BACKGROUND
Embodiments of the present disclosure relate to imaging, and more
particularly to the identification of an optimal image frame for ultrasound imaging.
As will be appreciated, ultrasound imaging has been employed for a wide
variety of applications. During the process of ultrasound scanning, a clinician
attempts to capture a view of a certain anatomy which confirmslnegates a particular
medical condition. Once the clinician is satisfied with the quality of the view or the
scan plane, the image is frozen to proceed to the measurement phase. For example,
ultrasound images are routinely used to assess gestational age (GA) and weight of a
fetus or to monitor cardiac health of a patient. Ultrasound measurements of specific
features of fetal anatomy such as the head, abdomen or the femur from twodimensional
(2D) or three-dimensional (3D) image data are used in the determination
of GA, assessment of growth patterns and identification of anomalies. Similarly, for
cardiac applications, thicknesses of cardiac walls are routinely measured by
cardiologists to check for cardiomyopathy.
Image acquisition is quite a challenging problem for sonographers.
Currently, image acquisition takes anywhere between 1 to 5 minutes for each correct
scan plane acquisition and more so for novice clinicians. The other challenge the less
experienced clinicians/sonographers face is the ability to correctly identi@ acceptable
scan plane frames. It is also desirable for the clinicians to have an understanding of
how far they are from correct scan plane. Moreover, ultrasound images are subject to
both patient and operator/clinician variability. Also, determining a quality of an
image frame is fraught with challenges. Particularly, pixel intensities in the images
vary significantly with different gain settings.
Currently, there exist semi-automated and automated techniques for
ultrasound image analysis. However, ultrasound images, such as fetal ultrasound
images are invariably contaminated by a number of factors that can compromise a
diagnosis. The contaminants may include factors, such as, but are not limited to, near
field haze due to fat deposits, unpredictable patient movement, and the ubiquitous
speckle noise. Operator variability also limits reproducibility of ultrasound imagery
and measurement. There are multiple reasons for the inter-operator variability.
Firstly, two-dimensional (2D) echocardiography visualizes only a cross-sectional slice
of a three-dimensional structure, commonly referred to as the scan plane. Even small
changes in positioning of the transducer, which has six degrees of freedom, may lead
to significant changes in the scene visualized, which may in turn lead to incorrect
measurement. In addition, sub-optimal ultrasound image settings such as gain, timegain
compensation may decrease the ability to visualize the internal structures of the
human body.
Early efforts at improving robustness and accuracy of clinical workflow
have tended to focus on semi-automated methods that include, for example, femur
seginentation, head segmentation and cardiac segmentation. However, the above
processes tend to be time-consuming. Additionally, use of these techniques may
entail user intervention or call for a trained sonographer. These techniques may also
be subject to operator variability or may be prone to false detection. In remote or
rural markets it may be particularly difficult to obtain services of a trained
ultrasonographer or ultrasound technician, causing remote regions to be poorly served
or underserved.
BRIEF DESCRIPTION
In accordance with aspects of the present technique, a method for
identifying an optimal image frame is presented. The method includes receiving a
selection of an anatomical region of interest in an object of interest. Moreover, the
method includes obtaining a plurality of image frames corresponding to the selected
anatomical region of interest. The method also includes determining a real-time
indicator corresponding to the plurality of acquired image frames, wherein the realtime
indicator is representative of quality of an image frame. Additionally, the
method includes communicating the real-time indicator to aid in selecting an optimal
image frame. A non-transitory computer readable medium including one or more
tangible media, where the one or more tangible media include code adapted to
perform the method for identifying an optimal image frame is also presented.
In accordance with another aspect of the present technique, a system is
presented. The system includes a rating platform configured to receive a selection of
an anatomical region of interest in an object of interest, obtain a plurality of image
frames corresponding to the selected anatomical region of interest, determine a realtime
indicator corresponding to the plurality of acquired image frames, wherein the
real-time indicator is representative of quality of an image frame, and communicate
the real-time indicator to aid in selecting an optimal image frame.
In accordance with yet another aspect of the present technique, an imaging
system is presented. The imaging system includes an acquisition subsystem
configured to obtain a plurality of image frames corresponding to a region of interest
in an object of interest. In addition, the imaging system includes a processing
subsystem in operative association with the acquisition subsystem and including a
rating platform, wherein the rating platform includes a feature extraction module
configured to extract one or more features of interest from the plurality of image
frames, a quality metric generator module configured to generate a quality metric
corresponding to one or more image frames in the plurality of image frames, an image
frame selector module configured to select one or more image frames based on the
quality metric, and a feedback module configured to generate and communicate in
real-time an indicator representative of the quality metric.
DRAWINGS
These and other features, aspects, and advantages of the present disclosure
will become better understood when the following detailed description is read with
reference to the accompanying drawings in which like characters represent like parts
throughout the drawings, wherein:
FIG. 1 is a diagrammatical illustration of a system for automated
identification of an optimal image frame for ultrasound imaging, in accordance with
aspects of the present technique;
FIG. 2 is a diagrammatical illustration of one embodiment of the system of
FIG. 1, in accordance with aspects of the present technique;
FIG. 3 is a flow chart depicting an exemplary method for automated
identification of an optimal image frame for ultrasound imaging, in accordance with
aspects of the present technique;
FIG. 4 is a diagrammatical illustration of an image frame corresponding to
a fetal head;
FIG. 5 is a flow chart depicting an exemplary method for automated
identification of an optimal image frame for ultrasound imaging of a fetal head, in
accordance with aspects of the present technique;
FIGs. 6(a) and 6(b) are diagrammatical illustrations of fetal head image
frames along with a quality indicator, in accordance with aspects of the present
technique;
FIG. 7 is a diagrammatical illustration of an image frame corresponding to
a patient's heart in the parasternal long axis view;
FIG. 8 is a flow chart depicting an exemplary method for automated
identification of an optimal image frame for ultrasound imaging of a heart, in
accordance with aspects of the present technique;
FIGs. 9(a), 9(b) and 9(c) are diagrammatical illustrations of heart image
frames along with a quality indicator, in accordance with aspects of the present
technique;
FIG. 10 is a diagrammatical illustration of an image frame corresponding to
a fetal femur;
FIG. 11 is a flow chart depicting an exemplary method for automated
identification of an optimal image frame for ultrasound imaging of a fetal femur, in
accordance with aspects of the present technique;
FIGS. 12(a) and 12(b) are diagrammatical illustrations of fetal femur image
frames along with a quality indicator, in accordance with aspects of the present
technique; and
FIG. 13 is a diagrammatical illustration of an ultrasound imaging system
for use in the system of FIG. 1.
DETAILED DESCRIPTION
As will be appreciated, during the process of ultrasound scanning, the
clinician, such as a radiologist or a sonographer tries to capture a view of a certain
anatomy, or a view which confirms or negates a particular condition. Once the
radiologist is satisfied with the quality of the scan plane, the image is frozen to
proceed to the measurement phase. To that end, acquisition of an "optimal" image
frame or scan plane corresponding to an anatomical region of interest in correct scan
planes is an important step towards accurate diagnosis. In accordance with exemplary
aspects of the present technique, systems and methods configured to aid in enhancing
ultrasound imaging workflow are presented. In particular, the methods and systems
are configured to aid in the automated identification of an optimal image frame.
Additionally, the system and methods are configured to generate an indicator for each
image frame in real time, where the indicator is generally representative of a quality
of the current image frame. Accordingly, the systems and methods described
hereinafter are also configured to flag the most accurate scan plane frame and
facilitate automated measurements using the optimal image frame. Moreover, once
an image is frozen, the systems and methods are also configured to rate the scan plane
quality before performing any measurement.
FIG. 1 is a block diagram of an exemplary system 100 for use in diagnostic
imaging in accordance with aspects of the present technique. The system 100 is
configured to aid a clinician such as a radiologist or an ultrasound technician in
imaging an object of interest.
