Abstract:
The identification of preferred seed calculations used to guide the determination of displacement vectors in elasticity imaging may evaluate seeds using a combination of a measure of the similarity of the data of the seed in pre- and post-compression data and continuity of the data in a path in the neighborhood of the seed. This dual evaluation helps avoid downstream error propagation.

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     This invention was made with United States government support awarded by the following agency: 
     NIH CA100373, CA133488 
     The United States government has certain rights to this invention. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates to elasticity imaging including but not limited to strain imaging and in particular to an improved method of determining displacement vectors used to produce such images. 
     Strain imaging produces images revealing the underlying elastic parameters of the material being measured. When used in medicine, strain imaging is analogous to palpation by a physician, that is, the pressing of tissue by the physician to feel differences in elasticity in the underlying structures. 
     In a common form of strain imaging, two separate images are obtained with the measured material in different states of deformation, typically, as deformed by an external force. In ultrasound strain imaging, the ultrasound probe itself may be used to provide this deformation. 
     The two images are analyzed to deduce the amount of displacement in the material at a number of corresponding regions. The gradient in these displacements, determined as a function of the spatial location of the regions, provides strain information generally reflecting the elasticity of the tissue. An example of such strain imaging and a description of techniques for determining displacement of tissue between two images are described in detail in U.S. Pat. No. 6,508,768 and in pending application Ser. No. 12/258,532 filed Oct. 27, 2008 and entitled: Ultrasonic Strain Imaging Device with Selectable Cost-Function, both hereby incorporated by reference. 
     The displacement between corresponding regions of the material in the first and second state of deformation can be determined by identifying a multi-point region (i.e. a reference kernel) in the material in the first state of deformation and moving this kernel within a two- or three-dimensional search region over a search region of the material in the second state of deformation. The displacement vector is determined by the best match between the reference kernel and its overlapping portion onto the search region of the material in the second state of deformation (i.e. the target kernel). The best match may be determined by evaluating a similarity of the data of the reference and target kernels for example, as a sum of the magnitude of differences between individual samples of these two kernels or other similar technique. 
     The matching process as used to determine the relative displacement of the material during deformation is subject to an error termed “peak hopping” in which the reference kernel falsely matches to a target kernel in the second image that represents “non-physical” motion, for example, as a result of random noise dominating the similarity determination. Such peak hopping becomes more acute with small kernels (necessary for high resolution imaging) or when there is substantial tissue displacement. 
     Peak hopping can be reduced by limiting the search region of the kernel based on a priori assumptions about the movement of the tissue, for example, that the trend of displacement in tissue will be continuous over adjacent regions reflecting the continuous nature of the tissue itself. In one implementation of this technique, the location of the search region of a given kernel is guided by the displacement vector of a previously evaluated adjacent kernel. 
     This assumption of continuity in tissue breaks down for many types of tissue where the tissue is inhomogeneous or where there are sliding interfaces, for example, between organs. Further, use of this of technique, where the evaluation of previous reference/target kernels guides the evaluation of later kernels, can result in “downstream tracking errors” when errors in the evaluation of earlier kernels are propagated downstream to the later kernels and kernels evaluated from those later kernels. 
     One method for reducing downstream tracking errors was proposed by Chen et al as discussed in “A Quality-Guided Displacement Tracking Algorithm For Ultrasonic Elasticity Imaging” in Medical Image Analysis 13 (2009) 286-296. In the Chen approach, displacement vectors for a computed kernel are used to guide the search region for later kernels only if there are no other displacement vectors that result in higher “quality” between the reference and target kernels. Correlation between the kernels was used by Chen et al. as the measure of quality. In this way, low-quality kernels carry less priority and could be discarded, given the presence of large number of high correlation kernels, thereby reducing the downstream tracking errors. One additional advantage of the Chen approach is that the computation of displacement vectors propagates through kernels in the direction that is flexibly directed by the resultant correlation of the displacement vectors estimated between reference and target kernels. Consequently, if the direction of propagation of the calculation confronts regions with low signal correlation (that might introduce errors) the direction of propagation of the calculations shifts to flow around the low signal correlation regions rather than through them. Because the propagation of the calculation follows the correlation of the data, tissue interfaces tend to be arrived at by calculations propagating from opposite sides of the interface and terminating at the interface rather than passing through the interface such as would introduce downstream tracking errors. 
