Abstract:
An elasticity measuring system determines tissue displacement between a pre-deformation and post-deformation image by a matching process using a cost function accepting as its arguments continuity of the tissue motion and correlation of the tissue in making the block matching. The invention allows the selection among different cost functions for different imaging situations or tissue types, to provide improved displacement calculations using a priori knowledge about the tissue and structure of tissue interfaces or information derived during the scanning process.

Description:
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     This invention was made with United States government support awarded by the following agency: NIH CA100373. The United States government has certain rights in this invention. 
    
    
     CROSS-REFERENCE TO RELATED APPLICATIONS 
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     BACKGROUND OF THE INVENTION 
     The present invention relates to ultrasonic imaging and, in particular, to an improved method and apparatus for calculating material displacement used to produce elasticity images including local strain, modulus and Poison&#39; ratio images. 
     Ultrasonic elasticity imaging produces an image showing the elastic parameters of the material being measured. When used in medicine, elasticity imaging is analogous to palpation by a physician, that is, the pressing of tissue by the physician to feel differences in elasticity of underlying structures. 
     In a common form of elasticity imaging, two separate ultrasonic images are obtained, the first image with tissue in an undeformed state relative to the second image (“pre-deformation”) and the second image with the tissue in a deformed state (“post deformation”). The two images are analyzed to deduce the amount of displacement of the tissue at corresponding areas within the images. One type of tissue elastic parameters is the local strain, i.e. the gradient in the displacement computed at many points over the image, which provides an indication of the tissue elasticity at those points. The general principles of elasticity imaging and techniques for determining displacement of the tissue between two ultrasonic images are described in detail in U.S. Pat. No. 6,508,768, hereby incorporated by reference. 
     An important aspect of processing the pre-deformation and post-deformation ultrasonic images to deduce the displacement of tissue elements, is identifying motion patterns of corresponding points between the two images. This is normally accomplished by identifying each point in the pre-deformation image and establishing a region of points (kernel) surrounding that identified point. This kernel is then moved within a one, two or three dimensional search window within the post-deformation image to identify the location within the search window providing the best match between the points of the kernel and a corresponding kernel in the post-deformation image. 
     Normally the size of the search window must be sufficient to accommodate likely tissue displacements between the pre-deformation and post-deformation images, but must also be limited to manage the computational burden of matching points with each other and to reduce the chance of possible false matches that violate a priori assumptions about limited mobility of a tissue continuum reacting to external mechanical stimuli. 
     SUMMARY OF THE INVENTION 
     The present inventors have recognized that there is an inherent tension in matching kernels of the pre-deformation and post-deformation images between restoration of signal coherence (high correlation between matching kernels) and correctness of the matching with respect to other matches (continuity or limited tissue mobility), and that this tension may best be resolved using mathematical optimization where cost functions allow different trade-offs between correlation and continuity for different tissue types and imaging situations. By allowing this trade-off to be varied, for example, based on a priori knowledge about the imaging situation, improved images may be obtained. 
     Specifically then, the present invention provides an ultrasound strain imaging machine having a transducer assembly producing an axial ultrasonic beam through a region of interest of a material and acquiring an echo signal from that region of interest in a first and second state of deformation. An electronic computer receives the echo signals from the transducer assembly and executes a stored program to: (i) select among cost functions providing different weighting between correlation and continuity in a matching of regions of the echo signals; (ii) apply the cost function in identifying corresponding regions of the echo signals between the first and second states to deduce displacement of the tissue under deformation; and (iii) output information to an operator revealing elastic parameters of the tissue based on the deduced displacement. 
     It is thus an object of at least one embodiment of the invention to vary the cost function used for matching elements of the pre-deformation and post-deformation image to better tailor the imaging process to different tissue types and imaging protocols. 
     The electronic computer may select among cost functions using a selection input from the user. 
     It is thus an object of at least one embodiment of the invention to permit the user to provide additional information about the imaging task to improve the imaging process. 
     The cost function selection input by the user may identify a tissue type. 
     It is thus an object of at least one embodiment of the invention to provide a simple and intuitive method of selecting among cost functions according to typical tissue qualities. 
     The tissue type is selected from the group consisting of breast, liver, cranium, thyroid, prostate, uterus and vasculature etc. 
     It is thus an object of at least one embodiment of the invention to provide empirically derived cost functions for common tissue types. 
     Alternatively, the cost function selection input by the user may be an imaging protocol such as abdominal, OB/GYN, breast, vascular, small parts, pediatric, neonatal transcranial, and musculoskeletal. 
     Thus, it is an object of at least one embodiment of the invention to permit the incorporation of cost function selection into systems that already allow the user to select particular imaging protocols to control other aspects of the ultrasound machine. 
