U.S. patent application number 11/529100 was filed with the patent office on 2007-01-25 for systems and graphical user interface for analyzing body images.
This patent application is currently assigned to Acculmage Diagnostics Corp. Invention is credited to David Goldhaber, Leon Kaufman, Mikhail Mineyev, Shelley Powers.
Application Number | 20070019849 11/529100 |
Document ID | / |
Family ID | 25425912 |
Filed Date | 2007-01-25 |
United States Patent
Application |
20070019849 |
Kind Code |
A1 |
Kaufman; Leon ; et
al. |
January 25, 2007 |
Systems and graphical user interface for analyzing body images
Abstract
Systems and graphical user interfaces for analyzing body images.
In an exemplary embodiment, the present invention provides a
graphical user interface having a display coupled to a
microprocessing device and a memory device. The graphical user
interface has an electronic representation of a first body image
and a second body image and an electronic map representing the
position of nodules on the first body image and second body
image.
Inventors: |
Kaufman; Leon; (South San
Francisco, CA) ; Mineyev; Mikhail; (San Francisco,
CA) ; Powers; Shelley; (Walnut Creek, CA) ;
Goldhaber; David; (San Mateo, CA) |
Correspondence
Address: |
TOWNSEND AND TOWNSEND AND CREW, LLP
TWO EMBARCADERO CENTER
EIGHTH FLOOR
SAN FRANCISCO
CA
94111-3834
US
|
Assignee: |
Acculmage Diagnostics Corp
South San Francisco
CA
|
Family ID: |
25425912 |
Appl. No.: |
11/529100 |
Filed: |
September 27, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
09908508 |
Jul 17, 2001 |
7130457 |
|
|
11529100 |
Sep 27, 2006 |
|
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Current U.S.
Class: |
382/128 |
Current CPC
Class: |
G16H 40/63 20180101;
G16H 30/20 20180101; G16H 15/00 20180101 |
Class at
Publication: |
382/128 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Claims
1. A graphical user interface device for comparing body images, the
interface comprising: a display coupled to a microprocessing device
and a memory device; an electronic representation of a first body
image taken at t.sub.1 along a first plane and a second body image
taken at t.sub.2 along the first plane, wherein the electronic
representations are stored in the memory device and displayed on
the display; and an electronic representation of at least one
composite image of the first body image and the second body image.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] The present application is a continuation application of and
claims priority to U.S. patent application Ser. No. 09/908,508,
filed Jul. 17, 2001, which is incorporated herein by reference. The
present application also is related to U.S. patent application Ser.
No. 09/908,432, filed Jul. 17, 2001, entitled "Methods and Systems
for Generating a Customized Lung Report" (Attorney Docket No.
021106-000320US) and U.S. patent application Ser. No. 09/908,466,
filed Jul. 17, 2001, now U.S. Pat. No. 6,901,277 and entitled
"Methods for Generating a Lung Report" (Attorney Docket No.
021106-000300US), each of which is hereby incorporated by
reference.
BACKGROUND OF THE INVENTION
[0002] The present invention relates generally to systems, methods,
software, and graphical user interfaces for displaying and
analyzing body images and for generating organ reports. More
particularly, the present invention relates to graphical user
interfaces and systems for analyzing one or more thoracic CT
datasets to track and analyze lung nodules and other lung
parameters.
[0003] Lung cancer is one of the most common forms of cancer among
both men and women. Advances in medical imaging, such as CT and MRI
scanning, have made it possible to localize and track early stage
nodules that were previously non-detectable. However, such scanning
protocols on a CT or MRI scanner typically generate no less than 40
images during a thoracic exam, while multi-slice protocols may
generate 300 or more axial images. In order to analyze the dataset
for lung nodules, the radiologist must review all of the slice
images to localize the lung nodules. If a nodule is found in one
slice image, the radiologist must then attempt to locate the nodule
in the adjacent slices.
[0004] Unfortunately, such large amounts of data for each patient
increases the probability that the radiologist will miss a
potential nodule in their analysis of the image dataset, i.e., a
"false negative." Tumors may be too small to be reliably detected,
or their appearance may be obscured by the surrounding tissues such
as vessels. Missed tumors may be detected months or years later in
a follow-up examination. During this interval the tumor may grow
larger and, in the worst case, metastasize.
[0005] In their early stages of development, malignant lung tumors
may not be detected even upon careful inspection of the image
dataset. The early detection of lung cancer is of particular
importance because the overall survival rate from the disease is
very low. It is generally believed that early detection of cancer
is beneficial, but in the case of lung cancer this is not
established because of the recentness of the technique. This patent
provides tools which will help to elucidate this question.
[0006] To improve detection of lung nodules manual and
semiautomatic pixel-based methods for segmenting CT images have
been developed. One such method is manually creating a region of
interest (ROI) delineating a nodule. A semiautomatic method
requires a single, operator-defined seed point in which a computer
algorithm will select similar contiguous gray-scale pixels that
surround the seed point as the potential nodule. In another case it
may be an operator-placed region of interest (such as a rectangle
or ellipse) around the nodule.
[0007] While the proposed imaging methods offer significant
potential to locate early stage nodules, still further improvements
are desirable. In particular, if a small nodule or nodules are
located in a first imaging scan, the radiologist will usually
recommend that the patient return for a second, follow-up imaging
scan. When the patient returns for the follow-up scan, the
radiologist must relocate the nodules in the image scan and analyze
the parameters of the nodules. Conventional imaging systems and
methods do not provide an efficient way to determine if the nodules
have increased in size, stayed the same, or the like.
[0008] Therefore, what is needed are reliable systems and user
interfaces which allow the radiologist to quickly and accurately
localize and track any changes in nodules found in an imaging scan.
Furthermore, what is needed is an improved method for visualization
and characterization of small malignant lung tumors on thoracic
image scan that would enable earlier detection of these tumors or
nodules so as to enable earlier detection.
BRIEF SUMMARY OF THE INVENTION
[0009] The present invention provides graphical user interfaces and
systems that allow an operator to localize and analyze lung nodules
in a baseline and follow up lung CT or MRI scan.
[0010] The graphical user interfaces of the present invention can
be displayed on a display that is coupled to a microprocessing
device and a memory device. The graphical user interface can
display an electronic representation of a first body image taken at
to along a first plane and a second body image taken at t.sub.2
along the first plane. At least one of the electronic
representations are stored in the memory device. The graphical user
interface also can display an electronic representation of at least
one composite image of the first body image and the second body
image.
[0011] The systems and graphical user interfaces of the present
invention can be used to localize lung nodules and to determine the
lung nodules relative position in the patient's lung, dimensions of
the lung nodule, and other morphological parameters of a baseline
image scan taken at t.sub.1. After the nodules are located in the
baseline scan, through manual, semiautomatic, or automatic
localization techniques, the nodule information derived from the
images can be analyzed by the operator to determine if the
potential nodule is a a benign or malignant nodule or merely a part
of the patient's vasculature.
[0012] The nodule information can be stored and later transferred
to a follow up image scan taken at t.sub.2 to ease the localization
of the previously localized nodules in the follow-up scans. The
stored nodule information provides a method that allows the
operator to quickly determine if the nodules localized in the
baseline scan have increased in size, the amount of increase, and
the like. Additionally, the present invention can also allow the
operator to quickly locate nodules--either manually or
automatically with an analysis algorithm--that were not located in
the baseline scan.
[0013] Typically, the graphical user interfaces of the present
invention can be used to analyze thoracic images to track potential
lung nodules over a time period. An operator can use the composite
image to track the potential nodules (if any) in the first body
image and the second body image. The composite image can provide
the user a visual indication of any change in surface area, pixel
area, or volume of the potential lung nodule.
