U.S. patent application number 14/827335 was filed with the patent office on 2019-05-16 for medical image analysis.
The applicant listed for this patent is ORTHOPEDIC NAVIGATION LTD.. Invention is credited to Ram Nathaniel, Dan Rappaport.
Application Number | 20190147295 14/827335 |
Document ID | / |
Family ID | 39225015 |
Filed Date | 2019-05-16 |
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United States Patent
Application |
20190147295 |
Kind Code |
A1 |
Rappaport; Dan ; et
al. |
May 16, 2019 |
MEDICAL IMAGE ANALYSIS
Abstract
A method of analyzing a medical image, the method comprising
making a measurement on a 2D medical image of an organ and
correcting the measurement in view of an angle of incidence between
an imaging instrument and an imaged organ in the 2D medical
image.
Inventors: |
Rappaport; Dan; (Tel Aviv,
IL) ; Nathaniel; Ram; (Tel Aviv, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ORTHOPEDIC NAVIGATION LTD. |
Ramat Hasharon |
|
IL |
|
|
Family ID: |
39225015 |
Appl. No.: |
14/827335 |
Filed: |
August 17, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13334110 |
Dec 22, 2011 |
9111180 |
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14827335 |
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11858947 |
Sep 21, 2007 |
8090166 |
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13334110 |
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60826448 |
Sep 21, 2006 |
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Current U.S.
Class: |
382/128 ;
378/62 |
Current CPC
Class: |
G06T 7/0012 20130101;
A61B 5/107 20130101; G06T 7/75 20170101; G06T 2207/30004 20130101;
G06K 2209/055 20130101; A61B 5/4509 20130101; A61B 5/4528 20130101;
G06T 5/006 20130101; G06K 9/6255 20130101 |
International
Class: |
G06K 9/62 20060101
G06K009/62; A61B 5/107 20060101 A61B005/107; G06T 5/00 20060101
G06T005/00; G06T 7/00 20060101 G06T007/00; G06T 7/73 20060101
G06T007/73 |
Claims
1. A medical imaging apparatus comprising: (a) interface circuitry
adapted to receive a medical image of an organ, wherein the image
is acquired by an imaging instrument; (b) image processing
circuitry adapted to estimate one or more dimensions of the imaged
organ at least partially based on an angle of incidence between the
imaging instrument and the imaged organ.
2. The apparatus according to claim 1, wherein the angle of
incidence is estimated using reference points on the acquired
image.
3. The apparatus according to claim 1, wherein the organ includes
at least one bone.
4. The apparatus according to claim 1, wherein the organ includes
at least one joint.
5. An apparatus according to claim 1, wherein the medical imaging
instrument is an X-ray.
6. An apparatus for estimating an angle of incidence from which a
2D medical image was captured; said apparatus comprising: (a)
interface circuitry adapted to receive a medical image of an organ,
wherein the image is acquired by an imaging instrument; and (b)
image processing circuitry adapted to: (i) acquiring a 2D medical
image of an organ from a subject; (ii) comparing the acquired image
to at least one angle specific 2D model of the organ; (iii)
determining a match score between the image and the angle specific
2D model; and (iv) estimating an angle of incidence to the image
based upon the match score.
7. The apparatus according to claim 6, wherein the image processing
logic is adapted to compare at least two separate 2D medical images
with one another while considering the estimated angle of incidence
of each image.
8. A apparatus according to claim 7, wherein said image processing
logic if further adapted to compare the contralateral organs with
one another while considering the estimated angle of incidence of
each organ.
9. A medical image analysis system comprising: (a) an input module
adapted to receive an input image of an organ; (b) a digital memory
storing a plurality of angle specific 2D organ models, each model
characterized by an angle of incidence; and (c) analytic circuitry
adapted to estimate an angle of incidence of the input image by
comparing the input image to at least two of the plurality angle
specific 2D organ models.
10. A system according to claim 9, wherein the input module
comprises an image capture device.
11. A system according to claim 9, wherein the analytic circuitry
is adapted to detect at least one discrepancy (D) between the input
image and a selected angle specific 2D organ model.
12. A system according to claim 11, wherein D suggests a pathologic
condition.
13. A system according to claim 9, further comprising a reporting
module adapted to generate a report.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to systems and methods for
analysis of medical images.
BACKGROUND OF THE INVENTION
[0002] Many algorithms for automated analysis of medical images,
such as X-ray images, have been suggested. In general, the
automated analyses rely upon segmentation and/or comparison to a
model. These automated analyses have been applied to analyses of
bone mineral density, fractures and lesions. However, previously
available automated analyses can be influenced by distortion caused
by an angle of incidence of X-ray beams with respect to an imaged
bone. FIGS. 9A and 9B show that a change in the angle of incidence
from -10 degrees (FIG. 9A) to +30 degrees (FIG. 9B) can cause
significant changes in important measured anatomic features. For
example, the femur head offset (indicated by the arrow) is 5.2 cm
at -10 degrees and 2.1 cm at +30 degrees. The head shaft angles
(.theta.) are 135.degree. and 153.degree., respectively. As a
result, even when automated image analyses accurately measure
selected image parameters, the automatically measured parameters
may be difficult to evaluate.
[0003] Manual analysis by a radiologist is subject to similar
influence from angle of incidence, although to a lesser degree. An
expert radiologist may be able to judge an approximate angle of
incidence for a particular image and mentally correct measurements.
However, it may take many years of clinical experience to acquire
this level of experience. Also, a radiologist with vast experience
in evaluating hip X-rays may have significantly less experience
with images of other body portions (e.g. ankle or spine).
Alternatively or additionally, an "eyeball" correction tends to be
more qualitative than quantitative.
[0004] It is known to register a 3D image (e.g. CT scan) of an
organ from a specific patient onto a 2D image acquired from the
same patient (e.g., an X-ray image). This permits information from
CT images to be used during interventional procedures by
registering the CT scan to an intra-operative X-ray fluoroscopy
image. This method registers a fluoroscopy image with respect to a
CT scan ("An Overview of Medical Image Registration Models" Maintz,
J. B. A., & Viergever, M. A. (1998) An Overview of Medical
Image Registration Methods. UU-CS (Ext. rep. 1998-22, Utrecht
University: Information and Computing Sciences, Utrecht, the
Netherlands) and also "Fast Intensity-based 2D-3D Image
Registration of Clinical Data Using Light Fields" (2003) Daniel B.
Russakoff, Torsten Rohlfing, Calvin Maurer; the contents of which
are each fully incorporated herein by reference). Methods described
in these references employ a 3D scan from a subject to evaluate a
2D image from the same subject and do not consider determining an
angle of incidence of a 2D image without acquiring a 3D scan from
the same patient.
[0005] Automatic contour and/or segmentation analysis of medical
images are generally known in the art.
[0006] Chen et al. describe a contour analysis model which relies
upon an algorithm based upon a plurality of 2D images with manually
defined Femur contours to determine Femur contours in additional
input images. (Ying Chen et al. (2005) "Automatic Extraction of
Femur Contours from Hip X-Ray Images", CVBIA 2005: 200-209; the
contents of which are fully incorporated herein by reference).
[0007] Exemplary segmentation models are described by Kass et al.
and by Long and Thoma (M. Kass et al. (1987) "Snakes: Active
contour models". In First International Conference on Computer
Vision; London; pages 259-268; L. R.
[0008] Long and G. R. Thoma (1999) "Segmentation and feature
extraction of cervical spine X-ray images" Proc. SPIE Medical
Imaging: Image Processing, San Diego, Calif., 3661:1037-1046 the
contents of which are each fully incorporated herein by
reference).
[0009] Registration of one image with respect to another has been
attempted by a variety of methods. This subject is reviewed in
"Image registration methods: a survey", by Barbara Zitova and Jan
Flusser (Image and Vision Computing 21 (2003) 977-1000). The
contents of this article are fully incorporated herein by
reference.
[0010] U.S. Pat. No. 6,990,229 describes a system for alignment and
simultaneous display of 2 or more 3D medical images. The disclosure
of this patent is fully incorporated herein by reference.
[0011] U.S. Pat. No. 6,075,879 describes systems and methods for
computer aided detection of suspicious lesions, primarily in breast
tissue. This patent describes comparison of two 2D images acquired
from different known angles. The disclosure of this patent is fully
incorporated herein by reference.
[0012] Horn and Brooks describe reconstruction of the shape of a
smooth object from a given single gray level image. (B. K. P. Horn
and M. J. Brooks (1989) "Shape from Shading" MIT Press, Cambridge
Mass.). According to Horn and Brooks, the shading of an object is a
function of the projection of the surface normal onto the light
source direction. However, shape from shading methods as described
by Horn are useful primarily for images acquired with light, which
is reflected. The contents of this article are fully incorporated
herein by reference.
[0013] U.S. Pat. No. 6,206,566 discloses a system for determining
the angle of incident X-ray beams using X-ray positive marks
deployed during image acquisition. All methods described in this
patent must be implemented during image capture. The disclosure of
this patent is fully incorporated herein by reference.
SUMMARY OF THE INVENTION
[0014] A broad aspect of some embodiments of the invention relates
to improving interpretation of medical images by estimating an
angle of incidence between an imaging instrument and an imaged
organ when performing an image analysis.
[0015] A broad aspect of some embodiments of the invention relates
to a spatial model of an organ in three dimensions based upon 2D
medical images from a plurality of subjects. In an exemplary
embodiment of the invention, the 2D medical images are X-ray
images.
[0016] An aspect of some embodiments of the invention relates to
estimating an angle of incidence of a conventional X-ray image
after capture of the image. Optionally, estimation of the angle of
incidence improves an automated analysis of the image. In an
exemplary embodiment of the invention, the conventional X-ray image
is input into a system which compares the input image to a series
of angle specific 2D models based upon previously acquired 2D
images from many subjects. Optionally, the previously acquired 2D
images include X-ray images and/or 2D images extracted from
tomographic scans.
[0017] Optionally, exemplary embodiments of the invention are able
to estimate the angle of incidence of an input image without
performing a complete segmentation. In an exemplary embodiment of
the invention, estimation of angle of incidence before completion
of segmentation relies upon matching of reference points.
[0018] Optionally, the reference points are used to define a
contour, for example by segmentation. In an exemplary embodiment of
the invention, angle of incidence is estimated by analyzing the
contour with respect to contours of the 2D models.
[0019] Optionally, approximation of angle of incidence includes
interpolation. In an exemplary embodiment of the invention, each
angle specific 2D model is a statistical model comprising an
average representation and a statistical variance. The statistical
models define one or more determinants selected from contours (C),
inner bone parameters (P) and anatomic features (F) as average
values bracketed by an indication of variance (e.g. standard
deviation or standard error of the mean). Optionally, two or more
of different types of determinants are linked in a single model.
For bones, C is generally a primary determinant of the model.
[0020] An aspect of some embodiments of the invention relates to
constructing a model, optionally a statistical model, based upon
previously acquired images from many subjects, each image with a
known angle of incidence. In an exemplary embodiment of the
invention, the model comprises a series of 2D models, each 2D model
characterized by an angle of incidence.
[0021] Optionally, the previously acquired images are standard
X-ray images or are extracted from 3D images (e.g. computerized
tomography scans). In some exemplary embodiments of the invention,
a single image is divided into several organ specific images for
preparation of angle specific 2D models (e.g. a single pelvic X-ray
might be used for modeling of right femur, left femur and pelvic
bones). In other exemplary embodiments of the invention, images of
individual organs are employed to prepare angle specific 2D models
(e.g. a series of lower mandible X-rays).
[0022] Optionally, the previously acquired images are acquired from
subjects including actual patients and/or living volunteers and/or
cadavers.
[0023] Optionally, each subject may provide one or a plurality of
images, each image acquired from a known angle of incidence. Angles
of incidence may be determined, for example, by direct measurement
or by use of fiducial markers.
[0024] In an exemplary embodiment of the invention, angles of
incidence of previously acquired images are grouped by ranges and
an angle of incidence is considered "known" if it can be placed in
one of the ranges. In an exemplary embodiment of the invention,
ranges are defined based on one or more of a desired total number
of models, analysis of actual angles of incidence in acquired
images and analysis of differences between experimental model
contours.
[0025] In an exemplary embodiment of the invention, a range of
angles of incidence covered by a spatial model of a specific organ
(e.g. femur) in a specific type of x-ray image (e.g. front or side
view) is defined according to physiologic parameters of the organ
and/or an expected range of error in positioning an image
acquisition device with respect to the organ.