As will be appreciated, during a scanning procedure, the clinician, typically
positions an ultrasound probe on or about a region of interest to be imaged. It may be
noted that the object of interest may include a patient, a fetus, or a test object. During
the scanning procedure, the clinician acquires a plurality of image frames
corresponding to an anatomical region of interest in the object of interest. However, it
is desirable to identify an optimal image frame that may be used to perform
measurements. As used herein, the term optimal image frame is used to refer to a best
possible image frame that has a desired image attribute in accordance with desired
guidelines and hence may be used to perform any subsequent measurements. The
desired guidelines may include clinical guidelines or industrial guidelines.
In particular, the system 100 is configured to determine a quality
corresponding to each acquired image frametplane. To that end, the system 100 is
also configured to generate an indicator that is representative of the quality of each
acquired image frame. Furthermore, as used herein, the term quality of the image
frame is used to refer to a goodness of fit of a current image frame to a standard
template for a specific view of an anatomical region of interest. Moreover, system
100 is also configured to communicate the indicator so generated to the clinician,
thereby aiding the clinician in the imaging process. In particular, the indicator may be
provided as feedback to the system 100 or the clinician. It may be noted that the
indicator may be generated and provided to the clinician in real-time. Furthermore, it
may be noted that in one example, the acquired image frame may include a twodimensional
(2D) image frame. Also, in certain embodiments, the image frames may
include B-mode ultrasound images. Additionally, the 2D image frames may include
static 2D image frames or cine loops that include a series of 2D image frames
acquired over time. It may be noted that although the present technique is described
in terms of 2D ultrasound images, use of the present technique with three-dimensional
(3D) ultrasound images and four-dimensional (4D) ultrasound images is also
envisaged.
In the present example, the object of interest may include a fetus in the
patient 102. It may be noted that although the present technique is described with
reference to a fetus as the object of interest, use of the present technique for imaging
anatomical regions of interest in other objects of interest such as an adult patient is
also envisaged. To that end, the system 100 may be configured to acquire image data
representative of the fetus. In one embodiment, the system 100 may acquire image
data from the fetus via an image acquisition device 104. Also, in one embodiment,
the image acquisition device 104 may include a probe, where the probe may include
an invasive probe, or a non-invasive or external probe, such as an external ultrasound
probe, that is configured to aid in the acquisition of image data. Also, in certain other
embodiments, image data may be acquired via one or more sensors (not shown) that
may be disposed on the fetus. By way of example, the sensors may include
physiological sensors (not shown) such as electrocardiogram (ECG) sensors andlor
positional sensors such as electromagnetic field sensors or inertial sensors. These
sensors may be operationally coupled to a data acquisition device, such as an imaging
system, via leads (not shown), for example.
The system 100 may also include a medical imaging system 106 that is in
operative association with the image acquisition device 104. It should be noted that
although the exemplary embodiments illustrated hereinafter are described in the
context of a medical imaging system, other imaging systems and applications such as
industrial imaging systems and non-destructive evaluation and inspection systems,
such as pipeline inspection systems, liquid reactor inspection systems, are also
contemplated. Additionally, the exemplary embodiments illustrated and described
hereinafter may find application in multi-modality imaging systems that employ
ultrasound imaging in conjunction with other imaging modalities, position-tracking
systems or other sensor systems. For example, the multi-modality imaging system
may include a positron emission tomography (PET) imaging system-ultrasound
imaging system. Furthermore, it should be noted that although the exemplary
embodiments illustrated hereinafter are described in the context of a medical imaging
system, such as an ultrasound imaging system, use of other imaging systems, such as,
but not limited to, a computed tomography (CT) imaging system, a contrast enhanced
ultrasound imaging system, an X-ray imaging system, an optical imaging system, a
positron emission tomography (PET) imaging system, a magnetic resonance (MR)
imaging system and other imaging systems is also contemplated in accordance with
aspects of the present technique.
As noted hereinabove, in a presently contemplated configuration, the
medical imaging system 106 may include an ultrasound imaging system. The medical
imaging system 106 may include an acquisition subsystem 108 and a processing
subsystem 110, in one embodiment. Further, the acquisition subsystem 108 of the
medical imaging system 106 is configured to acquire image data representative of one
or more anatomical regions of interest in the fetus via the image acquisition device
104, in one embodiment. For example, the acquired image data may include a
plurality of 2D image frames or slices. Additionally, the image data acquired from
the fetus may then be processed by the processing subsystem 110.
According to aspects of the present technique, the image data acquired
andfor processed by the medical imaging system 106 may be employed to aid a
clinician in identifying an optimal image frame for performing measurements. In one
example, the system 100 may be configured to aid the clinician in the selection of the
optimal image frame by providing an indicator that is representative of a quality of the
current image frame. In certain embodiments, the processing subsystem 110 may be
further coupled to a storage system, such as the data repository 114, where the data
repository 1 14 is configured to store the acquired image data.
Furthermore, in accordance with exemplary aspects of the present
technique, the processing subsystem 110 may include a rating platform 112 that is
configured to aid in the automated identification of the optimal image frame
corresponding to an anatomical region of interest. However, in certain embodiments,
the rating platform 112 may also be configured to aid in the identification of the
optimal image may entail manual intervention. More particularly, the rating platform
112 may be configured to generate a quality metric or score that is representative of
the quality of the current image frame.
In accordance with exemplary aspects of the present technique, the rating
platform 112 is configured to generate a quality metric corresponding to a 2D image
frame that corresponds to an anatomical region of interest such that the quality metric
conforms to clinical guidelines that are prescribed to observe the anatomical region of
interest. For example, for imaging the heart, the clinical guidelines to acquire a good
quality Parasternal Long Axis View (PLAX) may prescribe that features of interest
such as the pericardium, the mitral valve and the septum be visible in the acquired
image frame. Moreover, the quality metric may be generated such that the quality
metric is representative of a functionally optimal image frame that allows a correct
measurement and/or inference to be made. Furthermore, it is desirable that the
indicator so generated and communicated to the clinician should be visually
acceptable to the clinician. Additionally, rating platform 112 is further configured to
communicate the generated quality metric to the clinician or the system 100, thereby
aiding the clinician and/or the system 100 in selecting the optimal image frame for
performing measurements. In one embodiment, the system 100 and more particularly,
the rating platform 112 may be configured to provide feedback to the system 100
and/or the clinician in the form of an indicator. The indicator is generally indicative
of the computed quality metric. Also, the terms quality metric and score may be used
interchangeably.
As previously noted, the rating platform 112 may be configured to facilitate
the identification of an optimal image corresponding to the anatomical region of
interest employing the images acquired via the medical imaging system 106 and will
be described in greater detail with reference to FIGS. 2-13. It may be noted that the
anatomical region of interest may include any anatomy that can be imaged. For
example, the anatomical region of interest may include the heart, and fetal features
like the femur, the head, and the like. Also, the anatomical region of interest may
include the heart in an adult patient, for example. Although the present technique is
described in terms of identifying the optimal image frame corresponding to the
anatomical region of interest in the fetus, it may be noted that use of the present
technique for the determination of an optimal image frame corresponding to other
anatomical regions of interest or other objects of interest is also envisaged.
Further, as illustrated in FIG. 1, the medical imaging system 106 may
include a display 1 16 and a user interface 1 18. In certain embodiments, such as in a
touch screen, the display 1 16 and the user interface 118 may overlap. Also, in some
embodiments, the display 1 16 and the user interface 1 18 may include a common area.
In accordance with aspects of the present technique, the display 116 of the medical
imaging system 106 may be configured to display an image generated by the medical
imaging system 106 based on the acquired image data. Additionally, in accordance
with further aspects of the present technique, the optimal image frame identified by
the rating platform 112 may be visualized on the display 116. Moreover, the quality
metric generated by the rating platform 112 may also be visualized on the display
1 16. In one embodiment, the indicator that is representative of the quality metric may
be overlaid on the corresponding image frame visualized on the display 116. For
example, the generated indicator may be overlaid on or about the image visualized on
the display 1 16.