[0014] In exemplary embodiments, the graphical user interfaces of
the present invention displays a first image along a body slice
taken at t.sub.1 and a follow-up image of the same body slice taken
at t.sub.2 on a user output device, such as a computer monitor.
Typically, the first and second images are displayed adjacent to
each other so as to allow the operator to visually assess changes
in the nodules.
[0015] Typically, each of the nodules can be localized on the image
with a map of markers to indicate its lesion number and its
relative position in the patient's lung. By choosing the marker,
the operator can view the statistics of the marked nodule which can
include, but is not limited to, nodule number, anatomic position,
roundness, volume, surface area, major and minor axes, CT density
or MRI signal intensity, density and signal standard deviation,
signal histogram, roundness criteria, and the like.
[0016] Optionally, to improve the visualization of the nodules, the
nodules in the baseline and follow-up images are colored
differently from the surrounding tissue. Typically, the nodules
displayed in the first image are displayed a different color than
the nodules in the second image. Thus, if the first image and
second image are superimposed over each other, the operator can
visually assess the change in size (if any) of the superimposed
nodules. Generally, the change in size of the nodule will be
displayed in a third, different color.
[0017] A third comparison or composite image can be displayed on
the user input device to illustrates changes (if any) between the
first and second images. In particular, the comparison image can be
used to illustrate the change in size of the nodules and any
development of new nodules. Additionally, in some methods, a
comparison chart can be displayed which quantitatively illustrates
any change in size, volume, etc., of the previously localized
nodules.
[0018] In another aspect, the present invention provides a system
for interpreting thoracic images. The system includes a storage
device for storing a first image scan and a second image scan, a
user interface for displaying information, and a processor. The
processor is programmed to access the storage device to display on
the user interface an image from the first image scan and an image
from the second image scan. The processor is also programmed to
compare a lesion from the image from the first image scan and the
lesion on the second image scan. The processor is also programmed
to display differences of the lesion in the first image scan and
the second image scan.
[0019] Comparing the lesion and displaying the differences of the
lesion can take a variety of forms. For example, in some
embodiments, the processor can provide a panel or chart which
quantitatively displays changes of various parameters of the lesion
(e.g., volume, surface area, diameter, number of pixels, or the
like). Alternatively, the displaying of differences can include
displaying the lesion in different colors so as to visually
indicate the change in lesions. In yet other embodiments, the
displaying of the changes can be shown via a composite image. The
composite image can show the nodules superimposed over each other
to visually indicate the change in size of the lesions. In the
embodiments where the lesions are shown in different colors, the
change in size of the lesions will generally be shown in a third,
different color.
[0020] It should be appreciated, however, that displaying the
differences can include other convention methods of illustrating
changes or it can include a combination of the above described
methods.
[0021] The systems of the present invention can further include an
imaging device for collecting the image datasets. The image
datasets can be coupled to a database or the systems of the present
invention for processing. Optionally, the systems of the present
invention can further include a printer for printing a body report,
such as a lung report.
[0022] In another aspect, the present invention provides a system
for displaying body images on a user output device. The system
includes means for displaying a first mage in a first image window
and means for displaying a second image in a second image window.
The system further includes means for displaying a third image in a
third window. The third image either a sum or a subtraction of the
first and second image.
[0023] Optionally, the system includes a panel for displaying image
information of the first, second, and third image. For systems that
are used to analyze lung images, the information can include
information on the number of lung nodules, the area, volume,
surface area, number of pixels, change in size, and HU information
of the lung nodules.
[0024] In a further aspect, the present invention provdes a
computer system for displaying two body images for comparison. The
method comprises displaying a first and second image. A marked map
of lesions are displayed in the first and second image to display
the relative position of the nodules. The marked map can be used to
illustrate the growth of new lesions, or the like. Optionally, a
third composite image can be displayed to illustrate a change in
size of any of the lesions.
[0025] A further understanding of the nature and advantages of the
invention will become apparent by reference to the remaining
portions of the specification and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] FIG. 1 schematically illustrates a simplified system of the
present invention;
[0027] FIG. 2 schematically illustrates a simplified networked
system of the present invention;
[0028] FIG. 3 schematically illustrates an exemplary computer
station which incorporates the software code and methods of the
present invention;
[0029] FIG. 4 schematically illustrates software modules
communication with a database of the present invention;
[0030] FIG. 5 illustrates a simplified method of generating a lung
report the present invention;
[0031] FIG. 6 illustrates a simplified method of generating a
comparison image;
[0032] FIG. 7 schematically illustrates a decision tree
incorporated into a report generation module of the present
invention;
[0033] FIG. 8 schematically illustrates an exemplary list of fields
in a decision tree of the present invention;
[0034] FIG. 9 illustrates an exemplary graphical user interface of
the present invention;
[0035] FIG. 10 illustrates an exemplary graphical user interface
having a plurality of windows for illustrating different planes of
patient's lung;
[0036] FIGS. 11A to 11C are simplified figures illustrating the
axial, sagittal, and coronal image planes;
[0037] FIG. 12 is an exemplary lung panel of the graphical user
interface of the present invention;
[0038] FIG. 13 is one sample lesion panel interface of the
graphical user interface of the present invention;
[0039] FIGS. 14-16 illustrate exemplary panels for other organs
that are displayed in a graphical user interface of the present
invention;
[0040] FIG. 17 is an exemplary graphical user interface for
comparing a baseline image with a follow-up image; and
[0041] FIGS. 18A to 18D are exemplary pages of a lung report that
can be generated by the methods and software of the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
[0042] The present invention provides systems, software code,
graphical user interfaces, and methods for displaying and analyzing
lung CT or MRI image datasets of a patient. The lung datasets can
be analyzed to map, track, and analyze the nodules in a series of
lung slice images or image scans, as well as record other lung and
chest abnormalities.
[0043] A lung slice image can be displayed on a user interface
display for analysis by a radiologist or other operator. The
methods of the present invention allows the radiologist to locate
and map out the tumors, nodules, or lesions (hereinafter referred
to as "nodules") that are both manually localized and/or
automatically localized by the software of the present invention.
The mapped nodules can be segmented and have its volume and other
dimensions ascertained. Such nodule information can then be
transferred onto a lung report, if desired.
[0044] Exemplary embodiments of the present invention may allow an
operator to compare a first, baseline scan taken at t.sub.1 with
one or more follow-up dataset scans taken at t.sub.2, t.sub.3, etc.
If an operator locates one or more nodules in the baseline scan,
the operator can use the methods and software of the present
invention to relocate the previously located nodules and to compare
the nodule parameters from the baseline scan with the nodule
parameters from the follow up scan(s). The operator can compare
changes in volume, changes in surface area, changes in other
morphological parameters of the nodules, and the like. The present
invention also can also be used to locate additional nodules that
appear in the follow up scan that were not assessed (or present) in
the baseline scan.
[0045] In order to compare the baseline image with the follow up
image, the imaging technical parameters (e.g., slice thickness,
beam collimation, kV, mAs, table incrementation, slice overlap,
reconstruction parameters) of the baseline dataset scan and the
follow-up dataset scan should substantially correspond.
