Patent Application
20110317897
The subject matter disclosed herein relates generally to automated localization techniques and, in particular to a method and apparatus for automated localization of a moving structure internal to a body.
Localizing a moving structure internal to a body has several applications for various medical procedures related to the moving structure and the general surrounding region. For example, localization of tip of cardiac valves may be used for various cardiac biometric measurements such as left ventricle (LV) size and thickness of interventricular septum in diastole (IVSd). Measurement protocol specifies that measurement of thickness of IVSd should be carried out along a measurement line that is orthogonal to the centerline of the septum region and passing through the mitral valve tip. LV size along with thickness of IVSd is one of the main indicators of cardiac hypertrophy. Similarly, localization of the tip of aortic valve in PLAX view ultrasound images is used to measure left atrium (LA). Further, the tip of the opened tricuspid valve is used as landmark while measuring Right Ventricle (RV) size. Other applications, for example include Doppler measurements and atrioventricular valve (AV) plane displacement. Generally, for Doppler measurements, the gate locations are mitral valve and tricuspid valve in apical 4 chamber (4CH) view. For AV plane displacement, typically the displacement contour is anchored at the mitral valve.
However, localization of a moving structure internal to a body in a consistent and repeatable manner remains a challenge. Consistent and repeatable localization is difficult due to fast movement of the moving structures. Conventional techniques for localization of moving structures generally use training data or supervised learning and such techniques are limited in their application.
Therefore, there is a need in the art for an improved method and apparatus for localizing a moving structure internal to a body.
A method for automated localization of at least one moving structure internal to a body is disclosed. The method comprises acquiring a sequence of images of a region of the body, the region the body including the at least one moving structure, computing a motion map of a current frame of the sequence of images, identifying a plurality candidate pixels comprising the motion map, clustering the plurality of candidate pixels into at least one cluster, the at least one cluster corresponding to the at least one moving structure and computing a representative point of each of the at least one cluster, each of the representative point representing the location of the at least one moving structure. The plurality of candidate pixels identified correspond to motion of the at least one moving structure.
An apparatus for automated localization of at least one moving structure internal to a body is disclosed. The apparatus comprises an image acquiring section for capturing a sequence of images of a region of the body, the region the body including the at least one moving structure, a processing unit configured to automatically localize the at least one moving structure, a memory device coupled to the processing section and the image acquiring section to store and provide access to the sequence of images. The processing unit is configured to compute a motion map of a current frame of the sequence of images, identify a plurality of candidate pixels comprising the motion map, the plurality of candidate pixels corresponding to motion of the at least one moving structure, cluster the plurality of candidate pixels into at least one cluster, the at least one cluster corresponding to the at least one moving structure and compute a representative point of each of the at least one cluster, the representative point representing the location of the at least one moving structure.
These and other features, aspects, and advantages of the present invention 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:
The imaging section 110 is an imaging device that provides images of a region of the body, such as an ultrasound device and a fluoroscopic imaging device among others. The region of the body includes the at least one moving structure. The imaging section 110 is used to acquire a sequence of images 112 of the region of the body. The sequence of images 112 comprises images having temporal resolution that allows current position of the moving structure to be captured, such as in real time ultrasound images. The sequence of images 112 acquired by the imaging section 110 is stored in memory 120 and is accessible to the processing section 130. While memory 120 is depicted as separate from the processing section 130, memory, such as RAM, ROM, flash or disc, is part of the processing section in certain embodiments. The processing section 130 is configured to localize at least one moving structure internal to the body by a method, for example, a method 200 with reference to
The architecture or form factor of the apparatus 100 in one example is designed such that the apparatus 100 is large and mountable on a cart for portability, the size of a desktop or the size of a hand-held device such as a mobile phone. In one embodiment the apparatus 100 is a hand-held device, and the automated method of localization of at least one moving structure is advantageously exploited, as the hand-held device provides extreme flexibility and wide applications of the localization of the at least one moving structure. The apparatus 100 in one embodiment is packaged as a hand-held device through judicious selection of functions and features and the efficient use of integrated circuits and real-time imaging technology. As an example and not as a limitation, of judicious use of integrated circuits, a controller of an ultrasound imaging device is a RISC (reduced instruction set controller) processor in the apparatus 100 packaged as a hand-held device and using ultrasound as the real-time imaging technique. Various probes can be coupled to the imaging apparatus 100 thereby providing a variety of imaging applications.