     In an alternative embodiment, the electronic computer may select among cost functions using a B-mode image or other ultrasound-based parametric image generated from at least one of the echo signals. 
     It is thus an object of at least one embodiment of the invention to allow different cost functions to be applied to different portions of the image. 
     The electronic computer may select a cost function having reduced emphasis on correlation near boundaries between organs identified from the parametric image. 
     It is thus an object of at least one embodiment of the invention to relax the continuity requirements when there is a priori knowledge of a tissue interface. 
     The boundaries identified maybe input by a user demarcating the boundaries or the boundaries may be automatically identified by image statistics of the parametric images. 
     The process of applying a cost function in the motion estimation between two images may be achieved by dynamic programming techniques (e.g. Viterbi algorithm) to shorten the computational process. 
     It is thus an object of at least one embodiment of the invention to provide a simple but powerful way of integrating a cost function into the block matching process. 
     The cost function may provide cost that increases linearly with at least one of correlation and continuity, cost that increasingly increases with at least one of correlation and continuity, or cost that decreasingly increases with at least one of correlation and continuity. 
     It is thus an object of at least one embodiment of the invention to permit general-purpose cost functions that may be used in novel imaging applications or when empirical data has not been established. 
     The different cost functions may include at least one interfacial cost function having a lower dependency on continuity than the other cost functions. 
     It is thus an object of at least one embodiment of the invention to provide a simple method of managing block matching calculations across tissue boundaries. 
     The above-mentioned matching process by cost functions may only be applied to pre-selected regions to obtain guidance for subsequent matching processes. 
     It is thus an object of at least one embodiment of the invention to provide a need-based method of managing the sophistication in the matching processes. 
     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 representation of an ultrasound imaging machine suitable for practice of the present invention showing an ultrasonic transducer for collecting rows and columns of ultrasound data and for manually or mechanically deforming the tissue of an object, and showing a multiprocessor computer for processing the ultrasound data according to a stored program to produce an ultrasonic elasticity image on a display device; 
         FIG. 2  is a fragmentary representation of selected columns of ultrasound data of a pre-deformation image and a post-deformation image showing a block matching of data of a kernel within a search window to deduce tissue displacement; 
         FIG. 3  is a perspective representation of a cost function used in the block matching of  FIG. 2  varying linearly with correlation and continuity; 
         FIG. 4  is a graph along lines of arbitrary constant continuity or constant correlation showing variations of the cost fimction that may emphasize one of continuity or correlation; 
         FIG. 5  is a flow chart of the present invention including the step of selecting a cost function; 
         FIGS. 6-8  are flow chart blocks representing alternative methods of selecting the cost function in the flowchart of  FIG. 5 ; 
         FIG. 9  is a control display of the present invention allowing selection of cost functions by the proxy of tissue type; 
         FIG. 10  is a figure similar to that of  FIG. 9  allowing the selection of cost functions by image protocol; 
         FIG. 11  is a simplified B-mode image showing an interface between tissue types where a different cost function may be applied; and 
         FIG. 12  is plot of a line along a B-mode image and an elasticity image showing automatic determination of interface regions and cost functions. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to  FIG. 1 , an elasticity 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 corresponding volume elements  17  throughout a region of interest  19  in the tissue  18 . 
     The echo data  15 , and its corresponding volume elements  17 , may both be identified by logical rows  14  and columns  16 , wherein the rows  14  are generally echo data  15  or volume elements  17  extending perpendicularly to the propagation axis  20 , and the columns  16  are generally echo data  15  or volume elements  17  extending parallel to the propagation axis  20 . These terms should be understood generally to describe data acquired through a variety of ultrasonic acquisition geometries including those which provide for fan beams of ultrasound and the like, and therefore not be limited to strictly rectilinear rows and columns. 
     In addition to transmitting and receiving ultrasonic signals along the propagation axis  20 , the transducer  12  may also provide a source of deformation along deformation axis  20 ′ generally aligned with a propagation axis  20  of ultrasound from the transducer  12 . Generally, echo data  15  will be obtained with the tissue  18  in a first state of deformation and a second state of deformation (indicated by tissue  18 ′) to provide pre-deformation and post-deformation image sets. It will be understood that characterizations of “pre-deformation” and “post-deformation” are arbitrary and in fact the pre-deformation tissue may be the tissue that is deformed by the transducer  12 . 
     The transducer  12  communicates 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 . As is understood in the art, processing unit  22  provides for necessary interface electronics  24  that may sample the ultrasonic echo signals to produce the echo data  15 . The interface electronics  24  operate under the control of one or more processors  26  communicating with a memory  28 , the latter which may store the echo data  15  identified to rows  14  and columns  16  to form pre-deformation data sets  32  of echo data  15  and post-deformation data sets  32 ′ as will be described. 