[0046] If the imaging parameters of the two images are not
consistent, it may be difficult to align the baseline and follow up
images for comparison. Nevertheless, methodology exists for
correcting for some differences. For instance, images can be
rescaled to adjust for spatial resolution, slice thickness, slice
overlap and rotation. While changes in imaging parameters such as
kV in CT or timing parameters in MRI would impede comparison of
tissue characteristics, they would not affect size and volume
measurements. It should be appreciated however, that there will
inherently be some differences in the baseline and follow-up images
due to external factors such as the cardiac cycle or breathing
pattern of the patient. Some of these may be ameliorated by
retrospective or prospective gating techniques. A more complete
disclosure of Retrospective Gating can be found in co-pending
Provisional Patent Application Ser. No. ______, entitled
"Retrospective Gating," filed concurrently herewith (Attorney
Docket No. 021106-000400US), the complete disclosure of which is
incorporated herein by reference. Thus, the images will typically
not be exactly the same. Such deviations will generally not prevent
a comparison of the baseline and follow up images as long as they
are not so large as to affect the individual measurement itself in
a significant manner.
[0047] If the imaging parameters are substantially the same, the
images can be "aligned" to allow for comparison. Aligning can
include manually aligning the baseline and follow up image, or
automatically aligning the baseline and follow-up images using an
aligning algorithm.
[0048] The present invention further provides a lung report
generator for producing customizable body reports that can
incorporate information derived from the operator's analysis of the
image dataset(s). The lung report generator can analyze a single
image scan or can compare a plurality of body scans and produce a
customizable lung report. The lung report generator can use a
customizable decision tree to analyze the information from the
images and determine what information is displayed on the lung
report. Typically, the decision tree will generate information
related to the nodules, patient demographic information, medical
history, a comparison to population data, radiologist
recommendations, standards of practice of the community, or the
like. The customization aspect allows different imaging sites or
each physician to customize the lung report to meet their own
needs. This customization reflects community standards, personal
standing in areas where consensus is not universal, a different
knowledge base, and intellectual biases.
[0049] The present invention also provides graphical user
interfaces for display on a user interface output device, such as a
computer monitor, for displaying and analyzing the body image
dataset(s). The graphical user interfaces provides windows for
showing one or more image planes and inputs that allow the operator
localize and analyze lesions in the body image(s). For comparison
studies, the graphical user interface further allows the operator
to view and compare the images of a baseline scan with the images
in the follow-up scan so as to track and analyze potential lung
nodules. Such information will allow the operator to assess any
changes (visually and/or quantitatively) in the nodules over the
time period between the baseline scan and follow up scan.
[0050] The locations of the nodules on the graphical user interface
can be indicated with a marker, such as a number, symbol, shape,
color, or the like. The map of the lesions can then be
automatically transferred to a follow up image dataset to indicate
where the potential nodules are, or should be, located in the
follow up image dataset. In other embodiments, the potential
nodules can be automatically colored differently from the
surrounding tissue to allow the operator to more easily visualize
the nodules.
[0051] The graphical user interface generally allows the operator
to display the baseline image side-by-side with a follow up image
to allow the operator to visually assess the changes in the size or
shape of the nodules. In exemplary configurations, the graphical
user interface provides a third, comparison image that is a
combination of the baseline and follow up images. The combination
image can be a sum image, a subtraction image, or a superimposed
image of the baseline images. In one configuration, the lesions in
the baseline and follow up images can be displayed in different
colors so as to provide improved visual assessment of the change in
size and/or shape of the lesion. Thus, when the baseline and follow
up images are superimposed over each other, the corresponding
nodules having different colors provide a visual indication for the
purpose of matching nodules, and also indicates to the operator
changes in size of the nodule (if any) in the various image planes,
even though, the results of quantitative measures are more robust
for this latter purpose.
[0052] In many embodiments, a chart or panel can be displayed on
the graphical user interface to quantitatively compare the nodules
in the baseline and follow up images. The panel can provide
numerical indications of the change in volume, roundness, surface
area, HU, mean, standard deviation, density (e.g. HU in x-ray CT or
signal intensity in MRI), and the like. The chart will generally
indicate which nodule is attached to the numerical information
through the use of the marker which is superimposed over the
nodule(s).
[0053] While the remaining discussion will focus on analyzing lung
images and generating a lung report, it should be appreciated that
the present invention is not limited to analyzing lung images and
producing lung reports. For example, the present invention can also
be used to analyze other body organs such as for use in the colon,
the vascular tree, brain, Calcium Scoring, the whole body, or the
like.
[0054] Referring now to FIGS. 1-4, the systems 10 of the present
invention can take a variety of forms. As illustrated in FIG. 1,
the system 10 of the present invention includes an imaging device
12 (such as a helical or conventional CT scanner, MRI scanner,
X-ray unit, nuclear imaging unit, positron emission tomography
unit, ultrasound, or the like) that is in communication with a
database or memory 14. An operator can use a computer station 22
that has data processor(s) 16 and user interface(s) 18 for
accessing database 14 so as to process and view the image(s) and
image data.
[0055] In a particular embodiment, as illustrated in FIG. 1, the
system 10 of the present invention can be a stand-alone system and
all of the components of the system can be located at the same
imaging site. In other embodiments, as shown in FIG. 2, some or all
of the components of the system can be distributed throughout a
communication network 20. Some combination of the components of the
system 10 can be located in a single imaging facility while other
components of the system may be located remotely. For example, as
shown with the dotted lines in FIG. 2, the imaging device 12 can be
directly coupled to (or integral with) the computer station 22. In
such embodiments, the image data can be logged into a local memory
in the computer station (not shown) and/or sent via the
communication network 20 to the remote database 14. Alternatively,
images from a remote imaging device 12 can be sent to the computer
station 22 and/or the database 14 via the communication network 20
for analysis.
[0056] Communication network 20 may be comprised of many
interconnected computer systems and communication links.
Communication links may be hardwire links, optical links, satellite
or other wireless communication links, wave propagation links, or
any other mechanisms for communication of information. While in one
embodiment, communication network 20 is the Internet, in other
embodiments, communication network 20 may be any suitable computer
network, such as an intranet, a local area network (LAN), a
metropolitan area network (MAN), a wide area network (WAN), or the
like.
[0057] FIG. 3 is a simplified block diagram of an exemplary
computer station 22 of the present invention. Computer station 22
typically includes at least one processor 28 which communicates
with a number of peripheral devices via a bus subsystem 26. These
peripheral devices may include a storage subsystem 36, comprising a
memory subsystem 38 and a file storage subsystem 44, user interface
input devices 34, user interface output devices 32, and a network
interface subsystem 30. Network interface subsystem 30 provides an
interface to outside networks, including an interface to
communication network 20, and is coupled via communication network
20 to corresponding interface devices in other computer
systems.
[0058] User interface input devices 34 may include a keyboard,
pointing devices such as a mouse, trackball, touch pad, or graphics
tablet, a scanner, foot pedals, a joystick, a touchscreen
incorporated into the output device 32, audio input devices such as
voice recognition systems, microphones, and other types of input
devices. In general, use of the term "input device" is intended to
include a variety of conventional and proprietary devices and ways
to input information into computer system 24 or onto computer
network 20.
[0059] User interface output devices 32 may include a display
subsystem, a printer, a fax machine, or non-visual displays such as
audio output devices. The display subsystem may be a cathode ray
tube (CRT), a flat-panel device such as a liquid crystal display
(LCD), a projection device, or the like. The display subsystem may
also provide non-visual display such as via audio output devices.
In general, use of the term "output device" is intended to include
a variety of devices and ways to output information from computer
system 24 to an operator or to another machine or computer
system.