     The starting point for the calculation of displacement vectors used in Chen relies upon one or a few “seed” displacement vectors and their associated reference/target kernels spread within the images that are qualified to have high similarity. It is important that these seed displacement vectors and kernels have high correlation between the reference kernel and its best match to the target kernel, because they affect many subsequent calculations and any errors in these seed displacement vectors and kernels will create downstream tracking errors in many other regions. Yet these isolated seed displacement vectors and associated kernels, particularly because of their isolation, are highly susceptible to “peak hopping” errors in which similarity does not reveal an underlying displacement error. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method of improving the identification of high quality seed displacement vectors and their associated kernels by evaluating not only similarity (such as correlation) underlying the displacement calculations for those seed kernels but also local continuity. This combined approach greatly reduces the risk of the selected seed kernels having “peak-hopping” errors while preserving the benefits of the Chen technique. In one embodiment, the continuity check computes displacement vectors in a closed path around the candidate seed kernels in a manner constrained by continuity and then evaluates the extent to which continuity has been achieved. In this way, continuity may be evaluated at and around a spatially isolated point. 
     Specifically then, the present invention provides an elasticity imaging machine having an imaging system adapted to acquire first and second speckle fields in a region of interest of a material with the material in a respective first and second state of deformation. An electronic computer receives the speckle fields from the transducer assembly and executes a stored program to determine a displacement vector for spatially separated first region pairs of corresponding regions in each of the first and second speckle fields, each displacement vector indicating tissue displacement in the region pairs between the first and second state of deformation. The displacement vectors for the region pairs are then qualified with respect to both similarity between the speckle fields in the region pairs and continuity of displacement in neighboring region pairs. The displacement vectors of particular first region pairs are then selected according to their qualification to guide the determination of second displacement vectors in second region pairs surrounding the particular first region pairs. 
     It is thus a feature of at least one embodiment of the invention to provide a more robust method of identifying the quality of seed kernels that will be used to determine displacement vectors for many other kernels. It is a further feature of at least one embodiment of the invention to reduce the susceptibility of the Chen technique to peak hopping. 
     The qualification of displacement vectors may evaluate a similarity between the speckle fields in the first region pairs as a similarity between at least a first and second predetermined corresponding neighboring region pair within the regions. 
     It is thus a feature of at least one embodiment of the invention to make dual use of calculations in neighboring region pairs that are used to qualify continuity as an integral part of quality measure of resultant displacement vectors. 
     The neighboring region pairs may be arrayed along a path and displacement vectors of the neighboring region pairs calculated so as to promote continuity along the path from a starting neighboring region pair to an ending neighboring region pair adjacent to the starting neighboring region pair, and the qualification of continuity of displacement in first region pairs may evaluate a discontinuity between the displacement vectors of adjacent neighboring region pairs. 
     It is thus a feature of at least one embodiment of the invention to provide a simple method of assessing continuity for a spatially isolated seed. 
     The displacement vectors of the neighboring region pairs along the path may be determined by minimizing an energy function accepting as arguments both similarity of the underlying speckle fields of the neighboring region pairs and continuity in displacement vectors between successive neighboring region pairs on the loop. 
     It is thus a feature of at least one embodiment of the invention to establish a continuity metric that affects a flexible compromise between similarity and continuity. 