     Generally, the processors  26  may execute a stored program  30  contained in memory  28  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 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  FIG. 2 , the data of the pre-deformation data set  32  may be divided into blocks  40  (e.g. A, B, C) depicted two dimensionally but typically being three-dimensional volumes embracing kernels of multiple points of echo data  15 . Each block (for example, block A) in the pre-deformation data set  32  is matched to a corresponding block (for example, A′) in the post-deformation data set  32 ′ to deduce a displacement  42  that will be used to calculate elastic parameters. 
     In the present invention, this block matching involves comparing the block (depicted, for example, using block B) in the pre-deformation data set  32  to corresponding data in a search region/window  44  in the post-deformation data set  32 ′. In this process, the block B may be thought of as being scanned, for example, in a raster pattern throughout the window  44 . At each point in the scan, a correlation value  46  is obtained indicating how well the values of data block B match with the underlying data in the search window  44  at that position. This correlation value  46  is stored in a correlation map  47  relating it to a particular row and column. Correlation is used here to mean any mathematical matching process that yields a match quality, including, for example, computing the sum of the square of the differences between the echo data  15  of corresponding points in the blocks  40  of the pre-deformation data set  32  and post-deformation data set  32 ′. 
     At the same time, the displacement  42 ′ between the current scanned position of block B within the search window  44  and the previously deduced block A′ and a change in displacement is recorded as a continuity value  48  and stored in a continuity map  50  relating this displacement to a particular row and column of the current scanned position of block B. Generally, the displacement  42  may consider either or both of the length of the vector displacement or the length of the vector displacement and its angle with respect to a previous displacement  42  of block A′. In addition, while the displacement  42  is shown as a simple single vector from block A′, it may be the combination of displacement vectors of multiple previously established blocks (for example to the left and right and front and back) so as to provide a measure of deviation of block B from previously established blocks that generally reflects the sense that there is continuity of displacement between adjacent tissue blocks. 
     Referring now to  FIG. 3 , in the present invention, the correlation values from correlation map  47  and continuity values from continuity map  50  may serve as arguments input to a cost function  52  yielding a cost value. A cost value may be computed for each correlation value  46  and each continuity value  48  to create a cost matrix  54  equal in size to the continuity map  50  and correlation map  47  and providing a cost value for each row and column of the window  44 . The above-mentioned process will be executed for a list of blocks (e.g. A, B and C) to obtain multiple cost matrices for these blocks, respectively. These cost values together produce a cost graph, the local maximum cost-path traveling through the cost graph can be found using computationally-efficient dynamic programming techniques (e.g. Viterbi algorithm, IEEE Trans. Information Theory, 1967). Each node on the minimum cost-path represents the best location of a particular block in terms of trade-offs between correlation and continuity according to the cost function  52 . The list of blocks may be chosen along the direction of tissue deformation. Therefore, the directionality of these displacement vectors will make the matching process more robust. For instance, the list of blocks can be chosen along a column  16  of the region of interest  19  to establish guidance for the subsequent motion tracking/matching, as described in U.S. patent application number 2007/0234806 to Jiang et al. published Oct. 11, 2007 and entitled: “Ultrasonic Strain Imaging Device And Method Providing Parallel Displacement Processing” hereby incorporated by reference. 
     As depicted in  FIG. 3 , generally the cost provided by the cost function  52  increases with greater (improved) correlation and with greater (improved) continuity. Greater continuity in this sense means smallest changes in displacement  42  and greater correlation means smallest difference between block B and its underlying data. 
     Referring to  FIGS. 3 and 4 , the depicted cost function  52  of  FIG. 3  depicts a linear relationship between correlation and cost and continuity and cost; however, the present invention contemplates that a family of cost functions  52   a ,  52   b  and  52   c  may be used providing, respectively, functional relationships between cost and either or both of correlation and continuity that decreasingly increase the cost with increasing continuity or correlation (cost function  52   a ), that linearly increase the cost with increasing continuity or correlation (cost function  52   b ), or that increasingly increase cost with increasing continuity or correlation (cost function  52   c ). It will be understood that three cost functions can provide a basic palette of cost functions when specific information about the tissue or imaging protocol is not well characterized. These examples are not intended to limit possible cost functions or to require cost functions that are monotonic as depicted. Within each of these general characterizations of cost functions, there exists a family of cost functions precisely characterizing different curves. 
     Generally, cost function  52   a  may be preferred in mapping continuity to cost when there is expected to be significant discontinuity in tissue stiffness (e.g., organ interfaces). Conversely continuity following cost function  52   c  may be preferred for large homogenous tissue. Cost function  52   a  may be preferred in mapping correlation to cost for relatively yielding tissue that may produce lower correlation values whereas cost function  52   c  may be preferred for relatively rigid tissue. These expectations, of course, can be refined and corrected empirically. 