[0060] Storage subsystem 36 stores the basic programming and data
constructs that provide the functionality of the various
embodiments of the present invention. For example, database and
modules implementing the functionality of the present invention may
be stored in storage subsystem 36. These software modules are
generally executed by processor 28. In a distributed environment,
the software modules may be stored on a plurality of computer
systems and executed by processors of the plurality of computer
systems. Storage subsystem 36 typically comprises memory subsystem
38 and file storage subsystem 44.
[0061] Memory subsystem 38 typically includes a number of memories
including a main random access memory (RAM) 42 for storage of
instructions and data during program execution and a read only
memory (ROM) 40 in which fixed instructions are stored. File
storage subsystem 44 provides persistent (non-volatile) storage for
program and data files, and may include a hard disk drive, a floppy
disk drive along with associated removable media, a Compact Digital
Read Only Memory (CD-ROM) drive, an optical drive, or removable
media cartridges. One or more of the drives may be located at
remote locations on other connected computers at other sites
coupled to communication network 20. The databases and modules
implementing the functionality of the present invention may also be
stored by file storage subsystem 44.
[0062] Bus subsystem 26 provides a mechanism for letting the
various components and subsystems of computer system 22 communicate
with each other as intended. The various subsystems and components
of computer system 22 need not be at the same physical location but
may be distributed at various locations within distributed network
10. Although bus subsystem 26 is shown schematically as a single
bus, alternate embodiments of the bus subsystem may utilize
multiple busses.
[0063] Computer system 22 itself can be of varying types including
a personal computer, a portable computer, a workstation, a computer
terminal, a network computer, a module in the imaging unit, a
mainframe, or any other data processing system. Due to the
ever-changing nature of computers and networks, the description of
computer system 24 depicted in FIG. 2 is intended only as a
specific example for purposes of illustrating the preferred
embodiment of the present invention. Many other configurations of
computer system 24 are possible having more or less components than
the computer system depicted in FIG. 3.
[0064] FIG. 4 is a simplified block diagram depicting the various
software modules executing on a computer system 22 of the present
invention. Software of the present invention typically includes an
analysis module 46 for analyzing an localizing nodules, comparison
module 48 for comparing a baseline and follow up image dataset, and
a report generator module 50 for generating a lung report.
[0065] As shown in FIG. 5, images of the target portion (e.g.,
lungs) of the patient can be acquired with an imaging device 12,
such as a conventional or helical CT scanner, MRI scanner, or the
like. (Step 52). It should be appreciated however, that various
other types of images and imaging systems can be used to obtain the
patient images, such as nuclear imaging, ultrasound, x-ray, digital
x-ray, PET, and the like. The images of the target portion will
typically be digitized and stored in a database 14 remote from the
imaging device and/or within a local memory or database 36 in the
computer system 22. The imaging parameters of the scan should also
be logged into the database so as to allow for a comparison with a
follow up scan that has similar imaging parameters.
[0066] When an operator desires to analyze the image data and/or
produce a lung report, the operator will activate the software
using conventional methods, such as clicking on an icon on the
desktop with a cursor that is controlled by a mouse or other user
input device 34 or voice recognition devices. The operator can
request the patient image data via user interface 34 and the
analysis module 46 can retrieve the requested information from
database 14 and display the selected image(s) on user interface
display 32. Generally, the image data will be displayed on a window
102 in graphical user interface 100. (FIG. 9). In some embodiments,
the lung image will be shown as a single axial view. In other
embodiments, three viewing planes (e.g., axial, sagittal, and
coronal) of the image will be shown. In such embodiments, the
coronal and sagittal view will have arbitrary locations, until the
operator chooses, e.g., clicks on a point on one of the three views
to set the point in which the image planes will correspond to. It
should be appreciated, however, that it may be possible to display
an oblique and/or reformatted image plane, if desired.
[0067] To improve the analysis of nodules in the lung image, the
image data can then optionally be edited, either automatically by
the analysis module 46 or manually by the operator, to set the
thresholds of the image to separate vascular, interstitial, and
emphasemic tissue from the image. (Step 54). Based on known tissue
attenuation ranges, different tissue types (e.g., bone, tumors,
etc.) can be identified by pixels within a specified gray level
range. Setting the thresholds can be set by interactively moving
corresponding bars on a histogram 104 to detect and filter out
specific gray scale pixel intensity levels or using a Histogram
Preset Button to remove the non-target tissue from the image(s).
Typically, such Preset Buttons are configurable to allow the
operator to customize the ranges of lung attenuation (HU) that is
filtered out. It should be appreciated that gray level thresholding
is merely one method of segmenting the lung image dataset and that
other methods can be incorporated into the teachings of the present
invention. For example, other methods of filtering out known tissue
structures in the displayed image include the use of automated edge
tracking algorithms, knowledge-based methods, and patient specific
models, among others. Such methods are further described in Brown
et al., "Method for Segmenting Chest CT Image Data Using an
Anatomical Model: Preliminary Results," IEEE Transaction on Medical
Imaging, Vol. 16, No. 6, December 1997, and Brown et al.,
"Patient-specific models for lung nodule detection and surveillance
in CT images," SPIE Medical Imaging 2001, the complete disclosure
of which is incorporated herein by reference.
[0068] Once the thresholding of the images is completed, the
nodules in the image dataset can be selected for analysis. Such
selection can be accomplished manually or automatically via a
selection algorithm in the analysis module 46. One pixel-based
method for selecting the lesion comprises manually tracing an
outline of the potential nodule by manually tracing a region of
interest (ROI) around each of the potential nodules by moving a
mouse, joystick, or the like. Such tracing can require that the
radiologist perform the same tracing function for each of the
nodules in a series of the slice images. Such selection methods
will eventually allow the computer software to log the anatomic
position of the nodule, calculate the volume, surface area, and
other morphological parameters of the potential nodule. In some
semi-automated methods, the operator can select a potential nodule
on one slice and analysis module 46 can automatically select the
corresponding nodules either on the basis of spatial overlap or
proximity or of gray-scaling in adjacent slice images. In other
fully automated methods, the analysis module can use a selection
algorithm to automatically select and map out the potential nodules
in each of the slices. A further description of methods of
selecting nodules can be found in Brown M. S. et al.
"Knowledge-based segmentation of thoracic computed tomography
images for assessment of split lung function," Med. Phys. 27 (3)
pp. 592-598 March 2000, Brown, M. S. et al. "Model-Based
Segmentation Architecture for Lung Nodule Detection in CT"
Radiology 217(P):207, 2000 (Abstract), Zhao, B. et al.
"Two-dimensional multi-criterion segmentation of pulmonary nodules
on helical CT images," Med. Phys. 26 (6) pp. 889-895 June 1999, the
complete disclosures of which are herein incorporated by reference.
After the potential nodules have been selected, the operator can
review the potential nodules selected. The operator will then have
the option to accept or reject nodules that the operator does not
believe is a nodule. Moreover, the operator further has the option
to select other potential nodules that were not automatically
chosen by analysis module 46.
[0069] Since nodules will often have the same signal intensity as
blood vessels, chest wall or the like, problems in delineating the
nodule may occur when the nodule or lesion is directly adjacent
blood vessels, chest wall, or the like. In such cases analysis
module 46 may have difficulty picking out the nodule from the
adjacent body organs. Thus, if a non-selected image element appears
to be a nodule that was not automatically selected by the analysis
module 46, the operator can manually select a region of interest
(ROI) around the suspected nodule in one or more of the image
slices. The analysis module can then perform its analysis of the
suspected nodule and provide roundness information, volume
information, or the like. Likewise, if a selected nodule appears to
be tubular in shape, such data would tell the operator that the
nodule is likely part of the patient's vasculature, and not a
nodule. If desired, the operator may then de-select the suspected
nodule from further tracking for follow up body scans.