[0026] Optionally, selection of specific ranges for each individual
model may be based on statistical methods and/or a rule (e.g. at
least 5 models and/or no more than 8 models). For a given number of
images available for model building, there is often a trade-off
between increasing the number of models and decreasing the number
of images used to construct each model.
[0027] Optionally, reducing a number of images used to construct a
model increases variance within the model. In an exemplary
embodiment of the invention, an acceptable degree of variance for a
model is selected and the available images are divided into models
so that none of the models exceed the acceptable level of
variance.
[0028] In an exemplary embodiment of the invention, a set of images
with known angles of incidence are divided to form a series of 2D
models which are statistically differentiable from one another.
Optionally, an iterative process is employed to achieve the desired
division. In an exemplary embodiment of the invention, the
iterative process produces a series of angle specific 2D models
with different angular differences between different pairs of
models.
[0029] In an exemplary embodiment of the invention, an angle of
incidence is determined and/or recorded during capture of images
used to construct the angle specific 2D models.
[0030] In an exemplary embodiment of the invention, the previously
acquired 2D images from many subjects used to construct the 2D
models are registered prior to determination of a normal contour.
In an exemplary embodiment of the invention, registration includes
scaling and/or translation and/or rotation.
[0031] In an exemplary embodiment of the invention, each angle
specific 2D model of each organ is based upon 20, optionally 100,
optionally 1000, optionally 10, 0000, optionally 100,000 or lesser
or greater or intermediate numbers of images. The number of images
employed in constructing a model contributes to the statistical
variation of the model. Optionally, as the number of images
employed to construct a model increases, variation of the model
decreases.
[0032] In an exemplary embodiment of the invention, pathological
images are excluded from a normal model. Optionally, one or more
separate pathological models are generated. Optionally, the
separation of pathological images is used to prevent outliers form
unduly influencing the normal model.
[0033] In an exemplary embodiment of the invention, there is
provided a method of analyzing a medical image, the method
comprising:
[0034] (a) making a measurement on a 2D medical image of an organ;
and
[0035] (b) correcting the measurement in view of an angle of
incidence between an imaging instrument and an imaged organ in the
2D medical image.
[0036] Optionally, the method comprises:
[0037] (c) estimating the angle of incidence after acquisition of
the image using reference points in the 2D medical image and
without use of fiducial markers in the 2D medical image.
[0038] Optionally, the organ includes at least one bone.
[0039] Optionally, the organ includes at least one joint.
[0040] Optionally, the medical image is an X-ray image.
[0041] Optionally, the measurement includes a contour.
[0042] In an exemplary embodiment of the invention, there is
provided a method of organ modeling, the method comprising:
[0043] (a) acquiring a plurality of 2D images of an organ from a
plurality of subjects, each image characterized by an angle of
incidence; and
[0044] (b) producing a series of angle specific 2D models, each
angle specific 2D model comprising a representation of the organ
based upon 2D images with angles of incidence in a defined
range.
[0045] Optionally, each of the angle specific 2D models includes a
group of reference points on a contour (C).
[0046] Optionally, each of the angle specific 2D models includes at
least a portion of a contour (C).
[0047] Optionally, each of the angle specific 2D models includes
substantially all of the contour (C).
[0048] Optionally, each of the angle specific 2D models includes at
least one anatomic feature (F).
[0049] Optionally, each of the angle specific 2D models includes at
least one inner Parameter (P).
[0050] Optionally, each of the angle specific 2D models includes a
graphic representation of at least a portion of the organ.
[0051] Optionally, each of the angle specific 2D models includes a
numerical representation of at least a portion of the organ.
[0052] Optionally, in the numerical representations of the angle
specific 2D models are provided as a lookup table.
[0053] Optionally, the representation is an average
representation.
[0054] Optionally, the series of angle specific 2D models comprise
a spatial model of the organ.
[0055] Optionally, the angles of incidence cover a range of +90
degrees to -90 degrees.
[0056] Optionally, the angles of incidence cover a range of +60
degrees to -60 degrees.
[0057] Optionally, the angles of incidence cover a range of +45
degrees to -45 degrees.
[0058] Optionally, the angles of incidence cover a range of +30
degrees to -30 degrees.
[0059] Optionally, the angles of incidence cover a range of +10
degrees to -10 degrees.
[0060] Optionally, the plurality of 2D images of an organ are in a
same view Optionally, the plurality of 2D images of an organ are
X-ray images.
[0061] Optionally, some subjects provide at least two images of the
organ, each image acquired from a different angle of incidence.
[0062] Optionally, at least two of the images acquired from
different angles of incidence are employed to construct a single
angle specific 2D model.
[0063] Optionally, some subjects provide only a single image of the
organ.
[0064] Optionally, the method comprises measurement of the angle of
incidence during the acquiring.
[0065] Optionally, the methods comprise using fiducial markers
during image acquisition.
[0066] Optionally, the average representation includes an
indication of variance.
[0067] In an exemplary embodiment of the invention, there is
provided a method of estimating an angle of incidence from which a
2D medical image was captured; the method comprising:
[0068] (a) acquiring a 2D medical image of an organ from a
subject;
[0069] (b) comparing the acquired image to at least one angle
specific 2D model of the organ;
[0070] (c) determining a match score between the image and the
angle specific 2D model; and
[0071] (d) estimating an angle of incidence to the image based upon
the match score.
[0072] Optionally, the method is applied to at least two separate
2D medical images of an organ and comprises:
[0073] (e) additionally comparing the at least two separate 2D
medical images with one another while considering the estimated
angle of incidence of each image.
[0074] Optionally, the method is applied to images of two
contralateral organs from a same subject and comprises:
[0075] (e) additionally comparing the contralateral organs with one
another while considering the estimated angle of incidence of each
organ.
[0076] Optionally, the at least two separate 2D medical images each
include a same organ from a same subject;
[0077] wherein the 2D medical images are acquired at different
times.
[0078] Optionally, a series of angle specific 2D models are
employed.
[0079] Optionally, assigning includes interpolation to an angle
between two angles of the series of angle specific 2D models.
[0080] Optionally, the medical image is an X-ray image.
[0081] Optionally, the angle specific 2D models are statistical
models which include an indication of variance.
[0082] Optionally, the statistical models describe at least one
inner bone parameter (P) of a bone.
[0083] Optionally, the statistical models describe at least a
portion of a contour (C) of a bone.
[0084] Optionally, the statistical models describe at least one
anatomic feature (F) of a bone.
[0085] Optionally, the statistical models describe a joint.
[0086] Optionally, the angle specific 2D models cover angular
translations in at least two planes.
[0087] Optionally, the angle specific 2D models cover angular
translations of two bones comprising the joint.
[0088] In an exemplary embodiment of the invention, there is
provided an image analysis system, the system comprising:
[0089] (a) an input module adapted to receive an input image of an
organ; (b) a memory containing a plurality of angle specific 2D
organ models, each model characterized by an angle of incidence;
and
[0090] (c) analytic circuitry adapted to estimate an angle of
incidence of the input image by comparing the input image to the
plurality.
[0091] Optionally, the input module comprises an image capture
device.
[0092] Optionally, the analytic circuitry is adapted to determine
at least one discrepancy (D) between the input image and a selected
angle specific 2D organ model.
[0093] Optionally, D suggests a pathologic condition Optionally,
the system comprises a reporting module adapted to generate a
report.
[0094] Optionally, the report describes at least one discrepancy
(D) of at least one inner bone parameter (P) of a bone
(D.sub.P).
[0095] Optionally, the report describes at least one discrepancy
(D) of at least a portion of a contour (C) of a bone (D.sub.C).
[0096] Optionally, the report describes at least one discrepancy
(D) of at least one anatomic feature (F) of a bone (D.sub.F).
[0097] Optionally, the report describes at least one inner bone
parameter (P) of a bone.
[0098] Optionally, the report describes at least a portion of a
contour (C) of a bone.
[0099] Optionally, the report describes at least one anatomic
feature (F) of a bone.
BRIEF DESCRIPTION OF THE DRAWINGS
[0100] Exemplary non-limiting embodiments of the invention
described in the following description, read with reference to the
figures attached hereto. In the figures, identical and similar
structures, elements or parts thereof that appear in more than one
figure are generally labeled with the same or similar references in
the figures in which they appear. Dimensions of components and
features shown in the figures are chosen primarily for convenience
and clarity of presentation and are not necessarily to scale. The
attached figures are:
[0101] FIG. 1A is a schematic representation of an exemplary image
analysis system according to some embodiments of the invention;
[0102] FIG. 1B is a simplified flow diagram illustrating the
progression from input image to diagnosis according to exemplary
embodiments of the invention.
[0103] FIGS. 2A and 2B are a simplified flow diagram illustrating
procedures associated with an exemplary construction method for a
statistical model database used in some embodiments of the
invention;
[0104] FIGS. 3A and 3B are a simplified flow diagram illustrating
procedures associated with an exemplary image analysis method
employing a database as described in FIGS. 2A and 2B according to
some embodiments of the invention;
[0105] FIG. 4 is a simplified flow diagram illustrating procedures
associated with exemplary medical diagnostic methods according to
some embodiments of the invention;
[0106] FIGS. 5A, 5B, 5C, 5D and 5E illustrate X-ray images of the
hips of a single subject and corresponding diagrams of the right
femur contour and parameters for angles of incidence from -30
degrees to +30 degrees in 15 degree steps;
[0107] FIG. 6A indicates contour discrepancies (black and white
ring) with respect to a statistical contour model according to an
exemplary embodiment of the invention for an angle of incidence of
20 degrees;
[0108] FIG. 6B illustrates a statistical contour model for an angle
of incidence of 20 degrees according to an exemplary embodiment of
the invention;
[0109] FIG. 7A indicates textural parameter discrepancies (black
ring) with respect to a statistical parameter map for an angle of
incidence of 20 degrees;
[0110] FIG. 7B illustrates a statistical textural parameter map for
an angle of incidence of 20 degrees according to an exemplary
embodiment of the invention;
[0111] FIG. 8A indicates anatomic feature discrepancies with
respect to a statistical feature map for an angle of incidence of
20 degrees;
[0112] FIG. 8B illustrates a statistical anatomic feature map for
an angle of incidence of 20 degrees according to an exemplary
embodiment of the invention;
[0113] FIGS. 9A and 9B illustrate X-ray images of a femur acquired
from the same patient at angles of incidence +20 degrees and -5
degrees respectively;
[0114] FIGS. 10A and 10B illustrate X-ray images of a right femur
acquired at an angle of incidence -15 and left femur acquired at an
angle of incidence +15 from the same patient respectively;
[0115] FIGS. 11A and 11B illustrate X-ray images of a left femur
from a same patient taken at times 0 days and 8 days respectively
and acquired at an angle of incidence -15 and 0 degrees
respectively;
[0116] FIGS. 12A, 12B, 12C, 12D and 12E illustrate matching between
contour models and estimated contours and determination of a best
match score according to an exemplary embodiment of the
invention;
[0117] FIGS. 13A, 13B, 13C, 13D, and 13E indicate Principle
Component Analysis (PCA) of aligned contours comprising different
angle specific 2D models (13A, 13B, 13C, 13D, and 13E correspond to
models of angles of incidents in the range [-7, 7] around angles
-30, -15, 0, 15, 30, respectively; Center line is the mean model
contour and both external lines are one standard deviation unit
according to the PCA analysis);
[0118] FIG. 13 F indicates a PCA of contours from all images used
in constructing the models of FIGS. 13A, 13B, 13C, 13D, and 13E;
and
[0119] FIG. 14A shows the 5 angle specific 2D models of Figs. FIGS.
13A, 13B, 13C, 13D, and 13E aligned with respect to one
another;
[0120] FIG. 14 B shows the 299 individual contours the images used
in constructing the models of FIGS. 13A, 13B, 13C, 13D, and
13E.
[0121] FIG. 15 depicts manually selected reference points on a
contour superimposed on an X-ray image;
[0122] FIGS. 16A, 16B, 16C, 16D, 16 E and 16 F are simplified
schematic representations of common joint types (diarthrosis);
and
[0123] FIGS. 16G and 16H illustrate swing and spin movement within
a joint respectively.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
Overview
[0124] FIG. 1A is a schematic representation of an exemplary
medical image analysis system 100 according to some embodiments of
the invention. System 100, or components thereof, can be installed
on a network (e.g. LAN or Internet). Arrows represent communication
via any available channel of communication.