In addition, the user interface 11 8 of the medical imaging system 106 may
include a human interface device (not shown) configured to aid the clinician in
manipulating image data displayed on the display 116. The human interface device
may include a mouse-type device, a trackball, a joystick, a stylus, or a touch screen
configured to facilitate the clinician to identify the one or more regions of interest
requiring therapy. However, as will be appreciated, other human interface devices,
such as, but not limited to, a touch screen, may also be employed. Furthermore, in
accordance with aspects of the present technique, the user interface 118 may be
configured to aid the clinician in navigating through the images acquired by the
medical imaging system 106. Additionally, the user interface 118 may also be
configured to aid in manipulating and/or organizing the displayed images and/or
generated indicators displayed on the display 1 16.
Turning now to FIG. 2, a block diagram 200 of one embodiment of the
diagnostic system 100 of FIG. 1 is depicted. As previously noted with reference to
FIG. 1, the acquisition subsystem 108 (see FIG. 1) is configured to aid in the
acquisition of image data from the fetus in the patient 102 (see FIG. 1). Accordingly,
one or more image data sets representative of the patient 102 may be acquired by the
acquisition subsystem 108. In certain embodiments, the one or more image data sets
may include ultrasound data 202. It may be noted that the ultrasound images 202 may
be representative of an anatomical region in the fetus 102. For instance, in the
example illustrated in FIG. 2, the ultrasound images 202 may include image data
representative of the fetus or other patients. As previously noted, the ultrasound
image data set 202 may include two-dimensional ultrasound image frames, in one
example. Also, may include cine loops, where the cine loops include 2D image
frames acquired over time t.
Furthermore, the image data acquired by the acquisition subsystem 108
may be stored in the data repository 114 (see FIG. 1). In certain embodiments, the
data repository 114 may include a local database. The rating platform 112 (see FIG.
1) may then access these images, such as the ultrasound image data set 202, from the
local database 114. Alternatively, the ultrasound image data set 202 may be obtained
by the acquisition subsystem 108 from an archival site, a database, or an optical data
storage article. For example, the acquisition subsystem 108 may be configured to
acquire images stored in the optical data storage article. It may be noted that the
optical data storage article may be an optical storage medium, such as a compact disc
(CD), a digital versatile disc (DVD), multi-layer structures, such as DVD-5 or DVD-
9, multi-sided structures, such as DVD-10 or DVD-18, a high definition digital
versatile disc (HD-DVD), a Blu-ray disc, a near field optical storage disc, a
holographic storage medium, or another like volumetric optical storage medium, such
as, for example, two-photon or multi-photon absorption storage format. Further, the
ultrasound image data set 202 so acquired by the acquisition subsystem 108 may be
stored locally on the medical imaging system 106 (see FIG. 1). The ultrasound image
data set 202 may be stored in the local database 114, for example.
Also, in the embodiments illustrated in FIGS. 1-2, the processing subsystem
1 10 is shown as including the rating platform 1 12, where the rating platform 1 12 is
configured to aid in the identification of an optimal image frame employing the
acquired ultrasound image data set 202, as previously described. However, in certain
embodiments, the rating platform 112 may also be used as a standalone module that is
physically separate from the processing subsystem 110 and the medical imaging
system 106. By way of example, the rating platform 112 may be operationally
coupled to the medical imaging system 106 and configured to aid in identification of
the optimal image frame corresponding to the anatomical region in the fetus 102 using
the acquired ultrasound images 202.
In one embodiment, the rating platform 112 may include a feature
extraction module 204, a quality metric generator module 206, and an image frame
selector module 208. It may be noted that although the configuration of FIG. 2
depicts the rating platform 112 as including the feature extraction module 204, the
score generator module 206, the image frame selector module 208 and the feedback
module 2 10, fewer or more number of such modules may be used.
The rating platform 1 12 may also include a feedback module 2 10, in certain
embodiments. In accordance with aspects of the present technique, the feature
extraction module 204 may be configured to process the acquired image frames 202
to extract one or more features of interest based upon the selected anatomical region
of interest. For example, while imaging the fetal head, the feature extraction module
204 may be configured to extract an outline of the fetal head, if present, from the
acquired image frames.
As will be appreciated, while scanning, it is desirable to aid the clinician in
determining if a current 2D image frame is representative of an image frame is
optimal to make a measurement. In accordance with aspects of the present technique,
the rating platform 112 and in particular the quality metric generator module 206 is
configured to generate a metric or score for the viability of the current image frame
towards that end. Accordingly, the quality metric generator module 206 may be
configured to compute a metric corresponding to the image frames based upon a
quality of the image frames. As previously noted, the quality metric may be
representative of a closeness of fit of the image frame to a predefined or determined
model. To that end, the quality metric generator module 206 may be configured to
retrieve a corresponding model from a model database 2 14 and compare the current
image frame with the associated model to generate the quality metric. For example, if
the anatomical region of interest includes the fetal head, the quality metric generator
module 206 may be configured to retrieve a determined model of the fetal head from
the model database 214 and compare a current image frame with the retrieved model
to generate the quality metric.
With continuing reference to FIG. 2, the image frame selector module 208
may be configured to aid in selecting one or more image frames from the plurality of
image frames 202. In one example, the image frame selector module 208 may
configured to verify if a current image frame has an acceptable quality based on
clinical guidelines and visual acceptability of the clinician and/or the system 100. It
may be noted that the clinical guidelines may be obtained from the clinical guidelines
database 212, in one example. If the current image frame does not meet the
guidelines of acceptable view, then adjustments may be made to a position of a probe,
such as the probe 104, to acquire other image frames.
As noted hereinabove, the quality metric generator module 206 is
configured to generate a metric that is representative of the viability of the current
image frame as the optimal image frame. Accordingly, it is desirable to provide a
feedback to the clinician and/or the system 100 that is representative of the quality
metric. The feedback module 210 is configured to provide a feedback that is
symbolic of the quality metric to the clinician and/or the system 100. The symbolic
feedback may include a display, in one embodiment. The display may be a color bar,
a pie chart, a number, and the like that denote the quality of the image frame.
Moreover, the feedback may be an audio feedback andlor an audio-visual feedback.
In one example, the audio feedback may include one or more beeps or a voice in a
language of choice. Additionally, feedback may be an 'auto-freeze' of the image
frame. Furthermore, once the optimal frame is identified, an automated measurement
may be triggered.
The working of the rating platform 112, and the working of the feature
extraction module 204, the score generator module 206, the image frame selector
module 208 and the feedback module 210, in particular, may be better understood
with reference to the exemplary logic depicted in FIG. 3. Turning now to FIG. 3, a
flow chart of exemplary logic 300 for a method for identifying an optimal image
frame corresponding to an anatomical region of interest in the fetus is illustrated. It
may be noted that the method of FIG. 3 is described in terms of the various
components of FIGS. 1-2.
The method 300 may be described in a general context of computer
executable instructions. Generally, computer executable instructions may include
routines, programs, objects, components, data structures, procedures, modules,
functions, and the like that perform particular functions or implement particular
abstract data types. In certain embodiments, the computer executable instructions
may be located in computer storage media, such as a memory, local to an imaging
system 106 (see FIG. 1) and in operative association with a processing subsystem. In
certain other embodiments, the computer executable instructions may be located in
computer storage media, such as memory storage devices, that are removed from the
imaging system. Moreover, the method for automated identification of an optimal
image frame includes a sequence of operations that may be implemented in hardware,
software, or combinations thereof.
As will be appreciated during a typical scan session, an object of interest
such as a patient is positioned for imaging and the clinician attempts to image a
desired anatomical region of interest in the patient. Accordingly, the method starts at
step 302 where a patient is positioned for imaging. Subsequently, at step 304, a mode
of ultrasound imaging may be selected. For example, "obstetric ultrasound" may be
selected as the mode of ultrasound imaging if it is desirable to image a fetus.
Alternatively, "cardiac ultrasound" may be designated as the mode of ultrasound
imaging if it is desirable to image the cardiac region of the patient. It may be noted
that in one embodiment, the clinician may select the mode of imaging, while in
certain other embodiments, the system 100 may be configured to select the mode of
imaging.