[0070] Once the nodules are selected for analysis, a grow algorithm
in the analysis module 46 can calculate the parameter for each of
the selected nodules (Step 56) and the statistics for each of the
nodules will be logged as a file into database 14 (Step 58). The
algorithm can calculate 3D data for the selected lesion such as
volume, major and minor dimensions, density, roundness parameter,
gray-level features such as mean and standard deviation, maximum
and minimum signal intensity, and the like. The data derived from
the image can include histogram statistics for each of the slices
separately and for the entire slab/volume of the images. Analysis
module 46 of the present invention can allow the user to move
between the images slices to allow the operator to determine if the
nodule has been properly selected in each of the slices.
[0071] In addition to calculating the nodule characteristics, the
analysis module 46 can also assign each potential nodule a number
or marker. Selecting the marker, typically by clicking on the
marker will display the nodule statistics in a panel or chart on
the graphical user interface. Such a panel allows the operator to
"de-select" a localized nodule if it is determined that the
selected element is not a lesion or nodule. The panel displayed on
the graphical user interface will typically be only for the slice
that is shown in the display 34. In order to view the data for all
of the slices, the operator can scroll through all of the slices.
If desired, the analysis module can be customizable to display a 3D
rendering of the entire volume and the statistics for the entire
volume.
[0072] If desired, in addition to statistics for lung nodules, the
operator can also generate statistics for the lung and other body
organs imaged during the scan. For example, it may be desirable to
generate statistics for the patient's heart, mediastinum, heart,
aorta, chest walls, or the like. The statistics can include data on
volume, calcium scoring, surface area, density, or other visual
impressions from the operator.
[0073] Based on the operator's analysis of the statistics produced
by analysis module 46, the operator can enter a diagnosis or
recommendation (Step 60). Such information can be entered into the
database 14 via user input devices 34 such as a keyboard and mouse.
In some embodiments, a report generator module 50 can automatically
provide recommendations based on the parameters of the nodules in
the images and on patient demographic and medical history
information. For example, if a large number of nodules are found,
the report generator module 50 can automatically recommend that the
patient return in three months for a follow up scan.
[0074] If the image dataset is a first scan for the patient, the
operator will have the option to create a lung report (Step 62).
The report generation module 50 can produce a lung report that
shows statistics of the lung images, some lung image
cross-sections, recommendations, and the like. A sample lung report
is provided in FIGS. 18A to 18D. It should be appreciated that the
lung reports of the present invention can take a variety of forms.
For example, the lung report can be printed on a printer, sent
electronically over the communication network to a patient's
physician or patient, displayed electronically on output display,
saved on a computer readable medium, or the like.
[0075] If the image dataset is a second, follow-up scan for the
patient, the operator will have the option to compare the follow up
image dataset with a baseline image dataset of the patient. Using
the data logged from the baseline image scan as a starting "map" of
the potential nodules in the follow up scan, compare module 48 can
automatically relocate the nodules in the follow up images.
Automatically localizing the previously assessed nodules in the
follow up scan, allows the operator to quickly determine if the
previously located nodule(s) are growing or static. FIG. 6
schematically illustrates a simplified method of the present
invention for comparing the baseline image dataset with the
follow-up image dataset. In general, the first image dataset and
second image dataset are acquired at different times and are saved
into database 14. (Steps 64, 66). Each of the datasets can then be
downloaded into the software of the present invention. Typically,
the baseline image can be displayed on the graphical user interface
with the corresponding follow up image. An exemplary graphical user
interface 100 with comparison functionality is illustrated in FIG.
9. The image(s) from the first and second dataset are substantially
aligned or registered along three axis to allow for comparison
(Steps 68, 70). Typically, alignment is carried out by making at
least one of the images movable to allow the operator to manually
move one of the images so as to align the two images with each
other. In exemplary methods differences at image acquisition in
spatial parameters are automatically corrected using known methods
prior to panning the images.
[0076] It should be appreciated that in other methods of the
present invention, instead of manually aligning the first and
second image, the compare module 48 may automatically align the
first and second images by matching the major axis of the lung
using least square fits or other methodology. It is to be noted
that this alignment need not be perfect, since matching criteria
can include overlap of any one pixel in each nodule, or even
matching by overlap of a "halo" region around each nodule.
[0077] If the two images aren't registered, the operator will not
be able to properly compare the baseline images with the follow up
images. In order to compare the baseline image with the follow up
image, the technical imaging parameters of the two images should
substantially correspond or be adjusted for such variations. In
order to compare the baseline image with the follow up image, the
imaging technical parameters (e.g., slice thickness, beam
collimation, kV, mAs, Table incrementation, slice overlap,
reconstruction parameters) for acquiring the images in the baseline
dataset scan and the follow-up image dataset scan should
substantially correspond. Nevertheless, methodology exists for
correcting for some differences. For instance, images can be
rescaled to adjust for spatial resolution, slice thickness, slice
overlap and rotation.
[0078] One aspect of registering the images is aligning the lung
nodules in the two images. One method of matching lung nodules in
the two image is implementing software code that requires that at
least one voxel in the nodules overlap. Another method of matching
nodules is requiring that the edges of the lesions be no more than
a fixed number of pixels apart. In exemplary embodiments, the
software can require that the edges of the nodules (or other
lesion) be no more than one pixel apart, two pixels apart, or three
pixels apart. It should be appreciated however, that there are a
variety of other methods for registering the images and nodules in
the two images.
[0079] While changes in imaging parameters such as kV in CT or
timing parameters in MRI would impede comparison of tissue
characteristics, they would not affect size and volume
measurements. It should be appreciated however, that there will
inherently be some differences in the baseline and follow-up images
due to external factors such as the cardiac cycle or breathing
pattern of the patient. Some of these may be ameliorated by
retrospective or prospective gating techniques, but, the images
will typically not be exactly the same, and matching criteria will
be such as to allow some resiliency in the process.
[0080] In exemplary embodiments, comparison module 48 illustrates
to the operator any changes that may have occurred between the
baseline scan and follow up scan. In some embodiments, illustration
of the changes in the image datasets includes displaying a
comparison image on the user interface display 34 (Step 72). The
comparison image will typically be a composite image that can be a
subtraction of the first and second image--i.e., all common image
characteristics between the first and second image will not be
shown and only the differences will be shown. Alternatively, the
comparison image can be a sum of the first and second image which
shows overlap as brighter than the non-overlapped areas of a
nodule. If displayed in color, the baseline scan may mark the
nodules in one color (e.g. blue), the follow up scan in another
(e.g., yellow) and the overlapped region show in a third color
(e.g., green). Alternatively, or in addition to the comparison
image, comparison module 48 can provide a statistical chart that
shows the changes to the nodules found in the first image dataset.
In addition to comparing the nodules in the first and second image
dataset, the software of the present invention can also
automatically analyze the image datasets to determine if new
nodules have formed in the lung tissue since the first image
dataset.
[0081] After the nodules have been localized and/or compared, the
operator can generate a lung report. The lung reports can include
data comprising the patient's history, the image data, comparison
data to national statistics, radiologist recommendations, prognosis
for the number, size and location of nodules found in the scan, or
the like. Typically, the software of the present invention can be
configured to allow each of the lung reports to be customized. The
lung reports can be customizable for each patient, if desired.
Alternatively, each of the imaging facilities (or each of the
radiologists) can configure the software to produce customized lung
reports that are tailored to their needs.
[0082] FIG. 7 illustrates a decision tree of the present invention
that is incorporated into the report generator module 48.