[0125] FIG. 1B illustrates in general terms a method 102 for image
analysis which relies upon angle specific 2D models. In an
exemplary embodiment of the invention, angle specific 2D models
contribute to increased accuracy and/or reliability of the image
analysis. The following description refers to both FIGS. 1A and
1B.
[0126] Various actions indicated in FIG. 1B are depicted in greater
detail in FIGS. 2A, 2B, 3A, 3B and 4 and described below.
[0127] For example, method 102 indicates only that a database 110
has been previously constructed 200 when input image 120 is
provided 310. Details of an exemplary method of database
construction 200 are presented in FIGS. 2A and 2B and described
hereinbelow. Optionally, database 110 is updated over time.
[0128] Details of an exemplary method of comparison 300 are
presented in FIGS. 3A and 3B and described hereinbelow.
[0129] Exemplary diagnostic methods 400, are presented in FIG. 4
and described hereinbelow.
[0130] System 100 (FIG. 1A) comprises a database 110. According to
various exemplary embodiments of the invention, database 110 can
include at least one angle specific 2D model, optionally a
statistical model, of at least one organ and/or a plurality of
individual images of the at least one organ. In an exemplary
embodiment of the invention, the images are X-ray images and the
organs are bones.
[0131] In a first set of exemplary embodiments of the invention,
individual images of the organ (as opposed to prepared angle
specific 2D models) are stored in database 110. According to these
embodiments of the invention, when input image 120 is provided 310,
analytic circuitry 130 generates a series of angle specific 2D
models "on the fly" in response to receipt of image to be analyzed
120. "On the fly" exemplary embodiments offer a possibility of
ongoing updates to the models by adding additional images to the
database, but impose an increased computing burden on analytic
circuitry 130.
[0132] In a second set of exemplary embodiments of the invention, a
series of "ready to use" 2D models of the organ, each model
characterized by an angle of incidence .theta., is stored in
database 110. Optionally, individual images used to construct the
models are also retained in database 110 or in a separate storage.
"Ready to use 2D models" exemplary embodiments are less flexible
than the "on the fly" embodiments in terms of model updates, but
impose a reduced computing burden on analytic circuitry 130.
[0133] In an exemplary embodiment of the invention, the series of
angle specific 2D models constitute a partial spatial model in 3
dimensions.
[0134] FIG. 1B shows that provision 310 of an image to be analyzed
120 is followed by comparison 300 of image 120 to database 110 by
analytic circuitry 130. Optionally, comparison 300 can be to models
residing in database 110 or to models constructed "on the fly" from
images residing in database 110.
[0135] In an exemplary embodiment of the invention, comparison 300
estimates an angle of incidence .theta. of input image 120 by
identifying one of the of angle specific 2D models as a best fit to
input image 120.
[0136] In an exemplary embodiment of the invention, analytic
circuitry 130 is adapted to receive the image 120 to be analyzed.
In some exemplary embodiments of the invention, receipt of image
120 is directly from a digital X-ray camera (not pictured)
connected to analytic circuitry 130, for example by an interface
cable or across a computer network. Alternatively or additionally,
images may be received through a hardware device (e.g. CD ROM
reader; not pictured) or across a network (e.g. local area network
or Internet).
[0137] In an exemplary embodiment of the invention, analytic
circuitry 130 is adapted to communicate with database 110. In "on
the fly" embodiments of the invention, circuitry 130 retrieves
images from database 120 and constructs angle specific 2D models
from the retrieved images for subsequent comparison 300 to an input
image 120. In "ready to use model" embodiments of the invention,
circuitry 130 retrieves existing 2D models from database 120 and
compares 300 image 120 to the retrieved models. Optionally, the
models can be stored in database 110 or in a data storage 140
available to analytic circuitry 130. Optionally, data storage 140
is a temporary storage buffer or cache.
[0138] In various exemplary embodiments of the invention,
comparison between input image 120 and the 2D models may be
conducted by software, firmware or hardware.
[0139] In an exemplary embodiment of the invention, analytic
circuitry 130 and/or database 110 are installed at a central
location accessible via a network from a plurality of remote
locations (e.g. across the Internet). Optionally, analytic
circuitry 130 of system 100 is installed at a central location and
images 120 are supplied from remote locations.
[0140] Optionally, database 110 of system 100 is installed at a
central location and analytic circuitry 130 addresses queries to
database 110 from remote locations each time an input image 120 is
provided 310.
[0141] In an exemplary embodiment of the invention, installation of
circuitry 130 and/or database 110 at a central location contributes
to ease of updates. Optionally, the updates become available to
users at multiple remote locations and/or to follow usage by
multiple user and/or contributes to a reduced computing equipment
requirement at remote user locations.
[0142] In an exemplary embodiment of the invention, system 100
contributes to an increase in a level of service at a small or
understaffed medical facility. Optionally, access to database 110
and/or analytic circuitry 130 reduces a need for an experienced
radiologist.
[0143] In an exemplary embodiment of the invention, a clinically
useful model of a single bone from a series of angles .theta.
comprising a single view stored in database 110 occupies 7, 10, 15,
50 or 100 MB or lesser or greater or intermediate amounts of
memory. Optionally, the amount of memory occupied by database 110
varies as the number of images and/or the number of angle specific
2D models and/or an angular difference between the models.
[0144] An exemplary database 110 based upon 299 femur images and
used to generate the models and perform the image analyses
presented hereinbelow occupies about 10 MB of computer memory.
Optionally, a larger database based upon more images can provide 2D
models characterized by lower variance and/or supply a larger
number of models with smaller angular differences between them.
[0145] Analytic circuitry 130 can output results of comparison
and/or analysis to data storage 140 and/or display 150. Optionally,
the results include a diagnosis 160 resulting from a diagnosis
procedure 400.
[0146] Optionally, diagnosis procedure 400 is performed by a
diagnostic module. In an exemplary embodiment of the invention, the
results of diagnosis procedure 400 (diagnosis 160) are presented as
markings on input image 120. The results may include, but are not
limited to, one or more of a determined estimated angle of
incidence, a contour, one or more anatomic features (e.g.
distances, aspect ratios, angles), one or more bone parameters
(e.g. trabecular direction and/or spacing and/or bone mineral
density).
[0147] Optionally, diagnosis 160 includes an analysis of how the
contour and/or features and/or parameters conform to normative
values.
[0148] While system 100 is described as a collection of separate
functional components, two or more components may be integrated
into a single physical entity. Alternatively or additionally,
functions described as being performed by a single component may be
distributed among two or more separate physical entities. For
example, although analytic circuitry 130 is depicted as a single
item for clarity, it may be physically divided among various pieces
of hardware, with each hardware item performing a specific
function. Alternatively or additionally, although database 110 is
depicted as a single item for clarity, it may be physically divided
among various servers, with each server being queried by analytic
circuitry 130 each time an input image 120 is provided.
[0149] In an exemplary embodiment of the invention, diagnosis 400
comprises comparing multiple input images 120 one to another 418.
In an exemplary embodiment of the invention, consideration of angle
of incidence .theta. when comparing multiple input images 120 makes
diagnosis 160 resulting from diagnostic procedure 400 more accurate
and/or reliable.
Database Construction
[0150] FIGS. 2A and 2B illustrate an exemplary method 200 for
construction of database 110. Method 200 begins with acquisition
210 of a plurality of images of an organ, with each image being
characterized by an angle of incidence .theta. between the imaged
organ and an image capture device. While figures presented
hereinbelow depict a femur, the principles described herein can be
applied to other bones including but not limited to humerus,
vertebrae (especially cervical vertebrae), the ankle, the shoulder,
the elbow and the knee.
[0151] Alternatively or additionally, the principles described
herein can be applied to joints comprising two bones. Optionally,
for joints, each bone comprising the joint can be modeled
separately and/or the joint may be considered in its entirety.
Joint modeling is described in grater detail hereinbelow.
[0152] For any given organ (e.g. bone), a set of determinants
including one or more of relevant reference points, anatomic
features (F) and inner bone parameters (P) are decided upon prior
to model construction. Optionally, the reference points are used to
generate a contour (C). An exemplary set of femur reference points
are described in greater detail hereinbelow in "Defining exemplary
femur reference points".
[0153] Each image placed into database 110 is characterized with
respect to this set of determinants. Registration of multiple
images is performed based on reference points, optionally the
contour, of each bone segment. Optionally, registration is via a
warping technique. An exemplary warping method is described by Lee
et al. (Lee, S. Y. et al. Image morphing using deformation
techniques" (January, 1996) 7(1): 3-23, the contents of which is
fully incorporated herein by reference).
[0154] In an exemplary embodiment of the invention, a statistic of
each inner bone parameter (P) is calculated on a pixel by pixel
basis so that distribution, mean and deviation values can be
employed in modeling.
[0155] Images may be acquired either as digital images or on
conventional X-ray film. If film is employed, the image can be
scanned and converted to a digital image.
[0156] Optionally, the angle of incidence is determined manually
during image acquisition 210, for example by measurement, or after
image acquisition, for example by use of fiducial markers.
Optionally, relative positions between fiducial markers in an image
are indicative of angle of incidence .theta.. One method of
ascertaining the angle of incidence of an X ray image suitable for
use in the context of method 200 is disclosed in U.S. Pat. No.
6,206,566; which is fully incorporated herein by reference.
[0157] In an exemplary embodiment of the invention, images are
collected from many subjects. Optionally, the subjects cover a
range of ages, both genders, a variety of ethnic backgrounds and a
range of physical sizes. Optionally, images may be sorted within
database 110 by relevant criteria (e.g. for pelvis, gender sorting
may be appropriate). In an exemplary embodiment of the invention,
an influence of heterogeneity among the subjects is reduced by a
scaling and/or registration process 216. In an exemplary embodiment
of the invention, scaling and/or registration make the angle of
incidence the primary variable.
[0158] In some embodiments of the invention, each subject
contributes a single image acquired at a single angle of incidence.
In other embodiments of the invention, a single subject contributes
a series of images of an organ, each image acquired at a different
angle of incidence. Optionally, a single 3D image (e.g.
computerized tomography scan) is used to provide a series of
simulated X-ray images from different angles from a single subject
by projection.
[0159] In an exemplary embodiment of the invention, reference
points are identified 212 in each image. Organ specific reference
points are chosen based upon general characteristics of an organ so
that a sufficient number of reference points can be expected to
appear in substantially all images used in construction of database
110. Examples of suitable reference points include, but are not
limited to, an apex, endpoints of a transverse line at a specified
position (e.g. widest point or narrowest point) and a point at
which a contour changes direction. Optionally, two types of
reference points can be employed, anatomic landmarks and
intermediate points on lines connecting landmarks.
[0160] In an exemplary embodiment of the invention, anatomic
landmarks are used to indicate a corresponding position in every
image. Optionally, anatomic landmarks contribute to increased
registration reliability.
[0161] In other exemplary embodiments of the invention, the number
of available anatomic landmarks is considered insufficient. In
these cases, segments can be defined on lines connecting the
anatomic landmarks, for example along a contour with defined number
of intermediate points. Optionally, these intermediate points can
serve as additional reference points.
[0162] Considering femur modeling as an exemplary embodiment of the
invention, femur specific reference points can include, but are not
limited to, endpoints of a shaft cross section at a bottom edge of
the image, turning points at the lesser and/or greater trochanter,
turning points on both sides of the femoral neck and/or a center of
the femur head.
[0163] According to various exemplary embodiments of the invention
3, 5, 7 or 10 or intermediate or greater numbers of reference
points are employed. Use of a smaller number of reference points
can make data entry easier and more rapid. Use of a larger number
of reference points can contribute to more accurate registration
216 and/or alignment and/or scaling.
[0164] In an exemplary embodiment of the invention, marking of a
contour and/or of the anatomic landmarks is done manually on images
used in database construction. Optionally, manual marking
constitutes identification 212 of reference points. In some
exemplary embodiments of the invention, system 100 suggests a
contour and/or reference points to a human operator for approval or
correction. Optionally, additional references points are generated
by analytic circuitry 130. In an exemplary embodiment of the
invention, identified reference points are marked on the image
displayed on display 150 (e.g., using an input device such as a
mouse). At this point database 110 is constructed 214 by storing
the images including a specific angle of incidence and marked
reference points for each image. Images may be stored in database
110 in any convenient format, for example DICOM (Digital Imaging
and Communications in Medicine).