At step 206, a motion map for a current frame is computed. In one example, computing the motion map involves computing frame differences between the current frame (x) and another frame, which is a frame different from the current frame. The other frame may, for example be a frame x−z or a frame x+z, where z is a positive integer. For example, if z=1, and the other frame is the x−z frame, and the frame difference is computed based the difference between the current frame (x) and a previous frame (x−1). Similarly, if for example, z=1, and the other frame is the x+z frame, the frame difference is computed based on the difference between the current frame (x) and the other frame (x+1). Computing the motion map in one example is based on frame differences and provides economy in computation time that is suitable for automated localization of the at least one moving structure internal to the body.
Further, at step 207, a motion map for each frame in the sequence of acquired images is computed. According to an embodiment, successive frame differences are computed. The successive frame differences are, for example, computed according to the following representative logic, wherein the example is for illustrative purposes of the processing:
At step 208, multiple candidate pixels for each frame are identified. The number of candidate pixels identified is a predetermined number such as, for example 100 candidate pixels. The number of candidate pixels identified varies according to, for example, the application of the method 200 and provides a suitable number that allows for computational efficiency of the method 200, as will occur readily to one skilled in the art. According to one embodiment, candidate pixels are identified by considering those pixels in the frame that have a high magnitude of frame differences in order to capture locations corresponding to high or more significant motion. Further, according to one embodiment, more than one frame difference is used for identifying candidate pixels because the moving structure is not clearly visible in some frames. Only one frame difference may not yield desirable number of candidate pixels. For example, the candidate pixels are identified considering a curr_prev_FrameDiff and a next_curr_FrameDiff. The curr_prev_FrameDiff for a pixel is the frame difference between a current frame and a previous frame. The next_curr_FrameDiff for a pixel is the frame difference between a current frame and a next frame. Further, in some examples the multiple candidate pixels are pruned and refined using constraints such as region of interest (ROI). According to one embodiment, the multiple candidate pixels are pruned, by way of example and not as a limitation, based on anatomy specific ROI. Those skilled in the art will appreciate that pruning for the multiple candidate pixels, for example, for mitral valve tip is done considering lower half of a PLAX view ultrasound image as ROI.
At step 210, the multiple candidate pixels are clustered into at least one cluster. For example, the multiple candidate pixels are clustered into M clusters, where M is the number of the moving structures. M is specified by the user based on a priori knowledge of the number of moving structures that can be visualized in the sequence of images 112. According to one embodiment, M is specified as 2 by the user for 4CH view ultrasound image of the heart according to a priori standard medical knowledge that the 4CH view ultrasound image allows visualization of two valves. Any known means of clustering such as for example, k-means clustering may be used to cluster the candidate pixels. Those skilled in the art will appreciate that the number of candidate pixels comprising each of the M clusters varies. For example, in k-means clustering, the number of candidate pixels comprising each of the M clusters depends on which candidate pixel belongs to which of the M clusters with the nearest mean.
Subsequently, at step 212, a representative point of each cluster is computed. The representative point is for example a cluster centre, and in some instances can be a pixel or a point between pixels. The cluster centre is computed by using techniques generally known in the art. At step 214, localization of at least one moving structure corresponding to each of the at least one cluster is achieved. The computed representative point represents the location of the at least one moving structure corresponding to each of the at least one cluster.