     Referring now to  FIG. 5 , the present invention thus requires an ultrasonic echo data acquisition in tissue in two states of deformation as indicated by process block  60  and illustrated schematically in  FIG. 1  by tissue  18  and  18 ′. 
     At succeeding process block  62 , which alternatively could precede process block  60 , a cost function is selected for the analysis of this acquired data. 
     Referring to  FIG. 6  in a first embodiment indicated by process block  62   a , the cost function may be selected by the user, for example, by inputting a cost function either through a variety of data points or as a mathematical expression, or by selecting from particular cost function shapes depicted schematically. Preferably, however, as shown in  FIG. 9 , this selection of the cost function is done by a proxy of selecting a particular tissue type or imaging situation. As shown in  FIG. 9 , this first alternative displays to the user a set of tissue types  64  for which cost functions have been prepared empirically to be useful in these tissue types. Thus, for example, the user may select among abdominal, breast, cranial, thyroid, prostate, uterus or vascular tissue, and an empirically prepared cost function is then used automatically for the calculations. 
     Alternatively as indicated in  FIG. 10 , the imaging protocols  65  typically provided in an ultrasound machine and used to adjust other parameters of the ultrasonic acquisition may be used in the additional capacity of selecting the cost function. In this case, the user selects among imaging protocols for abdominal, OB/GYN, breast, vascular, small parts, pediatric, neonatal transcranial, and musculoskeletal. Again, empirically prepared cost functions may be used. One method of preparing cost functions empirically employs phantoms representing these different tissue types or imaging protocols, the phantoms constructed with known materials, and permits modification of the cost function parameters, for example by controlling cost function surface polynomial coefficients, until accurate elasticity images are obtained with minimal artifacts. 
     Referring now to  FIG. 7 , an alternative method of defining the cost functions, indicated by process block  62   c , uses a B-mode image or other ultrasound-based parametric image of the actual tissue  18 . Referring now to  FIG. 11 , the B-mode image, for example, may show an organ  68  surrounded by other tissue  70 . The user may trace a boundary  72  between the organ and surrounding tissue and assign a particular cost function to each. This assignment may, for example, select a cost function from a library of standard cost functions or may use tissue types as proxies as described with respect to  FIG. 9 . It is expected that it may be desirable to apply a separate cost function to the interstitial regions of the boundary  72 , typically one which relaxes the constraints of continuity reflecting the fact of slippage between organs and surrounding tissue. 
     Referring now to  FIGS. 8 and 12 , as indicated by process block  62   c , the selection of the cost function may also be prepared automatically by analyzing the B-mode image for different tissue types. As shown in  FIG. 12  depicting B-mode values taken along line  74  through the organ and tissue of  FIG. 11 , the echo data  76  of the B-mode image may be analyzed to automatically deduce the boundary  72  (and apply an appropriate cost function to that boundary), for example by looking at abrupt changes in average B-mode values reflecting changes between tissue types or dividing histograms of B-mode data into distinct regions. Morphometric filters may be applied to obtain a three-dimensional boundary around different tissue types. These boundaries and regions may be displayed to the user for correction and/or for the manual selection of cost functions to each region. 
     Alternatively, the cost functions may be deduced by knowledge of the protocol of the imaging (previously input by the user) indicating the likely types of tissue. 
     Initial elasticity data  78 , for example taken with a standard or default cost function, may also be used in conjunction with the B-mode image data to deduce the correct cost function. Thus, for example, the boundary  72  may be used on the elasticity data  78  to allow analysis of the elasticity statistics in each of regions A (the organ), C (the boundary  72 ) and B (the surrounding tissue). This initial elasticity measurement, as subdivided, can provide guidance on which cost function to use. For example, as shown in region A, tissue exhibiting fine scale elasticity changes may employ a cost function that relaxes continuity at lower scales. Conversely a tissue showing less variation in elasticity, as shown in region B, may employ a cost function that is more sensitive to changes in continuity. 
     Referring again to  FIG. 5 , at process block  80 , after the cost function has been selected, a block displacement or similar algorithm is used to find a multipoint displacement between the pre-deformation and post deformation images. In the preferred embodiment this may be done using a Viterbi algorithm of a type known in the art. Such algorithms deduce a likely sequence of states (in this case block displacements) based on previous states and a model of the likelihood of next states, in this case provided by the cost function. 
     The general organization and path of the block matching may follow the procedure described, for example U.S. patent application number 2007/0234806 to Jiang et al. published Oct. 11, 2007 and entitled: “Ultrasonic Strain Imaging Device And Method Providing Parallel Displacement Processing” hereby incorporated by reference. 
     As indicated by process block  82 , the output of elasticity measurements may then be provided to the user, for example, on the screen  34  of  FIG. 1 . 
     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.