[0165] In an exemplary embodiment of the invention, a contour is
manually indicated on each image. Optionally, images with manually
marked models are stored in database 110 so that the contours will
be available for model building. In some cases manual contour
designation may be more accurate than automatic or computer
assisted contour designation. For example, when a portion of an
organ is partially covered by another organ, visual identification
of the contour by an experienced radiologist may be more reliable
than a computer generated contour. Optionally, reference points
marked exactly on a manually determined image contour are sticky.
The term "sticky" as used here indicates that points chosen by a
human operator (e.g. radiologist) are not moved by circuitry
130.
[0166] Alternatively or additionally, contours may be determined
automatically. Many methods of automatic contour definition are
known, for example those described by Kass et al. or those
described by Long and Thoma ("Segmentation of Medical Images under
Topological-Constraints" M. Kass, A. Witkin, and D. Terzopoulos.
Snakes: Active contour models. In First International Conference on
Computer Vision, pages 259-268, London, June 1987 and "Segmentation
and feature extraction of cervical spine X-ray images" by Long and
Thoma (Proceedings of SPIE--Volume 3661 Medical Imaging 1999: Image
Processing, May 1999, pp. 1037-1046". Each of these articles is
fully incorporated herein by reference. These references describe
automated segmentation, edge definition and feature extraction from
medical X-ray images.
[0167] In an exemplary embodiment of the invention, the contours of
femur images stored in database 110 are defined with approximately
100 points divided into 5 segments defined by anatomic reference
points. Marking of the contours for model construction is
optionally performed manually by selecting correct points along the
contour. These chosen points are further interpolated using splines
and based on the reference points regeneration of points along the
contour. Optionally, this process contributes to equal distribution
along the contours and accurate alignment of reference points
across images.
[0168] FIGS. 5A, 5B, 5C, 5D and 5E show a series of hip images
extracted from a computerized tomography scan of a single subject
and covering a range of -30 degrees to +30 degrees in 15 degree
steps. Below each image a corresponding contour with six reference
points and several informative anatomic features is represented
graphically. These images and/or their corresponding graphic
representations are representative of the type of data used in
construction 214 of database 110.
[0169] In an exemplary embodiment of the invention, conventional 2D
images are employed instead of a 3D scan and an angle of incidence
is determined during image acquisition, for example using methods
described in U.S. Pat. No. 6,075,879, the disclosure of which is
fully incorporated herein by reference.
[0170] In order to generate angle specific models of the organ, a
plurality of images from a specific angle of incidence (.theta.)
are registered 216 to one another using the reference points.
Optionally, registration includes scaling. Registration and
alignment may be, for example, as described in U.S. Pat. No.
6,990,229, the disclosure of which is fully incorporated herein by
reference.
[0171] Once the images from a specific angle .theta. are registered
with respect to one another, a model (M) for each angle of
incidence .theta., can be generated 218. Assuming that images have
been acquired from angles .theta.1 to .theta.n; generation of
models produces a series M.sub..theta.1 to M.sub..theta.n. In an
exemplary embodiment of the invention, each model M is statistical
model comprising a weighted average of a set of items from all
images characterized by a particular angle .theta.. Items may
include, but are not limited to, contours (C), anatomic features
(F)(e.g. measurements, aspect ratios or distances) and inner bone
parameters (P; e.g. internal texture, bone mineral density,
trabecular thickness and/or spacing and/or direction).
[0172] In an exemplary embodiment of the invention, each model M
indicates a statistical variance for one or more items in the
model. Modeling of different item types is now described in greater
detail.
Contour Modeling
[0173] In an exemplary embodiment of the invention, generation 220
(FIG. 2A) of each model M produces a "model contour" C.sub.M. The
series of models M.sub..theta.1 to M.sub..theta.n includes model
contours C.sub.M.theta.1 to C.sub.M.theta.n. Optionally, each
C.sub.M is characterized by a statistical variance. Optionally, the
variance is determined by PCA. An exemplary model femur contour
C.sub.M+20 is shown FIG. 6B superimposed on an image with an angle
of incidence .theta. of +20 degrees.
Parameter Modeling
[0174] In an exemplary embodiment of the invention, calculation 230
(FIG. 2B) of inner bone parameters P for each of acquired images
210 is performed. According to various embodiments of the
invention, parameters P may include, but are not limited to, one or
more of trabecular parameters, cortical parameters and
cartilaginous parameters.
[0175] Trabecular parameters may include, but are not limited to,
one or more of trabecular direction, trabecular density and
trabecular aspect ratio (continuity and/or regularity).
[0176] Cortical parameters may include, but are not limited to, one
or more of distribution of gray level, edge sharpness and edge
curvature (continuity and/or regularity).
[0177] Cartilaginous parameters may include, but are not limited
to, one or more of distribution of gray level, edge sharpness and
edge curvature (continuity and/or regularity).
[0178] Several ways to calculate P are known.
[0179] In an exemplary embodiment of the invention, P is calculated
using methods described by Parkinson and Fazzalari (Fractal
analysis: Methodological principles for fractal analysis of
trabecular bone (2000) Journal of microscopy 198(2):134-142; the
contents of which are fully incorporated herein by reference).
[0180] In an exemplary embodiment of the invention, one or more P
are calculated using methods described by Geraets et al. (Regional
trabecular orientation and texture analysis: the radiographic
trabecular pattern of hips in patients with hip fractures and in
elderly control subjects (1998) Bone, 22(2): 165-173; the contents
of which are fully incorporated herein by reference). Optionally
parameters P are analyzed by texture analysis methods as by W. G.
M. Geraets and P. F. Van der Stelt (1991; Analysis of the
radiographic trabecular pattern, pp. 575-581; the contents of which
are fully incorporated herein by reference).
[0181] In an exemplary embodiment of the invention, P is calculated
using methods described by Gougherty and Henerbry (Modification of
texture analysis in CT to Xray images based on: "Lacunarity
analysis of spatial pattern in CT images of vertebral trabecular
bone for assessing osteoporosis (2002) Medical engineering and
Physics 24:129-138; the contents of which are fully incorporated
herein by reference).
[0182] In an exemplary embodiment of the invention, parameter data
P for each of the images is used to generate (232) model "parameter
maps" P.sub.M.theta.1 to P.sub.M.theta.n. An exemplary parameter
femur map P.sub.M+20 is shown FIG. 7B. Generation 232 optionally
includes texture analysis of images characterized by a same angle
.theta. and/or warping of P data to a relevant model contour
C.sub.M.theta.. In an exemplary embodiment of the invention, more
than one set of maps P.sub.M.theta.1 to P.sub.M.theta.n may be
generated, each set of maps from a subset of images. For example,
in order to prepare a set of parameter maps indicative of
osteoporosis, P.sub.M.theta.1 to P.sub.M.theta.n can be generated
using only images acquired from women aged 70 or more. A second
normative set of P.sub.M.theta.1 to P.sub.M.theta.n can be
generated from women aged 25 to 40.
[0183] At this stage, images for a common angle of incidence
.theta. are represented as model M including a contour
C.sub.M.theta. and/or a parameter map P.sub.M.theta. which
describes inner bone organization.
Feature Modeling
[0184] In an exemplary embodiment of the invention, "anatomic
features" F are calculated 234 for each image using contour and/or
parameter data from individual images 210. Feature data F from
images characterized by a same angle .theta. are employed to
generate 236 a feature map F.sub.M.theta.. For angles .theta.1 to
.theta.n, there will be n feature maps F.sub.M.theta.1 to
F.sub.M.theta.n. Anatomic features F may include, for example,
dimensions, aspect ratios or angles.
[0185] For the femur, medular canal histumus width (MCHW) and
medular canal trochanter width (MCTW) (nominate) are important
anatomic features F for pre-operative measurements in preparation
for total hip replacement. Similarly, Neck Length and the Head
Radius are important anatomic features F to define when preparing a
prosthetic Femur. Shaft width (SHW) and MCHW are important anatomic
features F useful in prognosis of future fracture. A moment of
inertia equation which considers SHW and MCHW can provide an
estimation of bone strength.
Use of Soft Tissue
[0186] In some exemplary embodiments of the invention, Contour C
and/or features F and/or parameters P include soft tissue. For
example, in lateral cervical spine radiographs, the retropharyngeal
space is a known optional radiographic marker (Harris, J. H. (1987)
"The normal cervical spine" In: the radiology of acute cervical
spine trauma. Ed 2. Baltimore: Williams & Wilkins p. 20; the
contents of which are fully incorporated herein by reference).
[0187] An exemplary femur feature map F.sub.M+20 is shown FIG. 8B
and is superimposed on an X-Ray image with an angle of incidence
.theta. of +20 degrees in FIG. 8A.
Advantages of Angle Specific 2D Modeling
[0188] The general process described above as method 200 reduces a
large number of individual images to n angle specific 2D models,
where n in the number of different angles of incidence being
considered. In an exemplary embodiment of the invention, n can be
20, 15, 10, 8, 6, 4 or lesser or greater or intermediate numbers.
Optionally, models M are designated in discrete steps of 5, 10, 15
or 20 degrees or lesser or greater or intermediate angular
differences. Optionally, the steps are not of uniform size. Use of
models M defined by discrete angles of incidence .theta. permits
use of a relatively small number of images for construction of
database 110.
[0189] A desired number of 2D angle specific models may vary from
organ to organ and/or view to view (e.g. front vs. side). In
general an estimated number of models is chosen as a starting point
and an acceptable functional variance is selected. Analytic
circuitry 130 attempts construction of models and a lowest number
of models which provided the accepted variance is adopted.
Typically, the number of models which provide the desired variance
may be influenced by an organ, a range of angles to be considered
and the degree of acceptable variance. In exemplary embodiments
presented herein, 5 angle specific models with 15 degree steps
covering a range of -30 to +30 degrees provide an acceptable femur
model.
[0190] In practice, it is often sufficient to cover a limited range
of angles .theta.. The range of angles .theta. to be covered by
models M is typically influenced by one or both of the expected
amount of variation in camera positioning and the expected amount
of variation in rotation of a target organ within the body.
[0191] For example, if an orthopedic doctor requests a "front hip
X-ray", the instruction is for an angle of incidence .theta. of 0
degrees. In practice, the actual angle of incidence .theta. may
typically vary between +30 degrees and -30 degrees. However, it is
not expected that the actual angle of incidence .theta. will vary
by .+-.90 degrees because a 90 degree shift would produce a "side
view" instead of a "front view". Variation from the requested angle
may be caused, for example, by operator error and/or by motion of
the patient between when they are positioned and when the image is
actually captured.
[0192] Angle of incidence .theta. can also be influenced by
position of a subject, or an organ within a subject, as opposed to
camera position relative to the subject. For example, each femur
may be subject to rotation within the hip. This rotation may result
from an orthopedic condition, or simply from positioning of a
subject on an examination table. For example, a patient lying prone
on their back might rotate their feet so that their toes point
slightly outward. This rotation of the foot would cause a rotation
of their femur which changes angle of incidence .theta. independent
of camera position.
[0193] For these reasons, a series of angle specific 2D models of
the femur might include M.sub.+45 degrees to M.sub.-45 degrees in
5, 10 or 15 degree steps (n=19, 10 and 7 respectively). Optionally,
one of models M corresponds to 0 degrees. In an exemplary
embodiment of the invention, once models M.sub..theta.1 to
M.sub..theta.n have been constructed, one or more characteristics
in C.sub.M or F.sub.M indicative of angle of incidence .theta. are
identified 240. For example, a particular bump in the contour may
be apparent only at angle .theta. of +5 to -5 degrees.
[0194] In the exemplary femur model presented here, the length of
the bulge in a horizontal axis of the lesser trochanter and the
shaft width ratio grows as angle of incidence .theta. changes from
-30 to +30 degrees as depicted in FIGS. 5A to 5E. Alternatively or
additionally, the head shaft angle changes monotonically across the
range of angle of incidence. In an exemplary embodiment of the
invention, both of these anatomic features included in angle
specific 2D models in order to amplify differences between models
from different angles of incidence .theta.. Optionally, use of
multiple anatomic features in models M contributes to a more
accurate estimation of angle of incidence .theta..
[0195] Optionally, one or more items in C.sub.M or F.sub.M may be
interpolated 250 to produce intermediate values corresponding to
intermediate angles of incidence .theta.. Optionally, interpolation
can contribute estimation of a relatively large number of angles of
incidence .theta. from a small number of angle specific 2D models.