Following the selection of the mode of ultrasound imaging, an anatomical
region of interest for imaging may be selected, as depicted by step 306. In one
example, the clinician may identify the anatomical region of interest in the fetus to be
imaged, where the anatomical region of interest may include the heart, the head
andlor the femur in the fetus. Subsequent to the selection of the anatomical region of
interest, a probe that is appropriate for imaging the selected anatomical region of
interest may be selected, as generally indicated by step 308.
In accordance with aspects of the present technique, the identification of the
optimal image frame may be performed in alignment with determined clinical
guidelines for imaging the selected anatomical region of interest. Accordingly, at step
310, the determined clinical guidelines corresponding to the selected anatomical
region of interest may be received. As previously noted, the clinical guidelines
corresponding to the anatomical region of interest may be received from the clinical
guidelines database 212 (see FIG. 2). By way of example, the clinical guidelines
associated with imaging the head in the fetus may entail verification of presence of
the head, the midline falx, the thalami and the cavum septum pellucidum (CSP).
Furthermore, as indicated by step 312, one or more images 314
corresponding to the anatomical region of interest in the fetus may be acquired. As
previously noted, the one or more images 3 14 may include 2D image frames. Also, in
certain embodiments, the 2D image frames may include B-mode image frames.
Moreover, the 2D image frames 3 14 may be acquired via use of the probe selected at
step 308 positioned on or about the anatomical region of interest in the fetus. It may
be noted although at this junction in the workflow, the clinician is aware of a current
location of the probe or the anatomical region of interest, however, an image frame
for performing measurements may not be frozen. By way of example, for imaging a
fetal head, a plurality of image frames corresponding to the head region in the fetus
may be acquired in conformance with the clinical guidelines at step 3 10. However, it
is desirable to select andlor freeze an optimal image frame in accordance with the
received clinical guidelines to make any subsequent measurements for diagnosis.
In accordance with exemplary aspects of the present technique, the optimal
image frame may be identified from the acquired plurality of image frames 3 14. As
previously noted, the optimal image frame is representative of an image frame that
may be used to perform measurements. To that end, at step 3 16, one or more features
of interest may be extracted from each acquired image frame 314. Accordingly, the
one or more features of interest may be extracted from a current image frame. It may
be noted that the features of interest correspond to the selected anatomical region of
interest. By way of example, if the anatomical region of interest includes the head of
the fetus, then the features of interest may include the midline falx, the paired thalami,
and/or the cavum septum pellucidum (CSP). In a similar fashion, if the anatomical
region of interest includes the fetal femur, then the features of interest may include the
femur shaft and the thigh skin. The feature extraction module 204 of FIG. 2 may be
employed to extract the one or more features of interest. Step 3 16 will be described in
greater detail with reference to FIGs. 4-12.
Furthermore, at step 318, a score or quality metric representative of a
quality of the current image frame may be generated. In accordance with aspects of
the present technique, the quality metric may be generated based on the anatomical
region of interest. In one embodiment, the quality metric generator module 206 of
FIG. 2 may be employed to generate the quality metric for the current image frame.
The generation of the score corresponding to the current image frame will be
described in greater detail with reference to FIGs. 4-12.
Additionally, at step 320, a check may be carried out to verify if the current
image frame is representative of an acceptable image frame. As used herein, the term
acceptable image frame may be representative of an image frame that is visually
acceptable to the clinician and/or the system 100. It may be noted that in certain
embodiments, the image frame selector module 208 may be configured to verify if the
current image frame is an acceptable image frame may be based on the score
generated at step 318. It may be noted that the image selector module 212 may be
used to identify the optimal image frame. The selection of the optimal image frame
will be described in greater detail with reference to FIGs. 4-12.
At step 320, if it is determined that the current image frame is
representative of an acceptable image frame, an indicator that is symbolic of the
metric or score may be generated and communicated to the clinician, as indicated by
step 322. In one embodiment, the indicator may be a visual indicator, an audio
indicator or both a visual indicator and an audio indicator that denotes the quality of
the current image frame. The visual indicator may include a color bar, a pie chart, a
number, and the like. Also, the audio indicator may include one or more beeps, a
voice in a language of choice, and so on. However, a combination of an audio
indicator and a visual indicator may be employed. In accordance with further aspects
of the present technique, the indicator may entail an 'auto-freeze' of the current image
frame. Furthermore, the indicator may also facilitate an automated measurement
utilizing the optimal image frame. The indicator may be generated and
communicated to the clinician by the feedback module 210 (see FIG. 2), in one
example. Subsequently, an optimal image frame corresponding to the anatomical
region of interest may be identified based on the quality metric andlor the indicator, as
depicted by step 326.
With continuing reference to step 320, if it is determined that the current
image frame is not indicative of an acceptable image frame, then fine adjustments
may be made to a position of the probe, as depicted by step 324 By way of example,
fine adjustments may be made to the probe axis with respect to the fetus. In addition,
at step 324, adjustments to the ultrasound instrument settings may also be made.
Control may then be passed to step 312 and steps 312-324 may be repeated until an
image frame of optimum quality is obtained.
The method of FIG. 3 may be better understood with reference to FIGS. 4-
12. In particular, the process of identification of the optimal image frame is described
with reference to the selection of the optimal image frame for imaging the heart, the
head in the fetus, and the femur in the fetus.
Referring now to FIG. 4, a diagrammatical representation 400 of an optimal
ultrasound image 402 for imaging the fetal head in accordance with the clinical
guidelines corresponding to imaging the fetal head. For identifying the optimal image
frame corresponding to the fetal head, in accordance with the clinical guidelines it
may be desirable to verify the presence of one or more landmarks in the image frame
402. By way of example, the landmarks for imaging the fetal head may include an
outline of the fetal head 404, a midline falx 406, paired thalami 408, and a cavum
septum pellucidum (CSP).
In accordance with aspects of the present technique, a method for
identifying an optimal image frame while imaging a fetal head is presented in FIG. 5.
FIG. 5 is a diagrammatical representation 500 of a method for identifying an optimal
image frame while imaging a fetal head. The method starts at step 502 where the
plurality of image frames 314 (see FIG. 3) may be received. Subsequently, at step
504, the plurality of image frames may be processed to verify for presence of a fetal
head. Particularly, a first subset of image frames that includes the fetal head may be
identified and selected. Also, image frames that do not include the fetal head may be
rejected. In one embodiment, each image frame in the plurality of image frames 3 14
may be processed to identify and segment an elliptical object that may be
representative of the fetal head. Consequent to this processing, the fetal head may be
segmented, thereby localizing the fetal head. Furthermore, the first subset of image
frames that includes the fetal head may be identified.
Once the first subset of image frames that includes the fetal head is
identified, each of the image frames in the first subset may be further processed to
generate a corresponding quality metric. To that end, in accordance with aspects of
the present technique, each of the image frames in the first subset may be processed to
verify presence of a midline falx, as depicted by step 506. Accordingly, image frames
that include the midline falx may be identified and selected, while the image frames
devoid of the midline falx may be rejected. In one embodiment, the presence of the
midline falx in each image frame may be verified by extracting an interior region of
fetal head. Subsequently, an edge detection filter may be applied. In one example,
the edge detection filter may include phase congruency applied on a normalized
image. Moreover, the midline falx may be extracted based on a cost function that
measures the symmetry of the midline falx with respect to the cranium and an
orientation of the midline falx. More particularly, a second subset of image frames
that includes the midline falx may be selected from the first subset of image frames.
This process aids in localizing the image frames, thereby reducing the scan time.
Following the verification of the presence of the midline falx, the second
subset of image frames may be identified. Subsequently, at step 508, the second
subset of image frames may be further processed to verify the presence of other
landmarks. These other landmarks may include the paired thalami and the CSP. In
one example, the presence of other landmarks such as the paired thalami and the CSP
may be verified by comparing the image frames in the second subset of image frames
with a corresponding model. Moreover, in one example, the model may be generated
as an average of shapes andlor appearances of the anatomical region of interest. To
that end, the model may be retrieved from the model database 214 of FIG. 2. In one
example, the model corresponding to the thalami and the CSP may be retrieved.
Subsequently, the current image frame from the second subset of image frames may
be compared with the corresponding model. If the current image frame substantially
matches the corresponding model, then that current image frame may be identified as
including the desired features and may be assigned a relatively high score.