     The energy function may be minimized using a Viterbi algorithm to determine the displacement vectors of the neighboring region pairs. 
     It is thus a feature of at least one embodiment of the invention to provide a computationally efficient method of establishing the continuity metric. 
     The first region pairs comprise less than 20% of the speckle fields. 
     It is thus a feature of at least one embodiment of the invention to provide a system of assessing seed calculations for continuity when the seed kernels are separated substantially from other seed kernels. 
     The electronic computer or an equivalent digital processing unit may further use displacement vectors of particular second region pairs as new first region pairs according to their qualification to guide the determination of displacement vectors in new second region pairs surrounding the new first region pairs and continue this process, typically, until all displacement vectors at predetermined locations are done. 
     It is thus a feature of at least one embodiment of the invention to provide a seed qualification system suitable for use with successive repeating calculations where downstream tracking errors are possible. 
     The successive repeating calculations for each first region pair may be terminated when a combination of the qualification of similarity and continuity for all second region pairs drops below a predetermined threshold. 
     It is thus a feature of at least one embodiment of the invention to avoid successive repeated calculations through low similarity regions to avoid downstream tracking errors. 
     Upon the termination of the successive repeating calculations for all first region pairs, remaining region pairs without determined displacement vectors may have displacement vectors derived by interpolation from surrounding region pairs having displacement vectors having at least a predetermined quality. 
     It is thus a feature of at least one embodiment of the invention to use an approach to regions of low similarity that reduces downstream tracking errors. 
     The first region pairs may be enrolled in a queue according to their qualification and wherein at each step of repetition, a first region pair is selected from the queue according to qualification to guide the determination of second displacement vectors in second region pairs surrounding selected first region pair. 
     It is thus a feature of at least one embodiment of the invention to flexibly propagate displacement vector calculations along a path of highest quality or from seed locations of highest quality. 
     The first region pairs may be selected at nonuniform locations according to an image produced by the imaging system. 
     It is thus a feature of at least one embodiment of the invention to preferentially locate seeds in areas likely to reduce downstream tracking errors based on image data derived from the system. 
     The material may be tissue and the electronic computer may further execute the stored program to identify a tissue structure and select the nonuniform locations according to a library of known tissue structures. 
     It is thus a feature of at least one embodiment of the invention to preferentially locate seeds to reduce downstream tracking errors based on a priori knowledge about tissue anatomy. 
     The identified nonuniform locations may favor homogenous tissue or tissue with motion continuity. 
     It is thus a feature of at least one embodiment of the invention to preferentially locate seeds away from inhomogeneities or tissue boundaries which may introduce continuity artifacts affecting either the initial qualification of seed calculations or the propagation of calculations from those seeds. 
     These particular objects and advantages may apply to only some embodiments falling within the claims, and thus do not define the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified block diagram of an ultrasound machine holding a stored program suitable for execution of the present invention; 
         FIG. 2  is a representation of speckle fields in a material in two states of deformation showing the analysis of region pairs to deduce displacement vectors in neighboring region pairs of the region pairs; 
         FIG. 3  is a detailed representation of the neighboring region pairs indicating different displacement vectors associated therewith; 
         FIG. 4  is a representation of similarity data obtained for each neighboring region pair of region pairs showing the selection of similarity peaks used to derive displacement vectors as constrained by an energy function sensitive to continuity; 
         FIG. 5  is a flow chart of steps executed by the programming implementing the calculations of  FIGS. 2-4 ; 
         FIG. 6  is a logical representation of a data structures used in the steps of  FIG. 5  of seed calculation queue and region calculation punch list; 
         FIG. 7  is a simplified representation of a path of propagation of calculations using the present invention showing avoidance of a low similarity region that may be calculated instead by interpolation; 
         FIG. 8  is a simplified representation of an organ showing nonuniform seed locations avoiding inhomogeneous tissue; and 