Furthermore, an optional smoothed location of the moving structure can be processed using smoothing techniques such as temporal smoothing. Temporal smoothing involves computing an average of the representative location of the moving structure from previous frames.
As described herein, in the method 200 of
According to one embodiment the method 200, by way of example and not as a limitation, is used for localization of one moving structure such as, for example a mitral valve tip. The sequence of real-time images acquired at step 202 is for example, parasternal long axis (PLAX) view images in B mode. PLAX view provides a good visualization of the mitral valve. For localizing one moving structure, the method 200 may be simplified by computing the representative point using a median calculation for the multiple candidate pixels at step 212. The method 200, simplified for localization of the mitral valve tip in a PLAX view ultrasound image of the heart allows for a fast real-time implementation of the method 200 and reliable and repeatable localization of the mitral valve tip.
The various embodiments discussed herein provide several advantages. For example, automating localization of at least one moving structure internal to the body provides a reliable, objective, and fully automatic real-time method. Further, automated localization of at least one moving structure internal to the body based on frame differencing approach provides a shorter processing time for the patient. In one example, frame differencing eliminates initializing or shape modeling limitations of conventional localization algorithms.
Furthermore, the embodiments discussed herein achieve the technical effect of automatic localization of at least one moving structure internal to a body, such as tips of the valves of the heart. Automatic localization of valve tips of the heart, for example, facilitates various diagnostic and interventional procedures that use valves as landmarks. According to one example, automatic localization of mitral valve tip facilitates measurement of thickness of IVSd, since measurement of thickness of IVSd is carried out along a measurement line that is orthogonal to the center line of the septum region and passing through the mitral valve tip. According to another example, automatic localization of the tip of the aortic valve facilitates in measuring left atrium (LA), since localization of the tip of aortic valve in PLAX view ultrasound images, is used to measure left atrium (LA). Similarly, automatic localization of the tip of the opened tricuspid valve facilitates in measuring right ventricle size, since the tip of the opened tricuspid valve is used as a landmark while measuring RV size.
Other applications that would be facilitated by automated localization of valves as landmarks, for example, include Doppler measurements and atrioventricular valve (AV) plane displacement. Generally, to acquire a Doppler echocardiogram, a sonographer needs to locate a Doppler gate on the top of a B-mode echocardiogram where the velocity and direction of blood are to be sampled by the Doppler transducer. For example, for Doppler measurement of mitral inflow, the Doppler gate is placed near the tip of the mitral valve. The AV plane displacement in the heart is used as an index of left ventricular systolic function. Automated localization of the mitral valve in apical 4 chamber view facilitates efficient placement of the Doppler-gate to assess valvular regurgitation. Similarly, for AV plane displacement measurements, the displacement contour is generally anchored at the mitral valve. Automated localization of the mitral valve tip facilitates efficient placement of the displacement contour.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which this invention belongs. The terms “first”, “second”, and the like, as used herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Also, the terms “a” and “an” do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item, and the terms “front”, “back”, “bottom”, and/or “top”, unless otherwise noted, are merely used for convenience of description, and are not limited to any one position or spatial orientation. If ranges are disclosed, the endpoints of all ranges directed to the same component or property are inclusive and independently combinable (e.g., ranges of “up to about 25 wt. %, or, more specifically, about 5 wt. % to about 20 wt. %,” is inclusive of the endpoints and all intermediate values of the ranges of “about 5 wt. % to about 25 wt. %,” etc.). The modifier “about” used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (e.g., includes the degree of error associated with measurement of the particular quantity).
While only certain features of the invention 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 invention.
This application is a Continuation-in-Part of U.S. patent application Ser. No. 12/825,755, entitled “Methods and Apparatus for Automated Measuring of the Interventricular Septum Thickness”, (GE Docket No. 242396) filed 29 Jun. 2010, which is herein incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 12825755 | Jun 2010 | US |
| Child | 12889576 | US |