However, since C.sub.M or F.sub.M as a function of angle of
incidence .theta. may not be linear, interpolation can contribute
to a reduction in accuracy of an estimate of angle of incidence
.theta.. In order to reduce introduction of unwanted inaccuracies,
interpolation is optionally employed between angle specific models
separated by angular differences of 10, optionally 5, optionally
2.5 degrees or lesser or intermediate values.
Defining Exemplary Femur Reference Points
[0196] FIG. 15 illustrates exemplary anatomic locations used as
reference points in an anterior-posterior image of a femur used in
a model according to some embodiments of the invention. In the
exemplary embodiment of the invention depicted in FIG. 15,
reference points are selected along contour C (black line).
Optionally, reference points are employed in scaling and/or
registration during model construction. In an exemplary embodiment
of the invention, similar reference points in input images 120 are
employed when comparing the input image to an angle specific 2D
model. Optionally, prominent anatomic locations are visually
identified in each image and serve as reference points. In an
exemplary embodiment of the invention, prominent anatomic locations
are points at which a change in contour curvature is visibly
apparent. In an exemplary embodiment of the invention,
relationships between these anatomic locations are defined by
mathematical expressions. Optionally, the relationships may be
defined with respect to identified positions within the image or
with respect to one or more of other anatomic locations. In an
exemplary embodiment of the invention, segments between anatomic
locations are made as regular as possible. The term "regular" as
used here indicates a change in curvature along the contour.
[0197] Ideally, each segment includes a similar change in
curvature. Optionally, use of regular segments provides reference
points distributed throughout the organ.
[0198] In the depicted exemplary embodiment of the invention, two
shaft points 1501 and 1502 are placed at intersection of the shaft
and a bottom edge of the image.
[0199] Optionally, three trochanter points 1504, 1505 and 1506 are
easily anatomically tractable and mathematically defined as
transition points of curvature along the contour.
[0200] In an exemplary embodiment of the invention, trochanter
points 1504, 1505 and 1506 are assigned after the identification of
the shaft.
[0201] Optionally, neck pivots 1507 and 1508 are indicated at
positions where gradients point externally to a head-neck axis.
Determining an Appropriate Number of Models
[0202] In an exemplary embodiment of the invention, the number of
angle specific 2D models is selected in consideration of a
computational burden on analytic circuitry 130. Optionally, a
required maximum variance of a selected anatomic feature that is
most affected by angle of incidence .theta. is designated and a
number of models which meet the variance requirement is
constructed. For example, in a femur model, angle of incidence
.theta. affects head offset to a great degree so that variance in
the amount of head offset can be used to define maximum tolerable
variance in models M.
[0203] In an exemplary embodiment of the invention, a maximum
number of models that are statistically distinguishable from one
another are constructed. Using the maximum number of models that
are statistically distinguishable from one another contributes to
estimation of angle of incidence .theta. with greater accuracy.
[0204] In an exemplary embodiment of the invention, a maximum
number of models that are statistically distinguishable from one
another is determined experimentally, optionally by an iterative
process. In the iterative process, an initial series of models
comprising a large number of angle specific 2D models is
constructed. The models are characterized and compared with one
another. Significance of difference between each pair of successive
models in the series is examined. If no significant difference
between any pair of successive models is found, members of that
pair are joined in a model of an intermediate angle in a next
iteration. In an exemplary embodiment of the invention, iterations
are repeated until each pair of successive models is significantly
different. Optionally, significance between models is calculated
according to a global parameter which reflects the total distance
of a specific contour from the average contour of its related
model, e.g. sum of square differences. In an exemplary embodiment
of the invention, iteration produces a series of angle specific 2D
models with different angular differences between different pairs
of models.
Pathology Considerations
[0205] In an exemplary embodiment of the invention, acquisition of
images 210 includes acquisition of some images characterized by at
least one pathology. In the case of bone images, relevant
pathologies can include, but are not limited to one or more of
fracture, abnormal bone mineral density (e.g. osteoporosis) and
tumor foci.
[0206] In an exemplary embodiment of the invention, construction of
database 110 includes identification and/or classification of
pathological images and/or construction of "pathology models"
M.sub.path. Optionally, each pathology affects one or more of
contour C, parameters P and features F.
[0207] Optionally, images with known angles of incidence .theta.
are acquired and image is linked to a diagnosis (e.g. from a
physician or an automated diagnostic procedure e.g. dexa for bone
mineral density estimation). The diagnosis is optionally used to
exclude a pathological image from a normal angle specific 2D
model.
[0208] Optionally, images are acquired without a diagnosis and
individual images which are more than one, optionally two,
optionally 3 standard deviations (or lesser or intermediate degrees
of variance) away from an angle specific 2D model generated from
the acquired images are excluded from the normal angle specific 2D
model and assigned to a pathology model. Analysis of individual
images can be based upon one or more of C, P and F. For P and F,
one or more individual discriminants can be analyzed separately
and/or as a group.
[0209] When a new input image 120 is analyzed, it is optionally
compared first with normal angle specific 2D models. In an
exemplary embodiment of the invention, this initial comparison to
normal angle specific 2D models contributes to identification 410
of discrepancies D. Identification 410 (FIG. 4) of D can contribute
to a reduction in false negatives and/or provide quantitative
assessment.
[0210] In an exemplary embodiment of the invention, D are then
analyzed in relation to one or more M.sub.path. Optionally,
comparison to M.sub.path contributes to a more exact diagnosis
and/or contributes to generation of an automated decision making
algorithm for classification to various pathological cases. Using
this type of exemplary two step process, a specific image
characterized by discrepancies D in density of regional trabecula
can first be excluded from a normal 2D angle specific model and
then be correlated to a specific osteoporotic stage by comparison
to a series of M.sub.path.
[0211] For example, osteoporosis is typically characterized by a
reduced bone mineral density (a parameter P) from it earliest
stages but may not cause any significant change in bone contours C
until very advanced stages. As a result, a hip X-ray from an
osteoporotic individual can include a femur with a normal contour C
and an abnormal parameter map P.sub.M which shows reduced
trabecular density. Optionally, angle of incidence .theta. for an
osteoporotic bone is determined first by comparison to a series of
normal 2D angle specific models, and parameters P indicative of
osteoporosis are then evaluated with respect to an osteoporosis
pathology model.
[0212] In another example a hip X-ray from an individual recovering
from a femoral fracture can include a femur with an abnormal
contour C and/or one or more abnormal features F. In cases of
fracture, angle of incidence .theta. for is determined first by
comparison to a series of normal 2D angle specific models, and
irregularities in C and/or F are considered indicative of fracture
pathology. The same X-ray from the individual recovering from the
femoral fracture may have large portions of a parameter map P.sub.M
which correspond to a normal parameter map P.sub.M with the same
angle of incidence .theta.. This situation arises because a
fracture will typically disrupt inner bone parameters only in close
proximity to the fracture. Optionally, definition of an edge or gap
between normal portions of parameter map P.sub.M can help to define
or identify the fracture.
[0213] Optionally, each acquired image 210 may be assigned to one
or more pathology models M.sub.path. For example, a single X-ray
may show evidence of severe osteoporosis and a radial fracture. In
this case, the same X-ray might be assigned to both a fracture
pathology model and an osteoporosis pathology model. Optionally, a
defined spatial portion of the image might be assigned to one
pathology model (e.g. osteoporosis) and a second portion of the
same image might be assigned to a different pathology model (e.g.
radial fracture of femur).
[0214] Alternatively or additionally, a pathology may be scored as
to severity. For example, a femur with an abnormal parameter map
P.sub.M indicative of osteoporosis might be scored as mild,
intermediate or severe. Optionally, scoring reflects a degree or
amount of deviation from a normal model using known algorithms for
analysis of P. In an exemplary embodiment of the invention,
pathology models M.sub.path are prepared for each degree of
pathologic severity.
[0215] In an exemplary embodiment of the invention, pathologies are
defined by rules and used to exclude individual images from
inclusion in normal angle specific 2D models and/or to make a
preliminary identification of pathology in input image 120. Rules
can optionally be expressed in terms of normal ranges and/or
statistical variance (e.g. .+-.2 standard deviations or more from a
normal value). Rules can include terms relating to contour C and/or
one or more parameters P and/or one or more features F.
Data Base Types
[0216] Database 110 has been described above in terms of contours
C, parameters P and features F. However, in some embodiments of the
invention, one or two of contours C, parameters P and features F
may be sufficient for construction of a model M.
[0217] For example, if angle specific models
M.sub..theta.1-M.sub..theta.n are being constructed primarily for
diagnosis of osteoporosis, parameters P are more important, and
contours C are less important.
[0218] On the other hand, if angle specific models
M.sub..theta.1-M.sub..theta.n are being constructed primarily for
diagnosis of fracture, contours C and/or anatomic features F are
more important and parameters P are less important.
Organ Considerations Figures presented herein relate to analysis of
a femur in a hip X-ray. However, the principles set forth
hereinabove and hereinbelow may be applied to other organs.
[0219] For example, operative principles described herein may be
applied to analysis of lungs in chest X-rays. Models M of lungs
optionally rely on contour C and/or inner parameters P. Parameters
P suitable for use in a lung model include, but are not limited to
average alveolar size, average alveolar density and airway
dimension.
[0220] Consideration of angle of incidence .theta. in a chest X-ray
can be important, for example, because a patient with some
pathology (e.g. chest trauma) may not be able to lie in a position
that permits acquisition of a regular AP (anterior-posterior) image
at zero degrees. A deviation from zero degrees can cause one and/or
another of the lungs to be distorted in size in a resultant X-ray
image. A difference in lung size, as defined by contour C, is often
considered an important clinical indicator. In an exemplary
embodiment of the invention, contralateral lungs are more
accurately compared to one another by estimating angle of incidence
.theta. using a series of angle specific 2D models as described
above.
[0221] With respect to bones, models M of humerus, vertebrae (e.g.
cervical spine) and ankle (e.g. tibia and/or fibula and/or talus)
are expected to find clinical utility in identification and/or
diagnosis of, lesions (e.g. metastatic), existing fractures,
fracture risks, and/or osteoporosis.
[0222] For the vertebra, there are 6 standard points commonly
collected in the field of vertebral morphometry because they
indicate important anatomic features of the vertebrae. Any or all
of these 6 points may serve as reference points for construction of
models according to exemplary embodiments of the invention.
[0223] For the humerus, reference points based upon anatomic
landmarks can be assigned based on Behiels et al. ("Evaluation of
image features and search strategies for segmentation of bone
structures in radiographs using active shape models" (2002) Medical
Image Analysis 6:47-62; the contents of which are fully
incorporated herein by reference).
Angle Considerations
[0224] The description and figures presented herein describe models
M which consider a single angle of incidence .theta. between an
image acquisition device (e.g. X-ray camera) and an organ (e.g.
bone). In the exemplary embodiments depicted in the figures, angle
.theta. is an angle of rotation with respect to a long axis of the
femur. However, the scope of the invention is not limited to this
particular type of angle of incidence. In various exemplary
embodiment of the invention, angle .theta. comprises alternate
and/or additional angles.
[0225] In an exemplary embodiment of the invention, model M
considers an angle of incidence .theta. between the femur and an
examination table as a knee is raised.
[0226] In an exemplary embodiment of the invention, model M
considers an angle of incidence .theta. could between the femur and
a midline of the body as a foot is moved away from the midline.
[0227] Optionally, angle of incidence .theta. represents a sum of
two or more discrete angles.
[0228] In some preferred embodiments of the invention, two or more
series of angle specific 2D models (e.g. an X series and a Y series
or X, Y and Z series) of an organ are constructed and an input
image is matched to each set separately. In other preferred
embodiments of the invention, a single series of angle specific 2D
models covers angular displacements of an organ in multiple planes
(e.g. X and Y or X, Y and Z) of an organ are constructed and an
input image is matched to each set separately. Optionally,
resolution in each plane can be different.
[0229] For the femur model presented in the figures, angle .theta.
is measured or estimated relative to a long axis of the femur. This
is because the femur at the pelvis is subject primarily to changes
rotation about the long axis. However, other angular
considerations, for example bending of the knee, can exert an
influence on an apparent shape of portions of the femur, such as
the femur head. In an exemplary embodiment of the invention, a
series of angle specific 2D models which consider both rotation of
the femur about its long axis and a degree of bending of the knee
is constructed. Optionally, consideration of 2 angles .theta.
contributes to an increase in accuracy or reliability.