Typically, the report generator module obtains the image analysis
data and recommendations that are stored in database 14 regarding
the patient's image(s). The report generator module will generate a
report that is generated from a programmable decision tree.
[0083] Typically, the operator will be given a choice to choose
images for a report. Three dimensional images can be selected prior
to generating a report. In addition to displaying the selected
images from the scan, if desired, the statistical data can be
compared to population data. The other patient data can be derived
from database 14 that stores the national percentile data. The
patient's position relative to the national percentile can be
established with a marker within a chart, as illustrated in FIG.
18C. Criteria for comparison can include smoking history, family
history, gender, age, occupational history, and other factors that
may be known at the time as influencing the risk factors of a
particular individual or his/her prognosis.
[0084] There are several aspects to report customization. The first
customizable aspect of the body report is the report layout. The
layout affects the look and feel of the report. Fields can be laid
out in particular locations. To accomplish the report layouts, a
list of fields will be created. The list can be tagged with a
position marker and the marker can direct the field to a particular
place on a page of the report. In this way, the location, number,
and order of the field can be chosen by the physician. The library
of choices for report elements can be quite large and can include
both graphic and text elements. Graphic elements can be fixed (e.g.
a histogram of population statistics) or particular to the patient
(e.g. an annotated image from the patient set.) Similarly, the text
elements can be fixed (e.g. site name and address, or an
explanation of the test) or related to the specific results of the
study.
[0085] Another aspect of the customization is a decision tree or
set of logic steps which can connect the fixed information results
from the tests (e.g., demographic data) and patient history data.
The program code can provide an interface comprising four sections
on the computer screen display. In exemplary embodiments, one
section can be a list of all the output fields (e.g.,
recommendations such as "urgent to visit a physician", "need to
return in six months", "results are normal for subject's age,
gender and medical factors"). The second section can be a list of
all the input fields (e.g., subject age, gender, occupational
status, number of nodules, size of nodules, and so on). If the
lists are longer than can be displayed at one time on the output
display, a scroll bar can be used to cycle through the list. The
operator can choose by a click an alphabetical or thematic ordering
of each list. The third section contains buttons for all needed
logic and arithmetic operations (e.g., AND, OR, NOT, GREATER THAN,
LESS THAN, EQUAL TO, THEN, NEXT OUTPUT FIELD, FINISH OUTPUT FIELD,
SUM, MINUS, DIVIDE, MULTIPLY, etc.) The fourth section contains a
graphical representation of the logic tree as it is generated.
[0086] In exemplary embodiments, the operator can generate the
logic or decision tree without any typing. For example, an output
field can first be created by clicking or otherwise selecting the
first section. Then, for that output field, a decision tree will be
created by clicks on the input fields interspersed with clicks on
the logic or arithmetic operators. In essence, the report can be
constructed by stringing together various input elements like
pearls in a necklace. Editing code can allow the operator to edit
the string of fields.
[0087] Another customization possibility is the incorporation of
information gathered from the aggregation and statistical analysis
of previous patient results and outcomes. These previous patients
can be those at the client imaging center, as well as those from
other imaging centers with which the client center is sharing
results data.
[0088] Further customization can occur through incorporation of the
decision tree to analyze all of the statistical data collected, so
as to give an automated recommendation to the patient. The
automated recommendations can be personalized by each radiologist
or imaging facility. For example, some radiologists may choose to
incorporate data regarding age, race, gender, or the like, while
other radiologists may choose to not choose to incorporate such
information into his recommendations. FIG. 8 shows sample criteria
that may be part of the decision tree:
[0089] 1. Has the never smoked, moderate smoker, heavy smoker?
[0090] 2. How old is the patient? below 30, below 50, below 70?
[0091] 3. Is the patient male or female?
[0092] 4. What is the patient's race?
[0093] 5. Does the patient's family have a history of lung
cancer?
[0094] 6. How many nodules does the patient have? less than 5 or
more than 5?
[0095] 7. Are any of the nodules over 1 cm in major axis? 3 cm in
axis?
[0096] 8. Has more than one study been performed? If so, have any
of the nodules shown an increase in size?
[0097] Based on a combination of such questions or other sets of
questions, the report generator module 50 will generate a different
recommendation. Some hypothetical examples are as follows:
[0098] If the subject:
[0099] 1. never smoked.
[0100] 2. age 70.
[0101] 3. male.
[0102] 4. has 5 nodules.
[0103] 5. no nodules over 1 cm in major axis.
[0104] 6. recommend follow up in 3 years.
[0105] If the subject
[0106] 1. heavy smoker.
[0107] 2. age 50.
[0108] 3. male.
[0109] 4. has 5 modules.
[0110] 5. one nodule over 3 cm in major axis.
[0111] 6. recommend urgent follow up with primary physician.
[0112] If the subject:
[0113] 1. never smoked.
[0114] 2. age 70.
[0115] 3. male.
[0116] 4. has 5 nodules.
[0117] 5. no nodule over 1 cm in major axis.
[0118] 6. second study shows significant size increase in at least
one nodule.
[0119] 7. recommend urgent follow-up with primary physician.
[0120] Typically, the operator will be given the choice to choose
between a regular report and a comparison report. If the report
generated is a comparison report, the operator must select the
first and second datasets that are to be compared. After the
dataset is selected, the operator (or decision tree) can choose
which information can be incorporated into the lung report.
Typically, the information will include statistics for the nodules
assessed during analysis, physician data inputs, such as
recommendations, or other comments, findings regarding the
surrounding organs such as the lungs, mediastinum, heart, thoracic
aorta, and chest wall, patient data, such as age, gender, medical
history, race, family medical history, smoking history,
occupational history, asthma, allergies and other factors that may
be known at the time as influencing the risk factors of a
particular individual or his/her prognosis, or the like can be
incorporated into the report. In some embodiments, the report
generator module 48 can use the patient data to tailor any
recommendations and conclusions. For example, if a patient has a
family history or lung cancer, report generator module 48 can be
customized to automatically recommend a follow up scan at a later
time, even if no nodules were localized during the first scan.
[0121] As shown in FIGS. 9-16, the present invention further
provides software code for generating a graphical user interface
100 on a user interface display 34. The graphical user interface
100 can display an image window 102, histograms 104, data graphs or
charts 106, and a toolbar 108 for displaying and analyzing the
images. For ease of reference, specific names will be used for the
various icons and buttons that are present in the graphical user
interface toolbar and such names should not be construed to be
limiting to the concepts of the present invention
[0122] One exemplary graphical user interface 100 is illustrated in
FIG. 9. In a particular embodiment, toolbar 108 is positioned along
a top portion of the display and window 102 is positioned in a
lower right portion of the screen. The body image slice illustrated
is an axial CT image of the patient's lung. At the bottom of the
graphical user interface are icons MPR1, MPR2, and MPR3 icons 110,
112, 114 that allow the user to toggle between different image
planes (i.e. axial, coronal, and sagittal).
[0123] Graphical user interface toolbar 108 of the present
invention will typically have a plurality of buttons or icons that
upon activation can manipulate the image displayed in window 102.
The icons can include a combination of, but is not limited to, a
crossbar button 116, an overlay color button 118, a Draw Region of
Interest (ROI) Button 120, a Block selection 122, an Auto Contour
Button 124, a Select Lesion 126, an Edit Lesion 128, a 3D button
130, a Frames button 132, Edit Text 134, a Draw Line button 136, a
Select Layout button 138, and a Report button 140.
[0124] As illustrated in FIG. 9, upon selection of the crossbar
button 116, a crossbar 142 will appear on image window 102 that can
be positioned by the operator through actuation of a user input
device 34 to restrict the area of analysis within window 102.
