast and efficient workflow provided as part of cloud
services allows for easy verification or adjustment of levels and
contours. The results are presented within the integrated reporting
function.
[0116] The techniques described above can also be utilized in the
area of segmentation analysis and tracking (SAT), which includes
supports analysis and characterization of masses and structures in
various scans, including pulmonary CT examinations. Features
include single-click segmentation of masses, manual editing tools
to resolve segmentation issues, automatic reporting of dimensions
and volume, graphical 3D display of selected regions, integrated
automated reporting tool, support for follow-up comparisons
including percent volume change and doubling time, and support for
review of sphericity filter results.
[0117] The techniques described above can also be utilized in the
area of flythrough which may include features of automatic
segmentation and centerline extraction of the colon, with editing
tools available to redefine these centerlines if necessary. 2D
review includes side-by-side synchronized supine and prone data
sets in either axial, coronal or sagittal views with representative
synchronized endoluminal views. 3D review includes axial, coronal
and sagittal MPR or MIP image display with large endoluminal view
and an unfolded view that displays the entire colon. Coverage
tracking is supported to ensure 100% coverage with stepwise review
of unviewed sections, one-click polyp identification, bookmark and
merge findings, as well as a cube view for isolating a volume of
interest and an integrated contextual reporting tool. Support is
provided for use of sphericity filter results.
[0118] The techniques described above can also be utilized in the
area of time-dependent analysis (TDA), which provides assessment
tools for analyzing the time-dependent behavior of appropriate
computerized tomographic angiography (CTA) and/or MRI examinations,
such as within cerebral perfusion studies. Features include support
for loading multiple time-dependent series at the same time, and a
procedural workflow for selecting input and output function and
regions of interest. An integrated reporting tool is provided as
well as the ability to export the blood flow, blood volume and
transit time maps to DICOM. The tools may also be used with
time-dependent MR acquisitions to calculate various time-dependent
parameters.
[0119] The techniques described above can also be utilized in the
area of CTA-CT subtraction, which includes automatic registration
of pre- and post-contrast images, followed by subtraction or
dense-voxel masking technique which removes high-intensity
structures (like bone and surgical clips) from the CTA scan without
increasing noise, and leaving contrast-enhanced vascular structures
intact.
[0120] The techniques described above can also be utilized in
dental analysis, which provides a CPR tool which can be applied for
review of dental CT scans, offering the ability to generate
"panoramic" projections in various planes and of various
thicknesses, and cross-sectional MPR views at set increments along
the defined curve plane.
[0121] The techniques described above can also be utilized in the
area of multi-phase MR (basic, e.g. breast, prostate MR). Certain
MR examinations (for example, breast, prostate MR) involve a series
of image acquisitions taken over a period of time, where certain
structures become enhanced over time relative to other structures.
This module features the ability to subtract a pre-enhancement
image from all post-enhancement images to emphasize visualization
of enhancing structures (for example, vascular structures and other
enhancing tissue). Time-dependent region-of-interest tools are
provided to plot time-intensity graphs of a given region.
[0122] The techniques described above can also be utilized in
parametric mapping (e.g. for multi-phase Breast MR), in which the
parametric mapping module pre-calculates overlay maps where each
pixel in an image is color-coded depending on the time-dependent
behavior of the pixel intensity. The techniques described above can
also be utilized in the area of SphereFinder (e.g. sphericity
filter for lung and colon). SphereFinder pre-processes datasets as
soon as they are received and applies filters to detect sphere-like
structures. This is often used with lung or colon CT scans to
identify potential areas of interest. The techniques described can
also be utilized in fusion for CT/MR/PET/SPECT. Any two CT, PET, MR
or SPECT series, or any two-series combination can be overlaid with
one assigned a semi-transparent color coding and the other shown in
grayscale and volume rendering for anatomical reference. Automatic
registration is provided and subtraction to a temporary series or
to a saved, third series is possible.
[0123] The techniques described above can also be utilized in the
area of Lobular Decomposition. Lobular Decomposition is an analysis
and segmentation tool that is designed with anatomical structures
in mind. For any structure or organ region which is intertwined
with a tree-like structure (such as an arterial and/or venous
tree), the Lobular Decomposition tool allows the user to select the
volume of interest, as well as the trees related to it, and to
partition the volume into lobes or territories which are most
proximal to the tree or any specific sub-branch thereof. This
generic and flexible tool has potential research applications in
analysis of the liver, lung, heart and various other organs and
pathological structures.
[0124] The techniques described above can also be utilized in the
area of Volumetric Histogram. Volumetric Histogram supports
analysis of a given volume of interest based on partition of the
constituent voxels into populations of different intensity or
density ranges. This can be used, for example, to support research
into disease processes such as cancer (where it is desirable to
analyze the composition of tumors, in an attempt to understand the
balance between active tumor, necrotic tissue, and edema), or
emphysema (where the population of low-attenuation voxels in a lung
CT examination may be a meaningful indicator of early disease).
[0125] The techniques described above can also be utilized in the
area of Motion Analytics. Motion Analytics provides a powerful 2D
representation of a 4D process, for more effective communication of
findings when interactive 3D or 4D display is not available. Any
dynamic volume acquisition, such as a beating heart, can be
subjected to the Motion Analysis, to generate a color-coded "trail"
of outlines of key boundaries, throughout the dynamic sequence,
allowing a single 2D frame to capture and illustrate the motion, in
a manner that can be readily reported in literature. The uniformity
of the color pattern, or lack thereof, reflects the extent to which
motion is harmonic, providing immediate visual feedback from a
single image.