Comparison of an Image to a Series of Angle Specific 2D Models
[0230] FIGS. 3A and 3B are a simplified flow diagram of an
exemplary method of analysis 300 of an input image 120. Analysis
300 includes comparison to database 110 as described above.
[0231] Method 300 typically begins with provision 310 of input
image 120 for analysis. Typically, input image 120 is provided 310
without exact information concerning angle of incidence .theta..
Optionally, image 120 may be designated front, left lateral, right
lateral or rear view. In an exemplary embodiment of the invention,
input image 120 is designated by organ and view (e.g. left femur;
front view).
[0232] Since database 110 optionally contains many series of models
M, designation of organ and/or view for image 120 contributes to
matching input image 120 to a correct series of models M in
database 110.
[0233] Since database 110 contains a series of models M, each model
within a series for a specific angle of incidence .theta., method
of analysis 300 estimates an angle of incidence .theta. for input
image 120 as it determines which model M is most relevant.
[0234] In order to estimate an angle of incidence .theta. for input
image 120, an attempt 312 at contour definition is made by trying
to fit each of C.sub.M.theta.1 to C.sub.M.theta.n onto input image
120. This produces a series of estimated contours C.sub.E1 to
C.sub.En. Calculation 314 of match scores for each of
[C.sub.M.theta.:C.sub.E1] to [C.sub.M.theta.n:C.sub.En] is
conducted and an estimated contour CE with a best match score is
designated real contour (C.sub.R).
[0235] Optionally, trying to fit each of C.sub.M.theta.1 to
C.sub.M.theta.n can rely on known search methods. For example,
every second contour in the series can be tried first, and
intermediate contours tried in regions that look promising. In
other embodiments of the invention, contours are arranged in a
hierarchy so meta models with variances inclusive of several angle
specific 2D models are tried first, and separate angle specific 2D
models within a promising meta model are tried individually
afterwards.
[0236] In an exemplary embodiment of the invention, a distance map
is computed from the initial attempt to match a C.sub.M to the
image and the distance map is used to generate a match score. Match
scores can also be generated based on other determinants. Other
determinants may include, for example regional determinants such as
curvature, tension and distance from an initial estimate at
specific points along the contour and/or global determinants based
on the entire contour such as, for example, a sum of squared
distance from initial estimate and/or a sum of deformation fields
calculated based on inner bone registration. These, or alternate,
determinants may serve as input for more complex techniques which
rely upon decision making circuitry based on one or more of
regression, Bayesian decisions, trees, neural networks and
k-nearest neighbor analysis. A specific implementation of contour
scoring in femur segmentation is described by Chen et al.
("Automatic Extraction of Femur Contours from Hip X-Ray Images"
(2005). Computer vision for biomedical image applications
3765:200-209; the contents of which are fully incorporated herein
by reference).
[0237] FIGS. 12A, 12B, 12C, 12D and 12E illustrate 5 models M super
imposed on an image 120. The diamond-shaped dots are the contour
estimation (CE). The reference contour, marked manually, is
represented in the continuous line. Each final contour is graded
[0-1] based on similarity to the model and the gradients in the
image. The contour with the best score is the selected 316 contour
(C.sub.R) which determines the estimated angle of incidence
.theta.. In this example, model 3 of FIG. 12C is characterized by a
best match score (0.99) between C.sub.M for angle .theta. and CE.
The CE of model 3 is therefore defined as real contour C.sub.R. For
examples presented herein, database 110 contained 299 images in
total (34, 60, 122, 19 and 64 images for models 1-5 respectively).
Optionally, a number of images per model increases over time.
[0238] Optionally, a match score is interpolated 318 to provide a
result characterized by an angle of incidence .theta. between
angles for which models M have been defined. In an exemplary
embodiment of the invention, interpolation applies a polynomial
function to at least neighboring, optionally all, available models
M. Optionally, sorting of images in database 110 by patient
criteria (e.g. age and/or gender) is not performed in conjunction
with interpolation.
[0239] FIG. 3B illustrates that once C.sub.R is determined, real
features F.sub.R can be calculated 320. Optionally, an estimation
of angle of incidence .theta. based upon interpolation 318 of the
match score, is performed. In an exemplary embodiment of the
invention, F.sub.R can be used to refine 330 an estimate of angle
of incidence .theta..
[0240] At this stage, input image 120 is characterized by a real
contour C.sub.R, an angle of incidence .theta. and set of anatomic
features F.sub.R. Optionally, parameters P of input image 120 are
automatically analyzed to produce a real parameter map (P.sub.MR).
At this stage, a diagnosis 400 can be performed.
[0241] Optionally, input image 120 is provided with a known angle
of incidence .theta.K. In this case an attempt 312 at contour
definition is made by interpolating existing models to produce a
model characterized by angle of incidence .theta.K. C.sub..theta.K
is then designated as real contour (C.sub.R). If known angle of
incidence .theta.K matches one of the angles .theta. used to define
a model M, C.sub.M.theta.K is defined as C.sub.R. Even if .theta.K
is known, comparison to angle specific 2D models characterized by
reduced variance increases diagnostic accuracy.
Determining Discrepancies Between Input Image and Selected Angle
Specific 2D Model
[0242] As described above with regard to FIGS. 3A and 3B, an angle
of incidence .theta. is estimated for each input image 120, for
example, by comparison of the input image to a series of angle
specific 2D models as described above with reference to FIGS. 13A
through 13E.
[0243] FIG. 4 illustrates exemplary methods of diagnosis 400 which
rely upon comparison of a selected angle specific 2D model and an
input image which has been partially characterized.
[0244] Method 400 begins after the input image has been analyzed to
the extent that it is characterized at least by an angle of
incidence .theta. and optionally by one or more of a real contour
C.sub.R and a map of real anatomic features F.sub.R as indicated at
332. Optionally, the image is further characterized by actual inner
bone parameters (P.sub.R), for example by texture analysis as
described above.
[0245] Discrepancies D can be indicative of one or more bone
pathologies and/or can monitor differences between different images
acquired from a same subject. Discrepancies D between input image
120 and the 2D model representing images acquired at the same angle
of incidence .theta. can be identified 410 in a variety of
ways.
[0246] In an exemplary embodiment of the invention, comparison 412
of C.sub.M.theta. to C.sub.R detects contour discrepancies D.sub.C
between the model contour C.sub.M.theta. and the real contour
C.sub.R. FIG. 6A illustrates contour discrepancies D.sub.C as grey
circles. Each circle indicates a point where there is a significant
difference in curvature direction and/or contour magnitude between
C.sub.M.theta. and C.sub.R. In FIG. 6A C.sub.R is depicted as a
series of white dots and C.sub.M.theta. is depicted as a solid
line. FIG. 6B shows C.sub.M.theta. as a solid line superimposed on
the input image. Angle of incidence .theta. is 20 degrees in both
panels.
[0247] Discrepancies (D) are significant differences between a
specific item (e.g. contour C, one or more inner bone parameters P
or one or more anatomic features F) in an input image and an angle
specific 2D model M.sub..theta.. Optionally, D considers a mean and
deviation value for each item in the model. The difference (e.g.
distance) between the item in the input image and a mean value of
M.sub.0 is calculated and, if found to exceed 1, optionally 2,
optionally 3 standard deviation units, it is considered a
discrepancy D. According to various embodiments of the invention,
decision making and/or matching and/or scoring of D can be by any
method known in the art and/or by as CAD (computer assisted
diagnosis).
[0248] Discrepancies D indicate possible abnormalities and can be
of clinical diagnostic value. In an exemplary embodiment of the
invention, a diagnosis considers multiple discrepancies D and a
degree of deviation of each discrepancy D from an average value.
According to various exemplary embodiments of the invention,
various statistical methods may be employed to determine D (e.g.
regression and/or Bayesian decisions and/or trees and/or neural
networks and/or k-nearest neighbors). Using this approach, it is
possible to calculate a variety of numerical data for each point
along contour C (e.g. normal intensity gradient and/or regularity
and/or continuity with respect to a length parameter).
[0249] Contour discrepancies (D.sub.C) can be an indication of
traumatic fractures and/or stress fractures and/or a malignant or
benign lesion nearby. Parameter discrepancies (D.sub.P) such as
regional trabecular texture discrepancies are calculated in the
segmented area by calculating the trabecular density, principal
axes (orientation) and principal axes ratio. Exemplary methods for
calculation of D.sub.P can be found in "Methods for comparison and
detection of regional abnormalities" by Lum et al. (Combining
Classifiers for Bone Fracture Detection in X-Ray Images. Proc. Int.
Conf. on Image Processing, 1149-1152, 2005; the contents of which
are fully incorporated herein by reference).
[0250] D.sub.P can indicate current state of bone mineral density
and/or micro-architecture variations that affect bone strength.
D.sub.P can also indicate a break line of displaced or un-displaced
fractures and/or suggest presence of lesion.
[0251] Anatomic Features (F) such as mechanical index are
optionally calculated according to geometrical measurements. The
neck shaft angle (.alpha. in FIG. 8A and A in FIG. 8B) is an
example of an anatomic feature in which a discrepancy D.sub.F can
indicate a fracture at the proximal femur.
[0252] In another exemplary embodiment of the invention, a Dr
indicating narrowing along the cortical bone at the diaphysis can
indicate scalloping caused by a proximal lesion.
[0253] In an exemplary embodiment of the invention, a comparison
414 of P.sub.M.theta. to P.sub.R detects inner bone parameter
discrepancies D.sub.P. FIG. 7B shows P.sub.M.theta. data
superimposed on the femur head. FIG. 7A illustrates trabecular
direction parameter discrepancies in a black circle relative to the
same are in FIG. 7B. Angle of incidence .theta. is 20 degrees in
both panels.
[0254] In an exemplary embodiment of the invention, a comparison
416 of F Me to F.sub.R detects anatomic feature discrepancies
D.sub.F. FIG. 8A illustrates anatomic features in the input image
(F.sub.R).
[0255] FIG. 8B shows F.sub.M.theta. data. Angle of incidence
.theta. is 20 degrees in both panels. Features of interest include
geometrical measurements and calculations based on these
measurements.
[0256] For the femur medular canal histumus width (MCHW) and
medular canal trochanter width (MCTW) (nominate) are typically
considered important pre-operative measures when planning a total
hip replacement.
[0257] Alternatively or additionally, moment of inertia can be
computed based on femur Shaft width (SHW) and femur MCHW. Moment of
Inertia is an important parameter for fracture prognosis (strength
estimation).
[0258] In this example, there is a difference of 15% between femur
head shaft angle in the model (A of FIG. 8B) and the input image (a
of FIG. 8A). Optionally, this discrepancy Dr in angle .alpha.
indicates an increased probability of fracture in the future.
Optionally, analytic circuitry calculates interaction among two or
more discrepancies D.
[0259] In an exemplary embodiment of the invention, consideration
of angle of incidence .theta. reduces variation in measured
contours (C), features (F) and parameters (P) in models M.
Optionally, this contributes to a more accurate analysis of input
images 120.
[0260] In FIGS. 6A, 6B, 7A, 7B, 8A and 8B, comparison 410 is
between a model M.sub..theta.+20 based upon images acquired from
normal individuals and an input image determined to have been
acquired from the same angle. In an exemplary embodiment of the
invention, comparison to one or more pathology models M.sub.path
may be conducted in addition to or instead of comparison to a
normal model.
[0261] Optionally, determination of discrepancies D between an
angle specific 2D model and an input image acquired from the same
angle increases sensitivity and/or early detection capability.
[0262] For example, for trabecular angle (a parameter P),
construction of an angle specific 2 D model reduces variance by
reducing interference from differences in angle of incidence
.theta.. Reducing variance makes it easier to detect a significant
discrepancy D in the trabecular angle parameter. Increased
sensitivity and/or early detection may be important, for example,
in identification of tumor foci and/or weakened areas which are
likely to become sites of future fractures. Tumor detection and/or
fracture prediction may optionally rely discriminants which are
most subject to interference from changes in angle of incidence
.theta..
[0263] In cases where little or no difference is found between the
model and the input image, system 100 optionally indicates that the
input image is "normal" or "healthy".