         FIG. 9  is a diagram of a user display permitting the selection or confirmation of organ types to invoke templates for nonuniform seed location per  FIG. 8 . 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to  FIG. 1 , a strain-imaging machine  10  of the present invention includes an ultrasonic array transducer  12  that may transmit and receive ultrasonic signals along a propagation axis  20  to acquire ultrasonic echo data  15  at a region of interest  19  in the tissue  18 . 
     In addition to transmitting and receiving ultrasonic signals along the propagation axis  20 , the transducer  12  may also provide a source of compression of the tissue  18  along propagation axis  20  in order to acquire additional ultrasonic echo data  15 ′ in the region of interest  19  of deformed tissue  18 ′. More generally, echo data  15  will be obtained of the tissue  18  in a first state of deformation and echo data  15 ′ will be obtained of the tissue  18 ′ in a second state of deformation that may be more or less deformed than tissue  18 . 
     The transducer  12  may communicate with a processing unit  22  that both provides waveform data to the transducer  12  used to control the ultrasonic beam and collects the ultrasonic echo signals (radio-frequency data) that form the echo data  15 ,  15 ′. As is understood in the art, processing unit  22  provides for necessary interface electronics  24  to sample and digitize the ultrasonic echo signals to produce the echo data  15 ,  15 ′. The interface electronics  24  operate under the control of one or more processors  26  communicating with a memory  28 , the latter of which may store the data of a speckle field  32  associated with echo data  15 , and data of a speckle field  32 ′ associated with echo data  15 ′. These processors  26  will generally implement “electronic computers” a term intended to an embrace not only conventional von Neumann architecture computers, but any electrical circuit capable of executing the algorithms described herein including, for example, digital signal processors (DSPs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs) as well as other similar devices. 
     As will be understood in the art, speckle fields  32  and  32 ′ are generally two- or three-dimensional images that include “speckles” being image characteristics associated with underlying small-scale features to the tissue  18  and  18 ′ that can be used to deduce the displacement of the tissue  18 ,  18 ′ between states of deformation. For simplicity, speckle field  32  will be termed the pre-deformation speckle field  32  and speckle field  32 ′ will be referred to as the post-deformation speckle field  32 ′. 
     The processors  26  may execute a stored program  30  contained in memory  28  to implement the present invention as will also be described below. The processors  26  also may communicate with an output screen  34  on which may be displayed a strain image  36  and may communicate with a keyboard or other input device  38  for controlling the processing unit  22  and allowing for user input as will be understood to those of skill in the art. 
     Referring now to  FIGS. 2 and 5 , the program  30  executed by the processors  26  may first identify one or more seed locations  40  in the pre-deformation speckle field  32  indicated by process block  39 . Each seed location  40  may define a centerpoint of a kernel neighborhood  41  holding a cluster of reference kernels  45  whose combined areas (or volumes) comprise less than 20% of the area or volume of the pre-deformation speckle field  32 . The size (or volume) of kernel neighborhood  41  is likely system-dependent, e.g. in the order of ultrasound pulse area (or volume). The seed locations  40 , and hence the kernel neighborhoods  41  of reference kernels  45  (the latter of which are neighboring region pairs), are generally separated from each other and may in one embodiment be uniformly spaced apart. 
     The data of the pre-deformation speckle field  32  in the reference kernels  45  will be compared to data of the post-deformation speckle field  32 ′ to find a best match that will indicate the relative displacement (displacement vector) of the underlying tissue at each seed location  40  in the pre-deformation speckle field  32  and post-deformation speckle field  32 ′. In order to avoid the need to examine the entire post-deformation speckle field  32 ′ in matching a given reference kernel  45  to data of the pre-deformation speckle field  32 ′, the matching process will be generally restricted to a search neighborhood  42  in the post-deformation speckle field  32 ′ of predetermined size only slightly larger than the kernel neighborhood  41  and centered at a coordinate within the post-deformation speckle field  32 ′ matching the coordinate of the seed location  40  of the particular kernel neighborhood  41 . Ordinarily, the search neighborhood  42  for a particular kernel neighborhood  41  will not overlap the search neighborhood  42  for any other kernel neighborhood  41 . 