Alternatively, if the current image frame does not substantially match the
corresponding model, then that current image frame may be assigned a relatively
lower score. It may be noted that this process may also be employed to process a cine
loop of image frames.
Consequent to the processing of step 306, a third subset of image frames
may be identified from the second subset. Once the third subset of image frames is
selected based on the presence of the desired landmarks, a quality metric or score
corresponding to each image frame in the third subset may be computed. As
previously noted, the quality metric is representative of a quality of the image frame.
In the present example, the quality metric is representative of the presence of the
landmarks in the image frame of interest. In accordance with aspects of the present
technique, each image frame may be compared with a determined model and the
quality metric may be generated based on this comparison. As previously noted, the
quality metric may be representative of a closeness of fit of the image frame to the
determined model. Subsequently, an image frame having a highest quality metric
may be identified as the optimal image frame. In one embodiment, the image selector
module 208 may be used.
Ensuing the generation of the quality metric, an indicator that is
representative of the quality metric may be communicated to the system 100 or the
clinician to aid in the selection of the optimal image frame. By way of example, the
quality metric may be visually displayed/overlaid on the current image frame in the
form of a quality indicator bar. The value of the quality metric may be manifested in
the form of a color of the quality bar. This process may be repeated for each image
frame in the third subset of image frames. Reference numeral 510 is generally
representative of a quality metric generated consequent to the processing of steps 502-
508. In accordance with aspects of the present technique, the quality bar may be
configured to be responsive to the image change based on probe movement. It may
be noted that since only a relatively small subset (third subset) of image frames are
processed to generate the quality metric, the scan time may be minimized, thereby
enhancing the clinical workflow.
As noted hereinabove, in the example where the acquired images include a
plurality of single image frames, a quality metric corresponding to each image frame
is generated. However, in certain embodiments, the acquired images may include a
sequence of image frames or a cine loop of image frames. In this situation, a quality
metric corresponding to each image frame in the cine loop may be generated. In
accordance with aspects of the present technique, frame clustering may be employed
to aid in identifying the optimal image frame. By way of example, in the case of cine
loops, the quality metric may incorporate temporal information. To that end, a cluster
of neighboring image frames of the current image frame may be identified.
Subsequently, a cluster score corresponding to each image frame may be generated.
In one example, the cluster score associated with each image frame may be dependent
on the quality metrics corresponding to the neighboring image frames. Consequently,
the quality metric associated with each image frame is weighted by the quality of the
neighboring frames. It may be noted that if the quality metric corresponding to the
neighboring image frames of the current image frame is high, higher is the probability
for the current image frame to be representative of the optimal image frame.
An indicator representative of the quality metric may be generated and
communicated by the system 100, thereby aiding in the selection of the optimal image
frame during an imaging session. In accordance with aspects of the present
technique, the indicator may includ; a visual indicator such as, but not limited to,
quality bar, a pie chart, a numeric value and the like anlor an audio indicator in the
form of a voice, a sound and the like. In one example, the indicator may be overlaid
on the current image frame.
FIG. 6 is a diagrammatical representation 600 of an output of the method
for identifying an optimal image frame corresponding to the fetal head. FIG. 6(a) is
representative of a first image frame 602 of the fetal head. Also, in this example, a
quality metric representative of a quality of the first image frame is represented in the
form of a quality bar 604 that is superimposed on the first image frame 602. This
quality bar 604 is indicative of the fact that the first image frame 602 may not be
representative of an optimal image frame and it may be desirable to acquire other
image frames.
In FIG. 6(b), a second image frame 606 of the fetal head is represented.
Here again, an indicator of the quality metric corresponding to the second image
frame 606 is represented in the form of a quality bar 608 that is superimposed on the
second image frame 606. In the example of FIG. 6(b), the quality bar 608 indicates
that this image frame 606 may be representative of an optimal image frame. In the
example of FIG. 6, the quality indicators 604, 608 have a horizontal orientation and
are superimposed along a lower border of the image frames 602, 606. However, the
quality bars 604, 608 may be superimposed at other convenient locations.
Accordingly, based on the feedback provided by the indicators 604, 608, the clinician
or the system 100 may decide if it is desirable to acquire more image frames of the
fetal head, thereby reducing scan time and enhancing the imaging workflow.
In the examples of FIGS. 6(a) and 6(b), the quality bars 604, 608 may be
color quality bars. One or more colors may be used in the quality bar to represent the
quality of the image frame. By way of example, the quality bar 604 of FIG. 6(a) may
be a smaller red bar, while the quality bar 608 of FIG. 6(b) may be a longer bar with a
green color. Accordingly, it may be desirable to select the image frame 606 that
corresponds to the longer color quality bar with green color 608 as the optimal image
frame. It may be noted that the workflow to provide the feedback described with
reference to F1G.s 5-6 entails use of the steps 504, 506, 508 working in a serial
fashion to down select the number of image frames, thereby enhancing the ease of
identifying the optimal image frame.
In accordance with further aspects of the present technique, while imaging
the heart, an optimal image frame may be identified. FIG. 7 is a diagrammatical
representation 700 of an image frame of the heart. In particular, the image frame of
FIG. 7 is representative of a Parasternal Long Axis (PLAX) view of the heart. It may
be noted that the in accordance with aspects of the present technique, the method for
identifying an optimal image frame corresponding to the heart may also find
application in the imaging of a fetal heart or the heart of a child.
Reference numeral 702 is generally representative of a PLAX view
corresponding to the heart. It may be noted that at the right scan plane and with
optimal ultrasound instrument settings, it is desirable to identifylverify the presence of
one or more anatomical landmarks. These anatomical landmarks may include the
pericardium 704, the posterior wall 706, the right ventricular outflow tract 708, the
septum 710, the aortic valve 712, the left ventricle 714, the mitral valve 7 16, the left
atrium 7 18, the descending aorta 720, and the like.
Turning now to FIG. 8, a diagrammatical representation 800 of a method
for identifying an optimal image frame corresponding to the heart is presented. It
may be noted, the method is configured to automatically determine a quality of a
plurality of image frames corresponding to the heart. In one example, the plurality of
image frames may include Parasternal Long Axis (PLAX) B-mode echocardiograms.
It may be noted, that with optimal instrument settings, it may be desirable
to verify that the long axis of the left ventricle 714 is oriented horizontally in a
standard PLAX view (see FIG. 7). Additionally, it is also desirable that the posterior
wall 706, the pericardium 704 and the septum 710 are substantially parallel to each
other. Any deviation from this may be attributed to an incorrect scan plane or suboptimal
instrument settings. For example, a poor quality image may be due to suboptimal
instrument settings such as gain. Also, non-parallel septum 710 and
pericardium 704 may be indicative of the fact that the scan plane failed to pass
through the center of left ventricle 714. Also, in another example, a missing
pericardium may complicate the measurement of the thickness of posterior wall 704
and diagnosis of pericardial effusion.
The method starts at step 802, where an image frame 804 representative of
the heart is received. As noted hereinabove, in one example, the received image
frame 802 may be representative of a PLAX view. According to aspects of the
present technique, the method for identifying the optimal image frame entails
verifying, in real-time, the presence of one or more features of interest. In one
example, the features of interest may include tube-like structures corresponding to the
septum 710, the mitral valve 716 and the pericardium 704. It may be noted that if the
three features of interest such as the septum 710, the mitral valve 716, and the
pericardium 704 are visible in an image frame, that image frame may be assumed to
have a desired quality. Accordingly, the image frame 804 may be processed to
enhance the contrast of the features of interest, as depicted by step 806. In one
embodiment, the image frame 804 may be processed via a Frangi vesselness filter to
enhance the contrast of the features of interest. Particularly, the image frame 804 may
be filtered using the Frangi vesselness filter to mitigate any intensity inhomogeneity
to generate an intermediate image such as a vesselness image frame 808. As will be
appreciated, the Frangi vesselness filter is a vessel enhancement filter that is used to
enhance the contrast of tubular structures with respect to the background. The
intensity inhomogeneity is substantially reduced in the vesselness image frame 808.