Crossbar 142 can also be used to reference a point in all three
planes of the body image. In use, the operator can move a cursor
144 within window 102 and click and hold a button on mouse 34 to
drag the crossbar to a desired position in window 102. Upon moving
to the desired position, the operator can release the button and
the crossbar will be maintained in the desired position. If the
operator switches to a multiple plane view (FIG. 10), crossbar 142
can act as a the reference point in the three image planes.
[0125] The operator can select the buttons on toolbar 108 to
perform various tasks with the image. To begin obtaining data for
the nodules in the lung images, the operator can activate the Draw
ROI button 120 or the Auto Contour Button 124. Selecting the Draw
ROI button 120 will allow the operator to manually draw a region of
interest around the potential nodules. If the operator wishes to
deselect a ROI, the operator can activate the Block Selection
Button 122 to delete the previously selected potential lesion.
Activation of Auto Contour Button 124 will activate analysis module
46 so as to automatically draw a ROI around potential nodules that
are displayed within the slice displayed in window 102.
[0126] After the lesions have been selected in the displayed slice,
the operator can repeat the selection process in the remaining
slices, until all of the nodules have been selected. It should be
appreciated however, that in some embodiments of the software of
the present invention, the software may automatically select the
nodules in the adjacent image slices or all of the image slices.
After the lesions have been selected, the operator may optionally
activate the Overlay Color button 118 so that the selected nodules
will be displayed in a different color to differentiate the
selected nodules from the surrounding tissue. Overlay color button
activates analysis module 46 to automatically highlight the
selected nodules in the window by changing the colors or intensity
of the nodules or other selected tissue. Determination of which
color to display the image elements can be based on the gray-scale
intensity of the selected elements.
[0127] In order to display statistics for an individual nodule, the
operator can activate the Select Lesion button 126 and move the
cursor over the desired nodule. By clicking on the desired nodule,
chart 106 will display various statistics of the selected nodule.
Some statistics that can be displayed are the anatomic position,
number of pixels, area, volume, Value (HU), including minimum,
maximum, median, mean, total sum, roundness, standard deviation,
diameter 1, and diameter 2.
[0128] As shown in FIG. 10, the operator can toggle to a multiple
plane view. To change from a single plane view to a multiple plane
view, the operator can select the "Select Layout" button 138 on the
tool bar 105 to toggle to the three plane view to concurrently
display three images along different planes. In this view, three
planes of the selected image can be shown simultaneously on the
screen as three separate image windows 102, 146, 148. The three
viewing planes (e.g., axial 102, sagittal 146, and coronal 148) of
the image will be shown and the coronal and sagittal view will have
arbitrary locations until the operator chooses, e.g., clicks on a
point 150 on one of the three views, the other two views will then
display an image that corresponds to the point 150 clicked on by
the operator (FIG. 11).
[0129] As illustrated in FIG. 10, when all three planes are
illustrated, the screen will be split into separate windows, and
typically four windows, in which the three image planes can be in
the first three windows 102, 146, 148, and a fourth window 152 can
contain another image, such as a rendered three dimensional view or
an image along an oblique plane. The three dimensional image will
152 provides information as to the relative position of the nodule
in the patient's lungs. While the illustrated embodiment shows the
display split up into four separate windows, it may be possible to
divide the display into any number of image windows.
[0130] To identify the image planes displayed in each window, each
of windows 102, 146, 148, 152 can be labeled with a title of the
viewing plane, such as "Axial," "Sagittal," and "Coronal."
[0131] In an exemplary embodiment, graphical user interface 100 can
include a "Frames" button 132 in the toolbar 108 that allows the
operator to toggle between concurrent and separate panning and
zooming of image windows 102, 124, 126, 128. In a preferred
embodiment, the images should start at the same scan FOV and then
have concurrent zoom as the default. Activation of Frames button
132, will provide concurrent, proportional zooming and panning
between the three image planes. Activating the Frames button 132 a
second time will return the software to its default position in
which each of windows will have independent zooming and panning in
all of windows 102, 146, 148, 152.
[0132] If the operator wishes to view only a single pane view, the
operator can double click within the selected window that the
operator wishes to view. Alternatively, the operator can activate
the Select Layout button 138 to toggle between a single plane view
and a multiple plane view.
[0133] Toolbar 108 can further include a 3D button 140. 3D button
toggles between 3D/2D mode for the statistics. When the 3D button
is activated, analysis module 46 will display three dimensional
statistics for the nodules. For example, in each plane, only
partial information on spatial characteristics is available. The 3D
button will display the nodule as fully rotatable axes which are
controlled by the direction of mouse movement. The user can then
see in real time the details of the shape in all axes of the
nodule.
[0134] Referring now to FIGS. 12-16, the graphical user interface
100 of the present invention can include a variety of tabs or icons
154 that allow the operator to toggle between the various data
input/output panels 106 of the graphical user interface. Activating
the tabs allows the operator to toggle between the various anatomy
tabs input panels. In an exemplary embodiment, the panel 106
includes tabs for the right and left lung, mediastinum, lesions,
chest wall and heart and aorta. It should be appreciated, however,
that in other configurations of the present invention, graphical
user interface 100 can include more or less tabs.
[0135] FIG. 12 shows Lung Tab Panel 156 of the present invention.
The operator can mark his findings from his analysis of the images
for both the right and left lung on checklist 158. Clicking on
Other 159 will display a text box (not shown) which allows the
operator to enter his findings and/or comments that are not found
in the main checklist. Typically, the comments entered in the text
box, will be displayed in the lung report that is generated by
report generator module 50. To toggle between the left and right
lung, operator can click on or otherwise activate the left or right
lung tab 160a, 160b. Clicking on add button 162 will store the
operator's findings in database 14. Optionally, the stored data can
be compared with national percentile data, and the report generator
module 50 can produce recommendations based on where the patient's
data is relative to the national percentile data.
[0136] FIG. 13 shows a mediastinum panel 164 of the present
invention. The mediastinum panel 164 has a similar checklist 166
that allows the operator to input his findings.
[0137] FIG. 14 shows a Heart and Aorta tab 168. In this tab, the
operator can enter his visual impression on the heart size and the
condition of the aorta. In some configurations, the analysis module
46 will be programmed to calculate the volume of the heart, measure
the long and short axes, provide calcium scoring of the coronary
vessels and aorta, and the like.
[0138] FIG. 15 shows a chest wall and spine tab 170 that allows the
operator to enter his findings and recommendations about the chest
wall and spine.
[0139] FIG. 16 shows a lesion/nodule tab 172. When a lesion is
selected, either automatically by the analysis module 48 or
manually by positioning a ROI around the potential nodule, analysis
module 48 analyzes the nodule selected and calculates all of the
statistics (e.g., volume, surface area, roundness, density, HU
values, and the like). Analysis module 48 can also filter out
surrounding vessels around the nodule, if desired by the
operator.
[0140] In some embodiments, the operator can click on the lesions
to add it to list 174. In other configurations, analysis module 48
can automatically add the selected lesions to list 174. The
operator can revise the list of lesions by clicking on the
potential lesion to select the lesion and click on the delete
button 176 or edit button 128 on toolbar 108. The lesion list 174
will characterize the lesions by its number, location, pixel size,
surface area, volume, two diameters (longest and shortest), mean
value, min/max, and median. The list of all of the tagged nodules
provides the operator with the ability to review and edit the
selected nodules, based on his observation of the statistics. A
total list 178 can be used to show the total statistics for all of
the tagged lesions.