[0126] The techniques described above can also be utilized to
support other areas such as Multi-KV, enhanced multi-modality,
findings workflow, and iGENTLE available from TeraRecon. Multi-KV:
Support for Dual Energy and Spectral Imaging provides support for
established applications of dual energy or spectral imaging CT
data, such as removal of bone or contrast, as well as toolkits to
support research and investigation of new applications of such
imaging techniques. Enhanced multi-modality support is offered,
including support for PET/MR fusion, and improved applications for
MR such as time-intensity analysis and parametric mapping tools,
which may be applied in the study of perfusion characteristics of
normal or cancerous tissue.
[0127] Findings Workflow supports progressive analysis of serial
acquisitions, for the same patient. Each finding can be tracked
across multiple examinations, in a table that is maintained
indefinitely in the iNtuition system's database, without requiring
the prior scans to remain present on the system. Measurement data
and key images are captured and retained, allowing new scans to be
placed in context with prior results, and reports to be produced at
any time. Support for RECIST 1.1 is included although the tool may
readily be used for analysis of various progressive conditions, not
only those related to oncology. Export using the AIM (Annotation
and Image Markup) XML Schema is supported.
[0128] iGENTLE ensures that iNtuition's powerful suite of
segmentation, centerline, and metadata extraction tools continue to
work effectively, even with noisy scans characterized by low-dose
acquisitions. Metadata are extracted from enhanced copies of the
original scan, and then applied back onto the original, unmodified
data, to improve performance of 3D tools without denying access to
the original scan data.
[0129] Example of Data Processing System
[0130] FIG. 14 is a block diagram of a data processing system,
which may be used with one embodiment of the invention. For
example, the system 1400 may be used as part of a server or a
client as shown in FIG. 1. Note that while FIG. 14 illustrates
various components of a computer system, it is not intended to
represent any particular architecture or manner of interconnecting
the components; as such details are not germane to the present
invention. It will also be appreciated that network computers,
handheld computers, cell phones and other data processing systems
which have fewer components or perhaps more components may also be
used with the present invention.
[0131] As shown in FIG. 14, the computer system 1400, which is a
form of a data processing system, includes a bus or interconnect
1402 which is coupled to one or more microprocessors 1403 and a ROM
1407, a volatile RAM 1405, and a non-volatile memory 1406. The
microprocessor 1403 is coupled to cache memory 1404. The bus 1402
interconnects these various components together and also
interconnects these components 1403, 1407, 1405, and 1406 to a
display controller and display device 1408, as well as to
input/output (I/O) devices 1410, which may be mice, keyboards,
modems, network interfaces, printers, and other devices which are
well-known in the art.
[0132] Typically, the input/output devices 1410 are coupled to the
system through input/output controllers 1409. The volatile RAM 1405
is typically implemented as dynamic RAM (DRAM) which requires power
continuously in order to refresh or maintain the data in the
memory. The non-volatile memory 1406 is typically a magnetic hard
drive, a magnetic optical drive, an optical drive, or a DVD RAM or
other type of memory system which maintains data even after power
is removed from the system. Typically, the non-volatile memory will
also be a random access memory, although this is not required.
[0133] While FIG. 14 shows that the non-volatile memory is a local
device coupled directly to the rest of the components in the data
processing system, the present invention may utilize a non-volatile
memory which is remote from the system; such as, a network storage
device which is coupled to the data processing system through a
network interface such as a modem or Ethernet interface. The bus
1402 may include one or more buses connected to each other through
various bridges, controllers, and/or adapters, as is well-known in
the art. In one embodiment, the I/O controller 1409 includes a USB
(Universal Serial Bus) adapter for controlling USB peripherals.
Alternatively, I/O controller 1409 may include an IEEE-1394
adapter, also known as FireWire adapter, for controlling FireWire
devices.
[0134] Some portions of the preceding detailed descriptions have
been presented in terms of algorithms and symbolic representations
of operations on data bits within a computer memory. These
algorithmic descriptions and representations are the ways used by
those skilled in the data processing arts to most effectively
convey the substance of their work to others skilled in the art. An
algorithm is here, and generally, conceived to be a self-consistent
sequence of operations leading to a desired result. The operations
are those requiring physical manipulations of physical
quantities.
[0135] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the above discussion, it is appreciated that throughout the
description, discussions utilizing terms such as those set forth in
the claims below, refer to the action and processes of a computer
system, or similar electronic computing device, that manipulates
and transforms data represented as physical (electronic) quantities
within the computer system's registers and memories into other data
similarly represented as physical quantities within the computer
system memories or registers or other such information storage,
transmission or display devices.
[0136] The techniques shown in the figures can be implemented using
code and data stored and executed on one or more electronic
devices. Such electronic devices store and communicate (internally
and/or with other electronic devices over a network) code and data
using computer-readable media, such as non-transitory
computer-readable storage media (e.g., magnetic disks; optical
disks; random access memory; read only memory; flash memory
devices; phase-change memory) and transitory computer-readable
transmission media (e.g., electrical, optical, acoustical or other
form of propagated signals--such as carrier waves, infrared
signals, digital signals).
[0137] The processes or methods depicted in the preceding figures
may be performed by processing logic that comprises hardware (e.g.
circuitry, dedicated logic, etc.), firmware, software (e.g.,
embodied on a non-transitory computer readable medium), or a
combination of both. Although the processes or methods are
described above in terms of some sequential operations, it should
be appreciated that some of the operations described may be
performed in a different order. Moreover, some operations may be
performed in parallel rather than sequentially.
[0138] In the foregoing specification, embodiments of the invention
have been described with reference to specific exemplary
embodiments thereof. It will be evident that various modifications
may be made thereto without departing from the broader spirit and
scope of the invention as set forth in the following claims. The
specification and drawings are, accordingly, to be regarded in an
illustrative sense rather than a restrictive sense.
* * * * *