Subsequently, at step 810, the vesselness image frame 808 may be
processed to generate a binary image 812. It may be noted that in the vesselness
image frame 808, in addition to the tubular structures of interest, there may exist
regions of near-field haze and boundary artifacts. Therefore, it is desirable delete any
undesirable regions fiom the vesselness image frame 808. To that end, the vesselness
image frame 808 may be thresholded to generate the binary image 812. This binary
image 8 12 may include the three features of interest, such as the septum 71 0, the
mitral valve 714 and the pericardium 704 in addition to the other regions of the heart
and imaging artifacts.
Once the segmented binary image 8 12 is generated, the binary image may
be compared with a determined or predefined model to generate a quality metric that
is indicative of a quality of the binary image 812 corresponding to the current image
frame 804, as depicted by step 814. In one example, the determined or predefined
model may include an atlas that defines the desired areas or features of interest. The
atlas may be generated by manually segmenting the features of interest in PLAX
images, in one embodiment. Alternatively, in certain other embodiments, the atlas
may be generated by obtaining an average representation of the features of interest in
PLAX images. In one example, a shape-based averaging algorithm such as the
Rohlfing and Maurer's Shape based averaging technique may be used to generate the
atlas.
Moreover, in one example, the comparison of the binary image 812 with
the atlas or determined model may be performed based on the Generalized Hough
Transform (GHT). It may be noted that the GHT may be used to detect the presence
of objects of interest in the binary image 812. In the present example, the objects of
interest may include the tube-like structures corresponding to the septum 710, the
mitral valve 716 and the pericardium 704. Furthermore, during a matching phase, in
accordance with aspects of the present technique, it may be desirable to find the most
probable location of the atlas on the binary image 812. For example, a pixel with the
maximum intensity in an accumulator A may be representative of the most probable
location of a reference point. Furthermore, the maximum value of the accumulator A
may be output as the PLAX quality metric (PQM) 81 8. The quality metric 8 18 may
be representative of a closeness of fit of the binary image 812 to the atlas or
determined model.
Additionally, it may be noted that the number of pixels corresponding to
the septum, the mitral valve, and the pericardium in the PLAX atlas is different.
Consequently, the number of votes received from each of these features of interest is
different. In accordance with aspects of the present technique, it is desirable to
appropriately weight the votes received from the septum, the mitral valve and the
pericardium to ensure that the contributions from the features of interest are
comparable. To that end, scalar weights w,, wz and wj may be assigned such that the
maximum contribution of the votes from any of the features of interest does not
exceed a value of 1. Accordingly, the PQM metric 8 18 can vary between a value of 0
and 3. Moreover, in one example, the scalar weights wl, w2 and w3 may be set to an
inverse of the number of pixels corresponding to the septum, the mitral valve and the
pericardium in the PLAX atlas, respectively.
Once the quality metric 8 18 is generated, an indicator of the quality metric
818 may be generated and communicated to the clinician or the system. As
previously noted, the indicator may be in the form of a quality bar that is
superimposed on the current image frame on the display of the imaging system. FIG.
9 is generally representative of a display 900 of the indicator in the form of a quality
bar that is superimposed on the image frames representative of the heart. In this
example, the quality indicator has a vertical orientation and is superimposed along a
right border of the image frames. However, the quality bar may be superimposed at
other convenient locations. It may be noted that the height of the quality bar may be
proportional to the PQM 8 18 computed for an image frame.
In the examples of FIGs. 9(a), 9(b) and 9(c), reference numerals 902, 906
and 910 are representative of a first image frame, a second image frame and a third
image frame. The corresponding quality bars may be represented by reference
numerals 904, 908 and 912 respectively. In this example, the quality bar may be a
color quality bar. As is evident from the three image frames 902, 906, 910, a quality
of the image frames improves from image frame to image frame as indicated by the
quality bars 904, 908, 912. For example, the quality bar 912 of FIG. 9(c) may be a
longer bar with green color and hence may be representative of the optimal image
frame as opposed to the image frames of FIGs. 9(a) and 9(b) based on the
corresponding quality bars. Accordingly, it may be desirable to select the image
frame 910 that corresponds to the longer color quality bar with green color 912 as the
optimal image frame.
Moreover, in accordance with yet another aspect of the present technique, a
method for identifying an optimal image frame while imaging the femur of the fetus is
presented. FIG. 10 is a diagrammatical representation 1000 of an image frame 1002
acquired while imaging a femur of the fetus. It may be noted while imaging the
femur in the fetus, it may be desirable to identify one or more landmarks, such as, but
not limited to sharp corners of a femur shaft 1004 and a surface 1006 that is adjacent
to the femur shaft 1004. In one example, the adjacent surface 1006 may include the
thigh skin.
Referring now to FIG. 11, a diagrammatical representation 1100 of a
method for identifying an optimal image frame while imaging the fetal femur is
depicted. The method starts at step 1102, where an image frame 1104 representative
of the femur shaft 1106 of the fetus may be received. Reference numeral 1108 may
be representative of the thigh skin that is disposed adjacent to the femur shaft 1106.
Furthermore, at step 1102, presence of the femur shaft 1104 may be verified. In one
embodiment, the presence of the femur shaft 1106 in the current image frame 1104
may be detected by verifying if the fetal femur 1106 is disposed such that the fetal
femur 1 106 is substantially horizontal with a tolerance of about 30 degrees.
As previously noted, for imaging the fetal femur it is desirable to identify
an image frame that includes the thigh skin 1108, a visible sharp edge at least at one
of the ends of the femur shaft 1106, and a femur shaft that is substantially horizontal.
In accordance with aspects of the present technique, the method for identifying the
optimal image frame corresponding to the fetal femur may include determining a first
score corresponding to the sharpness of at least one edge of the femur shaft 1106 and
a second score corresponding the thigh skin 1108 and generating a composite quality
metric based on the first and second scores.
To that end, once the presence of the femur shaft 1106 is verified, it may be
desirable to determine sharpness of at least one comer or extremity of the femur shaft
1106. Accordingly, at step 11 10, one or more comers or extremities1 112, 11 14 of the
femur shaft 1106 may be localized. Furthermore, pixels corresponding to the
localized comers or extremities may be clustered.
Subsequently, templates for the corners of the femur shaft 1106 may be
obtained. Furthermore, the templates may be convolved with the identified corners
11 12, 11 14 to generate a first score 11 16 corresponding to the comer sharpness of the
femur shaft 1106. In one example, the current image frame may be multiplied with
the template and all the pixel values may be added to generate the score for comer
sharpness.
Moreover, the presence of a surface adjacent to the femur shaft 1106 may
be verified. In one example, the adjacent surface may include the thigh skin 1108.
Accordingly, at step 11 18, the image frame 1104 may be processed to verify the
presence of the thigh skin 1108. According to aspects of the present technique, an
edge based template may be employed to aid in identifyinglverifying the presence of
the thigh skin 1108. In one embodiment, the edge based template may be based on
the Active Basis technique. Subsequently, current image frame 1104 may be
processed viaJcompared with the edge based template to generate a second score 1120
that is representative of the thigh skin visibility in the image frame 1104. In
accordance with further aspects of the present technique, the first score 11 16 and the
second score 1 120 may be combined to generate a composite quality metric 1 122 that
is representative of the quality of the current image frame 1104 while imaging the
fetal femur.
As described hereinabove, the method for identifying an optimal image
frame while imaging a fetal femur entails computation of a composite score based on
the first and second scores. Alternatively, in certain embodiments, the image frame
1 104 may be processed to verify the presence of the femur shaft 1 106. Also, the
orientation of the femur shaft 1106 may be determined. Subsequently, presence of a
surface adjacent to the femur shaft, for example, the thigh skin 1108 may be verified.
The image frames that include both the femur shaft 1106 and the thigh skin 1108 may
identified. Following the identification of the desired image frames, the image frames
may be processed to localize the extremities of the femur shaft 1106. Furthermore,
pixels corresponding to the localized extremities may be clustered. In addition, the
current image frame may be convolved with a template to generate a quality metric or
score for that image frame. Also, an indicator representative of the quality metric
may be generated and communicated as feedback to aid in the selection of the optimal
image frame.