[0264] In cases where a significant discrepancy D is found between
the model and the input image, system 100 optionally indicates that
the input image is "abnormal". This indication may include
depicting discrepancies D on the input image as shown above and/or
providing a visible or audible signal and/or generation of an
abnormality report. In an exemplary embodiment of the invention, an
abnormality report includes a list of potential diagnoses.
Optionally, a probability is assigned to each diagnosis in the
report, for example, using methods described in U.S. Pat. No.
6,925,200, the disclosure of which is fully incorporated herein by
reference.
[0265] In an exemplary embodiment of the invention, one or more
diagnoses from the abnormality report are independently confirmed.
Independent confirmation can come from comparison to non image data
entered into database 110 and/or from analysis of the input image
and/or non image data by a physician.
[0266] In an exemplary embodiment of the invention, after analysis
of the input image is complete, the image together with its
determined angle of incidence .theta. are added to database 110
and/or incorporated into the 2D model for angle .theta.. In an
exemplary embodiment of the invention, if the input image includes
a discrepancy D, it is added to a relevant pathology model
M.sub.path. Optionally, a substantiated diagnosis is correlated to
the image in database 110 and the Image/diagnosis pair may be used
in subsequent diagnoses. Methods for database update are described
in, for example, Cocosco C. A. et al. (1997) BrainWeb: online
interface to a 3-D MRI simulated brain database. Neuroimage
5(4):S425-27
http://citeseer.ist.psu.edu/article/cocosco97brainweb.html, the
contents of which are fully incorporated herein by reference.
Presentation of Results
[0267] In FIGS. 6A, 6B, 7A, 7B, 8A and 8B, contours (C), features
(F) and parameters (P) are indicated directly on images. In other
exemplary embodiments of the invention, (e.g. FIGS. 5A through 5E),
similar data may be presented as line drawings without
superimposition on a medical image. Alternatively or additionally,
data pertaining to contours (C), features (F) and parameters (P)
may be presented numerically in a table or graphically (e.g. a plot
of trabecular density as a function of displacement).
[0268] In cases where data is presented numerically or graphically,
it may optionally be presented alongside normative values or
normative ranges to make comprehension easier. In an exemplary
embodiment of the invention, analytic circuitry 130 of system 100
includes a reporting module adapted to generate a report.
Optionally, the report is designed to aid in diagnosis and/or
prognosis and/or for planning and/or monitoring a therapeutic
procedure.
[0269] Optionally, the report may describe one or more of at least
one inner bone parameter (P) of a bone; at least a portion of a
contour (C) of a bone and at least one anatomic feature (F) of a
bone. Description is optionally numerical and/or relative to a
normative value.
[0270] Optionally, the report may include one or more of a
discrepancy (D) of at least one inner bone parameter (P) of a bone
(D.sub.P); a discrepancy (D) of at least a portion of a contour (C)
of a bone (D.sub.C) and a discrepancy (D) of at least one anatomic
feature (F) of a bone (D.sub.F).
[0271] In an exemplary embodiment of the invention, the report is
employed in pre-operative planning. Optionally, the report
contributes to increased probability of treatment success.
Optionally, exemplary methods according to embodiments of the
invention automatically extract data, and the estimated angle of
incidence .theta. permits an accurate comparison to a normative
value. This possibility is a vast improvement relative to current
medical practice which typically relies upon sketches marked
manually on X-ray images. Optionally, accurate and quantitative
measurements find especial utility in procedures such as hip and/or
knee replacement. Optionally, exemplary methods according to the
invention can reduce a reliance on 3D imaging techniques such as
computerized tomography.
[0272] For hip replacement, the report optionally includes one or
more of head offset, head shaft angle, flare index (subtrochanteric
medullar canal width divided by the medullar width at the histmus),
and diameter of the femoral head.
[0273] In an exemplary embodiment of the invention, measurements
provided in the report are more accurate and/or more rapidly
available than comparable manual measurements. Optionally, once a
single physical measurement is performed for calibration, analytic
circuitry 130 calculates quantitative values for all measurements
in the image.
Comparison Modes
[0274] Referring again to FIG. 4, some exemplary embodiments of the
invention include comparison of multiple images 418 to determine
discrepancies D in one or more of C, P and F and D.
[0275] FIGS. 9A and 9B are images of a left femur of the same
subject acquired at +20 and -5 degrees respectively. According to
some exemplary embodiments of the invention, concurrent comparison
420 of images of a same organ acquired at different angles to
different angle specific 2D models of the organ is conducted.
Optionally, use of multiple angle specific 2D models increases
sensitivity of detection of discrepancies D in contour C and/or
parameters P and/or features F. The head offsets indicated by arrow
are 3.7 and 5.1 cm, in FIGS. 9A and 9B respectively. The head shaft
angles .theta. are 153.degree. and 135.degree. in FIGS. 9A and 9B
respectively. This example shows that the angle of incidence can
have a dramatic effect on the image.
[0276] In an exemplary embodiment of the invention, comparison of
two images acquired at different angles of incidence .theta.
reveals abnormalities which would not have been apparent in a
single image. For example, some fractures show a substantial
deformation normal to the image plane making them difficult to
diagnose. In an exemplary embodiment of the invention, acquiring
two images reduces a risk of missing clinically important
features.
[0277] FIGS. 10A and 10B depict comparison of images of
contralateral organs 430 from the same subject. FIG. 10A is an
image of a right femur acquired with an angle of incidence of -15
degrees and FIG. 10B is an image of a left femur acquired with an
angle of incidence +15 degrees.
[0278] As explained above, the right and left femurs may be
characterized by different angles of incidence even if their images
are derived from a single X-ray image. Difference in angle of
incidence between contralateral organs within an image may result,
for example, from anatomic abnormalities in adjacent organs (e.g.,
the pelvic bone in the case of a hip X-ray) and/or from slightly
asymmetric positioning of the contralateral organs.
[0279] In an exemplary embodiment of the invention, a series of
angle specific 2D models as described above is employed to perform
an angular correction on data from one organ image so that it has a
corrected angle of incidence .theta. which matches angle of
incidence .theta. of the contralateral organ.
[0280] Optionally, an image of one organ is inverted or mirrored to
facilitate alignment with the contralateral organ. In an exemplary
embodiment of the invention, comparison of inverted aligned
contralateral organs contributes to identification of differences
between the organs. Optionally, the differences are clinically
significant.
[0281] In another exemplary embodiment of the invention, a series
of angle specific 2D models as described above is employed to
perform an angular correction on data from both contralateral
organs so that they share a common angle of incidence (e.g. 0
degrees).
[0282] In an exemplary embodiment of the invention the angular
correction is applied to one or more features F. Optionally, an
initial relative value for any specific feature F is more accurate
than relative values determined by previously available modeling
methods which did not consider angle of incidence .theta..
Optionally, comparison of a feature F from two images produces a
more accurate comparison result because the values for F from each
image are more accurate.
[0283] In an exemplary embodiment of the invention, each of the two
images of the contralateral organs images is analyzed and compared
to a most relevant angle specific 2D model. Optionally, the two
images acquired from different angles of incidence .theta. differ
in one or more of contour C, relevant features F and relevant
parameters P. By analyzing each image with respect to the relevant
model, it is possible to determine discrepancies D.sub.C, D.sub.F
and D.sub.P for each of the contralateral images. Comparison of
discrepancies D between contralateral organs is optionally more
informative than comparing the two images of the contralateral
organs to one another. Optionally, an increase in informativity
results from removing an influence of angle of incidence .theta.
from the comparison.
[0284] Comparison of contralateral organs may be useful, for
example, in confirming tumor foci or for detecting any abnormality.
An initial analysis of a right femur may identify abnormalities in
inner bone parameters P. These abnormalities may be tentatively
identified as tumor foci. In an exemplary embodiment of the
invention, comparison to parameter data P from the left femur
(after appropriate angular correction) can either reveal similar
abnormalities in inner bone parameters P or not. If the
abnormalities in inner bone parameters P are similar in both the
left and right femurs, it may suggest that the abnormalities do not
represent tumor foci. If the abnormalities in inner bone parameters
P are different in the left and right femurs, it may suggest that
the abnormalities do represent tumor foci. Abnormalities in
trabecular density (indicated by black circles) are clearly visible
in the left femur. Since similar abnormalities are not present in
the right femur, the asymmetric abnormalities can form the basis
for a diagnosis, for example metastatic foci.
[0285] FIGS. 11A and 11B are images of a left femur from a single
subject acquired at an arbitrary time 0 and 5 months later
respectively. In addition, each image is characterized by a
different angle of incidence .theta. (-15; and 0 degrees
respectively). In an exemplary embodiment of the invention,
comparison of input images acquired from a same subject at
different times 440 can be used to track progression of clinical
developments (e.g. tumor progression or fracture healing). The
images are from a 70 year old subject complaining of pains in the
left thigh.
[0286] Analysis of FIG. 11A according to exemplary methods
described above suggested diffuse sclerosis possibly indicative of
Paget's disease.
[0287] Analysis of FIG. 11B, from 5 months later, according to
exemplary methods described above depicts less sclerosis and
blastic lesions on proximal femoral diaphysis, and suggests
osteosarcoma as a possible diagnosis.
[0288] Application of exemplary methods according to the invention
contributes to formulation of a working diagnosis based upon a
single X-ray image with respect to inner bone parameters (P).
[0289] In addition, these two panels illustrate the potential power
of exemplary 2D angle specific models according to embodiments of
the invention in making a diagnosis based on two or more images
that represent a clinical progression. Optionally, the ability of
methods according to the present invention to correct for
variations in angle of incidence .theta. in temporally disparate
images contributes to accuracy and/or reliability of diagnosis.
[0290] Appropriate angular corrections as described above can be
applied to a temporal series of images. In an exemplary embodiment
of the invention, use of appropriate angular corrections increases
sensitivity of analysis of clinical progression.
Use of Non-Image Data
[0291] In an exemplary embodiment of the invention, patient data is
entered in conjunction with image to be analyzed 120 and/or with
images acquired 210 to construct database 110. The patient data may
include, for example, age, gender, ethnic heritage and/or relevant
medical history. Optionally, use of non image data contributes to
increased accuracy and/or reliability of analysis 300 and/or
diagnosis 400. For example, comparison of an input image 120 in the
form of a hip X-ray from a 3 month old infant to database 110 may
yield a diagnosis 400 of "excessive cartilaginous tissue in femur".
If only images in database 110 from subjects less than 6 months old
are use to construct neonatal models nnM, comparison of the input
image 120 from a 3 month old infant to nnM may produce a diagnosis
of "normal".
[0292] In some "on the fly" exemplary embodiment of the invention,
non image data associated with images in database 110 is used to
select which images to employ in construction of angle specific 2D
models. Examples of non image data include, but are not limited to,
subject age, gender, height, weight and race.
Potential Advantages of Angle Specific 2D Models
[0293] A 2D model which is based upon images with a narrow range of
angles of incidence is characterized by reduced variance in terms
of contour C, parameters P and features F with respect to a similar
model which does not consider angle of incidence. Optionally, the
reduction in variance increases as the range of angles for each
model is reduced. The reduction in variance is seen most clearly in
FIG. 13.
[0294] FIGS. 13A, 13B, 13C, 13D and 13E each depict an angle
specific 2D model and its variance as determined by PCA (principle
component analysis). FIG. 13F shows a model based on all 299 images
(without regard to angle of incidence) employed to construct the
angle specific models of the preceding 5 panels. The "general"
model of FIG. 13F is characterized by a greater variance (.+-.35
mm) than the angle specific models, especially with regard to key
anatomic landmarks and/or anatomic features. For comparison, the
greatest variance in an angle specific 2D model (FIG. 13C;
.theta.=0 degrees) is .+-.5 mm.
[0295] FIG. 14A shows the five angle specific models of FIGS. 13A
to 13E registered with respect to one another. FIG. 14 B shows the
299 individual image contours used to construct the five angle
specific models of FIGS. 13A to 13E registered with respect to one
another.
[0296] Similarly, estimation of angle of incidence in an analyzed
X-ray image may permit a more stringent automated analysis of the
X-ray image. Optionally, use of a correct angle specific 2D model
contributes to an increased stringency of the analysis. Optionally,
more accurate scaling, registration and/or alignment of individual
images used in constructing angle specific 2D models contributes to
an increased stringency of analysis.
[0297] In an exemplary embodiment of the invention, an automated
medical diagnosis resulting from analysis of the X-ray image by
comparing to an angle specific 2D model is more accurate and/or
more reliable than an automated medical diagnosis conducted using
previously available technologies which do not consider the angle
of incidence. Optionally, an increase in accuracy and/or
reliability stems from the reduced variance in models which
consider angle of incidence.