     Referring still to  FIGS. 2 and 3 , in this matching process, the data of multiple reference kernels  45  in the kernel neighborhood  41  corresponding to spatial point  46  will compared to the data of corresponding search regions  47  within the search neighborhood  42 . In one embodiment, multiple target kernels  45  are used describing a path  48  passing from a starting kernel  45   a  preferably, but not necessarily, centered within the kernel neighborhood  41  on point  46  and ending at an ending kernel  45   f . In one embodiment, the path may lead from the starting kernel  45   a  and proceed to kernels  45   b , being one vertex of a rhombus centered about kernels  45   a , and then to kernels  45   c ,  45   d , and  45   e  forming the other vertices of the rhombus and finally terminating at end kernels  45   f  within the kite and adjacent to kernel  45   a.    
     Each of these kernels  45  is compared to data of a corresponding search region  47  within search neighborhood  42  of post-compression speckle field  32 ′. Generally, the search regions  47  will be a pre-determined two or three-dimensional region larger than but corresponding to the positions of the target kernels  45 . Alternatively, the search regions  47  may be guided in location by previously calculated displacement vectors for adjacent target kernels  45 . 
     In the matching process, the data of the multiple kernels  45  is moved through the search regions  47  to establish a similarity between the kernel data and the data overlapped by the kernels  45  within the search region  47 . The overlapping region between a reference kernel  45  (e.g.  45   a ) and the search region  47  is also known as a target kernel  49 . While the possible locations of the target kernels  49  are depicted for clarity as tiled in discrete non-overlapping locations in  FIGS. 2 and 3 , in fact, the target kernels  45  may be located anywhere within the search region  47  and this comparison process “slides” the data of the reference kernel  45  over the search regions  47  in increments at or near the resolution of the underlying data. 
     The similarity  50  of the match between a reference kernel  45  and possible target kernels  49  may be recorded for different locations of the reference kernels  45  within the search region  47  in a similarity map  52 . Again, the resolution of the similarity map  52  has been reduced for clarity. 
     Similarity, as used herein, evaluates the similarity of the underlying data of corresponding pixels of one kernel (e.g.  45   a ) and the target kernel  49 , for example, by summing a magnitude of the differences between the data. The term similarity as used herein is also intended to embrace other techniques of evaluating a matching between the reference kernel  45  and target kernel  49 , including, for example, measures of pattern matching between the two kernels or comparisons of the statistics of the data of the two kernels, for example, measures of data entropy or the like. Similarity may be distinguished from measures of continuity as will be described which look data outside of individual reference kernels  45  and target kernels  49 . 
     The location of a peak  51  of the similarity map  52  for the center target kernel  45   a  may be used to describe a displacement vector  54  for the kernel neighborhood  41  such that the displacement vector  54  has an origin at the center of the search region  47  and a terminus point centered at the location of the peak  51 . The height of the peak  51  may be used to characterize a first aspect of the quality of the kernel neighborhood  41 , that height indicating the similarity of data underlying the determined displacement vector  54 . 
     Once the displacement vector  54  (hereafter referred as to  54   a ) is determined based on the peak  51  for the starting kernel  45   a , a displacement vector (i.e.  54   b ) corresponding to the second kernel  45   b  can be determined by search a reduced search region  47  on the guidance of the displacement vector  54   a  given the continuity assumption. This checking process proceeds until a displacement vector  54   f  corresponding to individual kernel  45   f  is obtained. The degree of difference between displacement vectors  54   a  and  54   f  may, in a first embodiment, provide a measure of continuity that augments the measure of similarity as will be described further below. In this embodiment, continuity among displacement vectors  54  is promoted by using previous displacement vectors  54  to guide location of the search regions for later displacement vectors  54 . 
     Referring now to  FIG. 4 , in an alternative embodiment, the multiple target kernels  45  may have displacement vector  54  continuity determined using an energy function. For this purpose the displacement vectors  54  are determined for the remaining individual kernels  45   b - 45   f  evaluating their similarity maps  52  and the similarity map  52  of search region  47   a  in succession from the starting search region  47   a  to search region  47   f . The displacement vectors  54  for these other search regions  47   b - 47   f  are selected not simply according to the peaks of the similarity maps  52  (as was the case with the displacement vector  54  for search region  47   a ) but also to promote continuity among adjacent displacement vectors  54  along the path  48  (that is, to reduced the differences in angle and magnitude among adjacent displacement vectors  57  along the path  48 ). This may be done in one embodiment by minimizing a cost function as follows: 
     