Here again, an indicator that is representative of the composite quality
metric may be generated and communicated to the system 100 or the clinician to aid
in selecting the optimal image fiarne. FIG. 12 is a diagrammatical representation
1200 of an output of the method for identifying an optimal image frame
corresponding to the fetal femur. FIG. 12(a) is representative of a first image frame
1202 of the fetal femur. Also, in this example, an indicator representative of a quality
of the first image frame 1202 is represented in the form of a quality bar 1204 that is
superimposed on the first image frame 1202. Moreover, in FIG. 12(b), a second
image frame 1206 of the fetal femur is represented. Also, an indicator corresponding
to the quality metric of the second image frame 1206 is represented in the form of a
quality bar 1208 that is superimposed on the second image frame 1206. In this
example, the quality bars 1204, 1208 have a horizontal orientation and are
superimposed along a lower border of the image frames 1202, 1206. However, the
quality bar may be superimposed at other convenient locations. Accordingly, based
on the feedback provided by the indicators 1204, 1208, the clinician or the system 100
may decide if the optimal image fiame and been identified or if it is desirable to
acquire more image frames of the fetal femur, thereby enhancing the imaging
workflow by reducing scan time.
In the examples of FIGS. 12(a) and 12(b), the quality bar may be a color
quality bar. One or more colors may be used in the quality bar to represent the quality
of the image frame. By way of example, the quality bar 1204 of FIG. 12(a) may be a
smaller red bar, while the quality bar 1208 of FIG. 12(b) may be a longer bar with a
green color. Accordingly, it may be desirable to select the image frame 1206 that
corresponds to the longer color quality bar with green color 1208 as the optimal image
frame.
As previously noted with reference to FIG. 1, the medical imaging system
106 may include an ultrasound imaging system. FIG. 13 is a block diagram of an
embodiment of an ultrasound imaging system 1300 depicted in FIG. 1. The
ultrasound system 1300 includes an acquisition subsystem, such as the acquisition
subsystem 108 of FIG. 1 and a processing subsystem, such as the processing
subsystem 1 10 of FIG. 1. The acquisition subsystem 108 may include a transducer
assembly 1306. In addition, the acquisition subsystem 108 includes transmitheceive
switching circuitry 1308, a transmitter 13 10, a receiver 13 12, and a beamformer 13 14.
It may be noted that in certain embodiments, the transducer assembly 1306 is
disposed in the probe 104 (see FIG. 1). Also, in certain embodiments, the transducer
assembly 1306 may include a plurality of transducer elements (not shown) arranged in
a spaced relationship to form a transducer array, such as a one-dimensional or twodimensional
transducer array, for example. Additionally, the transducer assembly
1306 may include an interconnect structure (not shown) configured to facilitate
operatively coupling the transducer array to an external device (not shown), such as,
but not limited to, a cable assembly or associated electronics. In the illustrated
embodiment, the interconnect structure may be configured to couple the transducer
array to the T/R switching circuitry 1308.
The processing subsystem 110 includes a control processor 1316, a
demodulator 1318, an imaging mode processor 1320, a scan converter 1322 and a
display processor 1324. The display processor 1324 is further coupled to a display
monitor 1336, such as the display 116 (see FIG. l), for displaying images. User
interface 1338, such as the user interface area 118 (see FIG. I), interacts with the
control processor 13 16 and the display monitor 1336. The control processor 13 16
may also be coupled to a remote connectivity subsystem 1326 including a remote
connectivity interface 1328 and a web server 1330. The processing subsystem 1 10
may be further coupled to a data repository 1332, such as the data repository 114 of
FIG. 1, configured to receive and/or store ultrasound image data. The data repository
1332 interacts with an imaging workstation 1334.
The aforementioned components may be dedicated hardware elements such
as circuit boards with digital signal processors or may be software running on a
general-purpose computer or processor such as a commercial, off-the-shelf personal
computer (PC). The various components may be combined or separated according to
various embodiments of the invention. Thus, those skilled in the art will appreciate
that the present ultrasound imaging system 1300 is provided by way of example, and
the present techniques are in no way limited by the specific system configuration.
In the acquisition subsystem 108, the transducer assembly 1306 is in
contact with the patient 102 (see1306 FIG. 1). The transducer assembly 1306 is
coupled to the transrnitlreceive (TR) switching circuitry 1308. Also, the TR
switching circuitry 1308 is in operative association with an output of transmitter 13 10
and an input of the receiver 13 12. The output of the receiver 13 12 is an input to the
beamformer 13 14. In addition, the beamformer 1314 is further coupled to the input of
the transmitter 13 10 and to the input of the demodulator 13 18. The beamformer 13 14
is also operatively coupled to the control processor 13 16 as shown in FIG. 13.
In the processing subsystem 1 10, the output of demodulator 13 18 is in
operative association with an input of the imaging mode processor 1320.
Additionally, the control processor 13 16 interfaces with the imaging mode processor
1320, the scan converter 1322 and the display processor 1324. An output of imaging
mode processor 1320 is coupled to an input of scan converter 1322. Also, an output
of the scan converter 1322 is operatively coupled to an input of the display processor
1324. The output of display processor 1324 is coupled to the monitor 1336.
Furthermore, the foregoing examples, demonstrations, and process steps
such as those that may be performed by the system may be implemented by suitable
code on a processor-based system, such as a general-purpose or special-purpose
computer. It should also be noted that different implementations of the present
technique may perform some or all of the steps described herein in different orders or
substantially concurrently, that is, in parallel. Furthermore, the functions may be
implemented in a variety of programming languages, including but not limited to C++
or Java. Such code may be stored or adapted for storage on one or more tangible,
machine readable media, such as on data repository chips, local or remote hard disks,
optical disks (that is, CDs or DVDs), memory or other media, which may be accessed
by a processor-based system to execute the stored code. Note that the tangible media
may comprise paper or another suitable medium upon which the instructions are
printed. For instance, the instructions may be electronically captured via optical
scanning of the paper or other medium, then compiled, interpreted or otherwise
processed in a suitable manner if necessary, and then stored in the data repository or
memory.
The various systems and methods for automated identification of an
optimal image frame for ultrasound imaging described hereinabove provides a
framework for robust determination of an optimal image frame for imaging a desired
anatomical region of interest, such as the heart, the fetal head, andlor the fetal femur.
Moreover, the various systems and methods are automated, thereby circumventing the
need for manual intervention. Consequently, dependency on highly trained
professionals is reduced. In addition, the scan time may be dramatically minimized
when compared to manual image acquisition and measurement, thereby increasing the
throughput. Both image acquisition and measurement phases are tied together into
one automated process. Moreover, these methods and systems may be configured to
process image frames acquired from low-cost imaging systems, thereby addressing
the needs of the rural markets. By way of example, for rural setups with high
volumes of fetal scanning, these systems and methods aid in decreasing the net scan
time, thereby enhancing handling of higher volumes.
Furthermore, the visual and/or audio indicators introduced into the current
workflow of ultrasound scanning enhance ease of use of the system by a visual and/or
audio cue as a quality indicator. Less experienced clinicians greatly benefit from
these features since the system provides feedback corresponding to the quality of the
acquisition in real-time. In addition, the more experienced clinicians may use these
indicators as a way to reconfirm their findings. Moreover, for clinicians with heavy
workloads, the increased automation and assistance provided by the systems enables
the clinicians to perform a greater volume of fetal scanning. Also, the clinical
workflow is enhanced due to the reduced number of button clicks. The feedback
provided may also be used to train new users and assist less skilled users in their
practice.
While only certain features of the disclosure have been illustrated and
described herein, many modifications and changes will occur to those skilled in the
art. It is, therefore, to be understood that the appended claims are intended to cover
all such modifications and changes as fall within the true spirit of the disclosure.