[0298] Alternatively or additionally, use of angle specific 2D
models as described above reduces reliance on 3D scanning
technologies (e.g. computerized tomography) by permitting
estimation of an angle of incidence .theta. of a 2D image without
performing a 3D scan on the same patient. In an exemplary
embodiment of the invention, a series of angle specific 2D models
constructed from images acquired from many different subjects
substitutes for a CT scan acquired from a same subject that
provides input image 120.
[0299] In an exemplary embodiment of the invention, the angle
specific 2D models improve over time as additional images are added
to database 110 from which the models are constructed. Optionally,
the improvement over time will vary as the Gaussian nature of C, P
and F. In some cases, the number of images in database 110 can
reach a size beyond which addition of more images will not improve
angle specific 2D models generated from images in database 110.
Consideration of Angle of Incidence in Modeling of Joints
[0300] In an exemplary embodiment of the invention, methods as
described above are applied to analysis of joints. The term "joint"
as used herein covers any junction between at least two bones.
Optionally, each bone in the joint can be modeled individually
using exemplary methods as described above. As a consequence, a
joint model according to an exemplary model of the invention
combines all states defined for each bone with respect to all
states defined for every other bone in the joint. In an exemplary
embodiment of the invention, joint specific features, such as
articular cartilaginous dimensions and/or space dimensions between
different sections of the joint are added to the model.
[0301] As described above, the angle of incidence .theta. of a long
bone is defined by the position of two bone edges with respect to
the camera. Modeling of the junction between two bones adds another
edge of the second bone (first edge is constraint to the joint
position). In an exemplary embodiment of the invention, this
additional edge raises a number of degrees of freedom to nine.
Optionally, combining the two bones creates a much wider
theoretical number of degrees of freedom than actually exist in the
joint. In an exemplary embodiment of the invention, joint models
consider changes in spatial orientation for component part of the
joint. Optionally, a joint model is simplified by considering known
physical constraints on range of motion of components in a
particular joint in X and/or Y and/or Z directions.
[0302] FIGS. 16A, 16B, 16C, 16D, 16E and 16F are simplified
schematic representations of common joint types (diarthrosis) and
FIGS. 16G and 16H illustrate swing and spin movement within a joint
respectively (based upon figures by Huei-Ming Chai, PhD.) While
these joint types are all known, construction of angle specific 2D
models which consider the relative displacement of all bones in the
joint by grouping X-ray images acquired from known angles of
incidence has not, apparently, been described.
[0303] FIG. 16A depicts a plane joint, also known as an irregular
joint or arthrodial joint or arthrodia. Joints of this type are
non-axial and are capable only of sliding movement. Plane joints
are commonly found in facets of the spine. In an exemplary
embodiment of the invention, a model of a plane joint considers
angle of incidence .theta. as well as a degree of displacement
between the two bones.
[0304] FIG. 16B depicts a hinged joint or ginglymus. Hinged joints
are uniaxial and are characterized by a single degree of freedom.
The humeroulnar joint is a uniaxial joint. In an exemplary
embodiment of the invention, a model of a hinged joint considers
angle of incidence .theta. as well as an angle of flexion, or
swing, between the two bones.
[0305] FIG. 16C depicts a pivot joint, also known as a trochoid
joint or screw joint. Pivot joints are uniaxial and are
characterized by a single degree of freedom. The proximal
radioulnar joint is a pivot joint. In an exemplary embodiment of
the invention, a model of a pivot joint considers angle of
incidence .theta. as well as a degree of rotation of one bone with
respect to another bone.
[0306] FIG. 16D depicts a condyloid joint, also known as an ovoid
joint or ellipsoidal joint. Condyloid joints are biaxial and are
characterized by two degrees of freedom and an ovoid joint surface.
The radiocarpal joint is a biaxial joint. In an exemplary
embodiment of the invention, a model of a condyloid joint considers
angle of incidence .theta. as well as a rotation of the condyloid
"ball" within the chondyloid "socket". The rotation may optionally
be expressed as X rotation and Y rotation or as a combined
measurement reflecting the total angular displacement.
[0307] FIG. 16E depicts a saddle joint or sellar joint. Saddle
joints are biaxial and are characterized by two degrees of freedom
and a sellar joint surface. The first carpometacarpal joint is a
biaxial joint. In an exemplary embodiment of the invention, a model
of a pivot joint considers angle of incidence .theta. as well as a
degree of rotation of each bone with respect to the other bone.
[0308] FIG. 16F depicts a ball-and-socket joint or spheroidal
joint. Ball-and-socket joints are triaxial and are characterized by
three degrees of freedom and a spherical joint surface. The
glenohumeral joint is a ball and socket joint. In an exemplary
embodiment of the invention, a model of a pivot joint considers
angle of incidence .theta. as well as a degree of rotation of a
ball portion of the joint within a socket portion of the joint.
Rotation of the ball within the socket may optionally be expressed
as X rotation and Y rotation or as a composite measurement
reflecting the total angular displacement.
[0309] In general, osteakinematic movements in a synovial joint
(between 2 bony segments) can be characterized as one of three
types. [0310] The first type is "swing" (FIG. 16G) which
describes--rotary motion about a fixed axis at a proximal segment
e.g. knee flexion. [0311] The second type is "spin" (FIG. 16H)
which describes axial rotation about a longitudinal axis of a
distal segment e.g. forearm pronation. [0312] The third type is
"slide" (FIG. 16A) which describes linear translation of one bone
with respect to another.
Exemplary Considerations for Choosing an Angular Range for Modeling
Different Organs
[0313] In an exemplary embodiment of the invention, a series of
angle specific 2D models comprise a spatial model which describes
an organ through an angular range. Optionally, the angular range is
selected in consideration of a normal range of motion of the organ.
Optionally, the normal range of motion may include two or more
angular rotations in different planes. Optionally, only one angular
rotation is deemed relevant to the model. The anatomical position
is used as zero degrees unless otherwise indicated.
[0314] For example, the femur in anterior-posterior (AP) image, may
exhibit all the range of rotation (e.g., -40 to +60 degrees) but
only minor degree of possible flexion and tension values (same for
adduction and abduction). The imaged plane is defined by the type
of the image, i.e. the frontal plane.
[0315] In an exemplary embodiment of the invention, a hip model
considers movement of a femur as it rotates in an acetabulum.
Relevant normal ranges of motion (in degrees) for an exemplary hip
model (using the anatomical position as zero degrees) are Flexion=0
to 125 degrees; Extension=0 to 30 degrees; Adduction=0 to 25
degrees; Abduction=0 to degrees; External rotation=0 to 60 degrees
and Internal rotation=0 to degrees.
[0316] In an exemplary embodiment of the invention, a knee model
considers movement of a femur with respect to the tibia and/or
fibula and/or patella. Relevant normal ranges of motion (in
degrees) for a Knee model are: Flexion=0 to 140 degrees and
Extension-zero degrees=full extension.
[0317] In an exemplary embodiment of the invention, an ankle model
considers the following ranges of motion:
[0318] (i) from that position, dorsiflexion is 0 to 20 degrees;
plantar flexion is 0 to degrees (neutral position is with foot at
90 degrees to leg); and
[0319] (ii) any varus or valgus angulation of the os calcis in
relationship to the long axis of the tibia and fibula.
[0320] In an exemplary embodiment of the invention, shoulder,
elbow, forearm, and wrist range of motion are measured with zero
degrees the anatomical position with two exceptions. The first
exception is that supination and pronation of the forearm are
measured with the arm against the body, the elbow flexed to 90
degrees, and the forearm in mid position (zero degrees) between
supination and pronation
[0321] The second exception is that shoulder rotation is measured
with the arm abducted to 90 degrees, the elbow flexed to 90
degrees, and the forearm reflecting the midpoint (zero degrees)
between internal and external rotation of the shoulder.
[0322] In an exemplary embodiment of the invention, image analysis
f an organ, depends on a position of another organ (e.g. bone)
visible in a same image as a target organ. For example, upper arm
bone can be analyzed after lower arm rotation and/or extension is
determined.
[0323] In an exemplary shoulder model, relevant ranges of motion
are: forward flexion=zero to 180 degrees; abduction=zero to 180
degrees; external rotation=zero to 90 degrees and internal
rotation=zero to 90 degrees.
[0324] In an exemplary elbow model, relevant ranges of motion are:
flexion=zero to 145 degrees; forearm supination=zero to 85 degrees
and forearm pronation=zero to 80 degrees.
[0325] In an exemplary wrist model, relevant ranges of motion are:
dorsiflexion (extension)=zero to 70 degrees; palmar flexion=zero to
80 degrees; radial deviation=zero to 20 degrees and ulnar
deviation=zero to 45 degrees.
Types of Angle Specific 2D Models
[0326] Exemplary embodiments of the invention described in detail
hereinabove employ contour C as a primary determinant used to
estimate angle of incidence .theta.. However, contour based angle
specific 2D models are only one of a wide variety of angle specific
2D model types.
[0327] In an exemplary embodiment of the invention, each of the
angle specific 2D models includes a group of reference points on a
contour (C) and the reference points serve as primary
determinants.
[0328] Optionally, the reference points can be connected to define
one or more of portions of contour C, optionally to define
substantially all of contour (C) as in the exemplary embodiments
depicted in FIGS. 13 A through 13E and 14A. In an exemplary
embodiment of the invention, the angle specific 2D models rely upon
at least one anatomic feature (F) as a primary determinant.
[0329] Optionally, the anatomic feature includes one or more lines
connecting two reference points not along contour C. Optionally,
feature F is an aspect ratio between two or more such lines.
[0330] In an exemplary embodiment of the invention, the angle
specific 2D models rely upon at least one inner Parameter (P) as a
primary determinant.
[0331] According to various exemplary embodiments of the invention,
the angle specific 2D models can be stored and/or presented in a
variety of ways.
[0332] Optionally, each of the angle specific 2D models includes a
graphic representation of at least a portion of the organ.
Alternatively or additionally, each of the angle specific 2D models
includes a numerical representation of at least a portion of the
organ in terms of one or more of C, one or more relevant P and one
or more relevant F. Optionally, the numerical representations of
the angle specific 2D models are provided as a lookup table.
General
[0333] The individual features described herein can be used
together, in the manner above, in a single system or method.
Alternatively, each of the features (or some combination or
sub-combination of them) can be used separately. Specifically,
features described in the practice of a method may characterize a
system and features described in conjunction with a system may
characterize a method. Furthermore, it should be understood that
the described embodiments are exemplary in nature and are not
intended to limit the scope of the invention which is defined by
the claims.
[0334] The described methods and systems rely upon execution of
various commands and analysis and translation of various data
inputs. Any of these commands, analyses or translations may be
accomplished by software, hardware or firmware according to various
embodiments of the invention. In an exemplary embodiment of the
invention, machine readable media contain instructions for
construction of angle specific 2D models and/or comparison of input
images to angle specific 2D models. In an exemplary embodiment of
the invention, analytic circuitry 130 executes instructions for
construction of angle specific 2D models and/or comparison of input
images to angle specific 2D models.
[0335] The terms "include", "comprise" and "have" and their
conjugates as used herein mean "including but not necessarily
limited to".
[0336] The present invention has been described using detailed
descriptions of embodiments thereof that are provided by way of
example and are not intended to necessarily limit the scope of the
invention. In particular, numerical values may be higher or lower
than ranges of numbers set forth above and still be within the
scope of the invention. The described embodiments comprise
different features, not all of which are required in all
embodiments of the invention. Some embodiments of the invention
utilize only some of the features or possible combinations of the
features. Alternatively or additionally, portions of the invention
described/depicted as a single unit may reside in two or more
separate physical entities which act in concert to perform the
described/depicted function. Alternatively or additionally,
portions of the invention described/depicted as two or more
separate physical entities may be integrated into a single physical
entity to perform the described/depicted function. Variations of
embodiments of the present invention that are described and
embodiments of the present invention comprising different
combinations of features noted in the described embodiments can be
combined in all possible combinations including, but not limited to
use of features described in the context of one embodiment in the
context of any other embodiment. The scope of the invention is
limited only by the following claims.
[0337] All publications and/or patents and/or product descriptions
cited in this document are fully incorporated herein by reference
to the same extent as if each had been individually incorporated
herein by reference.
* * * * *
References