[0141] If the operator chooses to compare two image dataset, the
operator must select the two studies for comparison from database
14. Once the operator has selected the two studies, the software of
the present invention will generate graphical user interface 200,
as shown in FIG. 17. After the two studies are chosen, graphical
user interface 200 will display at least two windows 201, 203 for
displaying images from the first and second study. In exemplary
embodiments, graphical user interface will include a lung nodule
comparison panel 205 for displaying the statistics for the nodules
in the first and second studies. As shown, the panel will include a
portion for displaying the tagged nodules and their statistics for
the first study 207, a portion for displaying the old tagged
nodules and their statistics 209, and a portion for displaying any
newly located nodules 211.
[0142] Lung nodule comparison panel 205 can include an axial button
213, coronal button 215, and sagittal button 217 for displaying
corresponding images of the first and second study in their
respective image planes in windows 201, 203.
[0143] After the first and second studies have been opened, the
operator can align the first and second studies in all three
planes. Alignment can be carried out manually by panning at least
one of the images until the first and second images are aligned.
Alternatively, the comparison module 48 can be activated, typically
via an input button on the graphical user interface, to
automatically align the first and second images.
[0144] In exemplary embodiments, activating the align button 219
starts a calculation process in the comparison module 48 to list
all of the known nodules in the first study and to determine if any
of the nodules localized in the second study are new or old.
Criteria for determining whether a localized nodule in the second
study is new or old can take a variety of forms. One example, one
method of determining of a nodules is new or old is to determine
there are N amount of common pixels of the nodules found in the
first study and a nodule in the second study.
[0145] After the calculating process has been completed, the
comparison data is posted into portions 207, 209, 211,
respectively. If the nodule was incorrectly placed in the wrong
portion of panel 205, the operator can choose the nodule and click
on arrows 221 to move the chosen nodule to the correct portion. The
comparison data can thereafter be saved as a separate file in
database 14.
[0146] Once the operator has reviewed and tagged all of the
nodules, and in the case of comparison studies analyzed the old
nodules and located all of the new nodules, the operator can
activate the Report button 180 to generate the lung report for the
patient. An exemplary lung report 200 is illustrated in FIGS.
18A-18D. It should be appreciated that the lung report 200
illustrated is merely a sample lung report, and that the lung
report of the present invention is customizable by the operator to
display customizable data and recommendations.
[0147] The lung reports of the present invention will vary
depending on whether the image dataset is a first study for the
patient or a comparison study between a baseline and follow-up
study. As illustrated in FIG. 18A, the lung report will typically
list the patient information 202 that is stored in database 14. The
lung report can also list the parameters of the imaging procedure
204. Since each of the lung report is customizable, depending on
what the operator (or imaging facility) has entered into the fields
of the decision tree (FIGS. 7 and 8), each of the lung reports will
typically have different recommendations, and some lung reports may
not list the patient information or information regarding the
imaging procedure.
[0148] As shown in FIG. 18B, typically the lung report 200 will
include a mapped image 206 illustrating the location of the
nodules. As illustrated, the location of the nodules are shown by
numbered markers 208. A nodule chart 210 can be used to provide
data as to the position of the nodules, the number of nodules in
each area of the lung, the volume of the nodule, the volume of the
nodule, mean diameter, mean HU, Total Sum HU, roundness or surface
regularity parameters, or other parameters of the nodule.
[0149] In cases where the lung report is a follow up study, the
lung report 200 can include a comparison chart 212 which lists the
nodules statistics from the baseline study--typically in the form
of volume, the number of nodules etc. The statistics from the
baseline study can then be compared with the statistics from the
follow up scan so as to display new nodules and any percentage
change in size of the old nodules.
[0150] As illustrated in FIG. 18C, lung report 200 can further
include selected body lung images, from the baseline study and/or
the follow up study to display various slices of the lungs 214.
Optionally, lung report 200 can also include a score percentile
rank information 216 to show the patient where he or she ranks in
relation to other patients of similar backgrounds.
[0151] FIG. 18D illustrates lung report 200 that includes the
operator's findings 218 from his analysis of the lung slice image
dataset. Typically, the findings are derived from the operator's
inputs into the graphical user interface input panels (FIGS.
12-16). Conclusions 220 will generally provide customizable
recommendations. The recommendations will typically be produced
through the various factors listed above.
[0152] In another aspect, the present invention provides a database
14 of lung nodule statistics (FIG. 1). Information from the
database can be used to guide the software decision tree to
customize the lung report and to guide a treatment
recommendation.
[0153] In exemplary databases 14 of the present invention, the
database can be accessed over a communication network, like the
internet, and can be used to store lung information of the
population. The population lung information can be used to
determine an individual patient's standing relative to other people
of a similar demographic so as to provide a customized
recommendation based on the patient's percentile ranking.
[0154] In exemplary embodiments, database 14 can store population
information in the database for every person scanned regarding each
nodule. Such information can include, but is not limited to, nodule
information such as the location of the nodule with respect to
anatomic landmarks, volume of the nodule, major and minor axis of
the nodule, an index of the nodule's roundness, surface area,
average signal intensity, standard deviation, maximum and minimum
pixel intensity and patient information, such as age, race, gender,
smoking history, demographics, geographic location, diet, or the
like.
[0155] In addition to the above nodule information, the database of
the present invention can further store the number of nodules,
total nodule mass, and demographics of each of the particular
patients. From this database, one can build an expectation value
for people in the population with similar demographics (e.g., age,
gender, race, smoking pattern, diet, geographic location, and the
like).
[0156] The population lung information can be searched and sorted
using known methods. Typically, the database can be searchable
based on at least one of the nodule statistics and/or patient
fields. As the database is developed, it will be possible to allow
imaging facilities from around the world to access the database and
to compare the individual patient's lung nodule information with
the population data of people of similar demographics. Typically,
the information from each of the individual data scans can also be
logged into the database so as to update the database and allow
radiologists from around the country to compare the outcome of the
patient's case study with other similar case studies.
[0157] When survival rates for different populations and nodule
burdens become known through development of the database, the same
database can be used to provide the lung information based on
demographics and nodule burden. As can be understood, as this
information is accumulated there will be additional information
generated, for instance, whether the location of the nodules in the
lung, or their proximity, may change risk factors and
recommendations.
[0158] As can be appreciated, the stored information in the
database can be used to guide the decision tree of the present
invention regarding the customization of the lung report generator.
It is understood that the risk and recommendations are dynamic and
may change in response to the information accumulated in the
database, or elsewhere. For example, a male of 50 years of age,
with a history of smoking having a burden of five nodules under 3
mm maximum dimension which would be in a high risk group if the
database information shows that the norm for this age and smoking
history is 1 nodule not to exceed 5 mm.
[0159] After the patient lung information has been compared, the
software of the present invention can provide a customized
recommendation in the lung report 300 indicating the
recommendations of the physician based on any combination of the
patient's age, race, gender, smoking history, demographics, nodule
information, and the like. Additionally, the lung report can
provide a graph or chart that illustrates where the patient is
relative to national rankings. (FIG. 18C).
[0160] While the above is a complete description of the preferred
embodiments of the inventions, various alternatives, modifications,
and equivalents may be used. For example, while the above methods
recite methods of comparing lesions in a first image with the
corresponding lesions in a second image, it should be appreciated
that the present invention can be used to compare a "clean"
baseline image with a second, follow up image to determine if any
lesions have formed since the baseline image scan was taken.
Although the foregoing has been described in detail for purposes of
clarity of understanding, it will be obvious that certain
modifications may be practiced within the scope of the appended
claim.
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