ELEMENT LIST
100 System for automated identification of an optimal image frame for
ultrasound imaging
102 Patient
104 Image data acquisition device
106 Medical imaging system
108 Acquisition subsystem
1 10 Processing subsystem
1 12 Rating platform
1 14 Data repository
116 Display
1 18 User interface
200 System for automated ultrasound based tracking of pathologies
202 Ultrasound images
204 Feature extraction module
206 Score generator module
208 Image frame selector module
2 10 Feedback module
2 12 Clinical guidelines database
2 14 Normal model database
300 Flowchart depicting method for automated identification of an optimal
image frame for ultrasound imaging
302-326 Steps for performing the method automated identification of an
optimal image frame for ultrasound imaging
400 Diagrammatic illustration of fetal head image frame
402 Image frame
404 Fetal head
406 Midline falx
408 Thalami
4 10 Cavum septum pellucidum
500 Flowchart depicting method for automated identification of an optimal
image frame for fetal head ultrasound imaging
502-510 Steps for performing the method automated identification of an
optimal image frame for fetal head ultrasound imaging
600 Diagrammatic illustration of fetal head image frames with quality bar
indicator
602 First image frame
604 Quality bar indicator
606 Second image frame
608 Quality bar indicator
700 Diagrammatic illustration of fetal head image frame
702 Cardiac image frame
704 Pericardium
706 Posterior wall
708 Right ventricular outflow tract
710 Septum
712 Aortic valve
7 14 Left ventricle
7 16 Mitral valve
7 18 Left atrium
720 Descending aorta
800 Flowchart depicting method for automated identification of an optimal
image frame for cardiac ultrasound imaging
802-818 Steps for performing the method automated identification of an
optimal image frame for fetal head ultrasound imaging
900 Diagrammatic illustration of fetal head image frames with quality bar
indicator
902 First image frame
904 Quality bar indicator
906 Second image frame
908 Quality bar indicator
9 10 Third image frame
9 12 Quality bar indicator
1000 Diagrammatic illustration of fetal head image frame
1002 Fetal femur image frame
1004 Femur shaft
1006 Thigh skin
1008
1010
1 100 Flowchart depicting method for automated identification of an optimal
image frame for fetal femur ultrasound imaging
1102-1 120 Steps for performing the method automated identification of an
optimal image frame for fetal femur ultrasound imaging
1200 Diagrammatic illustration of fetal head image frames with quality bar
indicator
1202 First image frame
1204 Quality bar indicator
1206 Second image frame
1208 Quality bar indicator
1300 Medical imaging system
1306 Transducer assembly
1308 T/R switching circuitry
1 3 10 Transmitter
1 3 12 Receiver
13 14 Beamformer
13 16 Control processor
13 18 Demodulator
1320 Image mode processor
1322 Scan converter
1324 Display processor
1326 Remote connectivity subsystem
1 328 Interface
1330 Web server
1332 Data repository
1334 Imaging workstation
1336 Display
1338 User interface

WE CLAIM:
1. A method for identifying an optimal image frame, comprising:
receiving a selection of an anatomical region of interest in an object of
interest;
obtaining a plurality of image frames corresponding to the selected anatomical
region of interest;
determining a real-time indicator corresponding to the plurality of acquired
image frames, wherein the real-time indicator is representative of quality of an image
frame; and
^1^ communicating the real-time indicator to aid in selecting an optimal image
fi"ame.
2. The method of claim 1, wherein the object of interest comprises a
patient, a fetus, or a test object.
3. The method of claim 1, further comprising computing a quality metric
representative of a quality of the plurality of image frames.
T ^ 4. The method of claim 3, further comprising identifying a cluster of
neighboring image frames of the image frame that comprises a feature of interest
based on the quality metric.
5. The method of claim 1, wherein the anatomical region of interest
comprises a heart, a fetal head, a fetal femur, or combinations thereof
36
6. The method of claim 1, further comprising:
identifying presence of a fetal head in the plurality of image frames to form a
first subset of image frames, wherein the image frames in the first subset of images
frames comprise the fetal head; and
identifying presence of a midline falx in the first subset of image frames to
form a second subset of image frames, wherein the image frames in the second subset
of images frames comprise the at least the midline falx.
^ f 7. The method of claim 6, further comprising identifying presence of a
paired thalami and a cavum septum pellucidum in the second subset of image frames
to form a third subset of image frames, wherein the image frames in the third subset
of images frames comprise at least the paired thalami and the cavum septum
pellucidum.
8. The method of claim 7, wherein identifying presence of a paired
thalami and a cavum septum pellucidum in the second subset of image frames to form
a third subset of image frames comprises comparing the image frames in the second
subset with a determined model, and wherein the determined model is representative
of an average of shapes, appearances, or combinations thereof of the anatomical
^ 1 region of interest.
9. The method of claim 8, further comprising determining a closeness of
fit based on a determined model to generate a quality metric.
37
10. The method of claim 1, further comprising enhancing a contrast of one
or more features of interest in a cardiac image frame, wherein the cardiac image frame
comprises a Parasternal Long Axis (PLAX) view image frame.
11. The method of claim 10, further comprising filtering the cardiac image
frame with enhanced contrast to generate a binary image comprising the one or more
features of interest.
12. The method of claim 11, fiirther comprising comparing the binary
^ ^ image with a determined model to determine a closeness of fit of the binary image to
the determined model to generate a quality metric.
13. The method of claim 12, wherein the determined model comprises an
atlas corresponding to the feature of interest, and wherein the atlas is generated based
on a shape-based averaging.
14. The method of claim 13, wherein comparing the binary image with the
atlas based on a Generalized Hough Transform.
15. The method of claim 13, wherein comparing the binary image
comprises determining a most probable location of the atlas on the binary image.
16. The method of claim 15, further comprising determining a PLAX
quality metric based on a closeness of fit of the atlas to the binary image.
38
17. The method of claim 1, further comprising:
identifying a femur shaft using the image frame; and
determining an orientation of the femur shaft.
18. The method of claim 17, fiirther comprising detecting an adjacent
surface of the femur shaft based on an edge based template.
^ P 19. The method of claim 18, fiirther comprising:
localizing extremities of the femur shaft;
clustering pixels corresponding to the localized extremities; and
convolving the image frame with comer features of a template to generate a
metric for comer sharpness.
20. The method of claim 19, further comprising generating a quality metric
representative of a quality of the image frame.
21. The method of claim 1, wherein communicating the real-time indicator
to the clinician comprises visualizing the real-time indicator on a display, playing an
audio-indicator of the real-time indicator, or a combination thereof
22. A system, comprising:
39
a rating platform, configured to:
receive a selection of an anatomical region of interest in an object of
interest;
obtain a plurality of image frames corresponding to the selected
anatomical region of interest;
determine a real-time indicator corresponding to the plurality of
acquired image frames, wherein the real-time indicator is representative of
quality of an image frame; and
communicate the real-time indicator to aid in selecting an optimal
^ ^ image frame.
23. A computer-readable non-transitory media storing computer
executable code to perform the method of:
receiving a selection of an anatomical region of interest in an object of
interest;
obtaining a plurality of image frames corresponding to the selected anatomical
region of interest;
determining a real-time indicator corresponding to the plurality of acquired
image frames, wherein the real-time indicator is representative of quality of an image
^ ^ frame; and
communicating the real-time indicator to aid in selecting an optimal image
frame.
24. An imaging system, the system comprising:
40
an acquisition subsystem configured to obtain a plurality of image frames
corresponding to a region of interest in an object of interest;
a processing subsystem in operative association with the acquisition
subsystem and comprising a rating platform, wherein the rating platform comprises:
a feature extraction module configured to extract one or more features
of interest from the plurality of image frames;
a quality metric generator module configured to generate a quality
metric corresponding to one or more image frames in the plurality of image
frames;
r^ an image frame selector module configured to select one or more
image frames based on the quality metric; and
a feedback module configured to generate and communicate in realtime
an indicator representative of the quality metric.
25. The system of claim 24, wherein the system comprises an ulfrasound
imaging system, a contrast enhanced ultrasound imaging system, an optical imaging
system, an X-ray imaging system, a computed tomography imaging system, a
magnetic resonance imaging system, a computed tomography imaging system, a
positron emission tomography imaging system, or combinations thereof