       
         
           
             COST 
             = 
             
               ∫ 
               
                 
                   ∫ 
                   path 
                   
                       
                   
                 
                 ⁢ 
                 
                   
                     ( 
                     
                       
                         α 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           E 
                           C 
                         
                       
                       + 
                       
                         ϕ 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           E 
                           S 
                         
                       
                     
                     ) 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     ⅆ 
                     path 
                   
                 
               
             
           
         
       
     
     where α and φ are empirically selected scale factors, E C  is a measure of similarity in the speckle of the regions and E S  is a measure of continuity between adjacent search region  47  displacement vectors  54 . 
     This process of identifying the displacement vectors  54  for each of the successive search regions  47   a - f  may use an optimization algorithm such as the Viterbi algorithm to rapidly identify a displacement vector  54  for each of the search regions  47   b - f.    
     Again, at the conclusion of this process, collectively indicated by process block  60  of  FIG. 5 , the displacement vectors  54   f  and  54   a  of the starting search region  47   a  and ending search region  47   f  are compared to provide a measure of quality of the kernel neighborhood  41  based on continuity. For a high quality kernel neighborhood  41 , as measured by continuity, the displacement vectors  54  for these two adjacent regions will be similar or identical. The degree of difference between these displacement vectors  54  for search regions  47   a  and  47   f  (and hence for target kernels  45   a  and  45   f ) thus provides a measure of continuity that augments the measure of similarity described above. 
     Continuity, as used herein may alternatively employ other statistical tests such as variance among displacement vectors  54   a - f  (either in vector angle or vector length or both) and could also be appropriate metrics as measures of continuity. Generally, in contrast to similarity, continuity evaluates data among different target kernels  45  in the kernel neighborhood  41 . 
     These two values of continuity and similarity may be combined, for example, by a simple weighted sum and used to provide a quality measurement for each kernel neighborhood  41 . The seed locations  40  for those center target kernels  45  of kernel neighborhoods  41  having a quality value above an empirically determined threshold value may then be enrolled in a queue  70  shown in  FIG. 6  and as indicated by process block  62  of  FIG. 5 . The queue  70  logically provides a first column holding the quality measurement for each enrolled target kernel  45  (combining similarity and continuity) together with the coordinates of the seed location  44  of the target kernel  45  in two or three dimensions in the next two or three columns. The displacement vector  54  in two or three dimensions may then be provided in the next two or three columns. 
     Referring to  FIG. 5 , at succeeding process block  64 , the queue  70  is interrogated to find the highest quality associated with a reference kernel  45  location among all of the enrolled reference kernels  45 . The displacement vector  54  for this highest reference kernel  45  is adopted (that is it will be used in a final displacement assessment) and that adoption is indicated in a punch list  72  providing a flag location for each pixel of the speckle field  32  (optionally arranged in kernel-sized units). 
     At succeeding process block  64 , new kernels  45 ′ (shown in  FIG. 7 ) are then constructed from the neighboring pixels of the adopted reference kernel  45  and the quality of these new kernels  45  are evaluated as described above using associated kernel neighborhoods centered around the new kernels  41 ′. Those new kernels  45 ′, whose quality exceeds the threshold described above, are then enrolled in the queue  70  per process block  76  as new reference kernels  45 . 
     Assuming that displacement vectors  54  have not been adopted for all possible reference kernel  45  in the speckle field  32  meeting the required quality measurements, per decision block  77 , the process loops back to process block  64  and the queue  70  is interrogated to find the highest quality new reference kernel  45 ′. The displacement vector  54  for this highest quality new reference kernel  45 ′ is then adopted and marked in the punch list  72  and the process continues at process block  76  using this highest quality new reference kernel  45 ′ as if it were a reference kernel  45  to evaluate its previously unevaluated neighbors. 
     The process continues to loop through process (referred to herein a successive repeated calculation) and decision blocks  64 ,  76 , and  77  until all possible reference kernel  45  have been evaluated, some adopted in the punch list  72  and some not. 
     Referring to  FIG. 7 , this process of selecting the highest quality reference kernels  45  or  45 ′ in the queue  70  causes a generally radial outward growth in the evaluation of reference kernels  45  from original reference kernels  45  as indicated by arrows  80 , the growth following a path of relative highest quality around low correlation regions  82  and typically stopping at tissue interfaces  84  where low similarity or continuity occurs. When multiple seed reference kernels  45  are provided, this growth pattern at a given interface  84  may be approached from two different directions without passing through the interface creating downstream tracking errors. This evaluation may make use of parallel processing on multiple processors. 
     Referring to  FIG. 5 , as indicated by process block  85 , when there are no more seed reference kernels  45  in the queue  70 , the reference kernel  45  that have been evaluated but that have not had displacement vectors adopted as indicated by punch list  72  may be re-evaluated through a different interpolation process using weighted displacement vectors  54  from the surrounding kernels whose displacement vectors  54  were adopted and enrolled in the queue  70 . At process block  86  a strain image may be output using the displacement vectors  54  in the queue  70  and as interpolated, the strain image calculated according to well-known techniques. 
     Referring now to  FIG. 8 , the starting locations  40  for the seeds need not be uniformly arrayed along a grid but maybe opportunistically placed, for example, in regions of tissue homogeneity as determined by an image  94  constructed from the echo data  15  described above. For example, with a kidney, the seed locations  40  would be placed outside of the inhomogeneities caused by, for example, the renal arteries and veins or the renal sinus or regions of coherent tissue motion (e.g., avoiding sliding interfaces between organs). These starting locations  40  may be selected manually by observation of the image  94  or may be identified automatically from the image data using a template or the like. The template may incorporate a priori knowledge about inhomogeneities or discontinuous tissue motion for an organ or tissue type identified from the image data. In some cases, elasticity information itself, taken on a coarse grid or from a previous imaging attempt may guide the placement of the seed locations  40 . 
     Referring to  FIG. 9 , in one embodiment using a template, a physician may select a menu  90 , for example, labeled with different organ types to identify a template  92  for a particular organ providing an empirically pre-determined preferred seed location  40  in a mask  91  that may be superimposed over the image  94  and expanded/contracted or rotated to fit to an organ in the image  94  manually or automatically using image recognition techniques. 
     It is specifically intended that the present invention not be limited to the embodiments and illustrations contained herein, but include modified forms of those embodiments including portions of the embodiments and combinations of elements of different embodiments as come within the scope of the following claims.