Coupled axial and lateral displacement estimation for elasticity imaging

The determination of axial and lateral displacement in a material subject to compression is determined by fitting a multi-dimensional model function to the match between corresponding portions of the material in two states of compression. In one embodiment, iso-contour lines in a correlation between a reference kernel and a target kernel are fit to an ellipse whose center defines the maximum correlation and hence the displacement.

CROSS-REFERENCE TO RELATED APPLICATIONS

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 a mechanical or physiological stimulus. 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 entitled: Ultrasonic Elasticity Imaging, and in pending U.S. application Ser. No. 12/258,532 filed Oct. 27, 2008 and entitled: Ultrasonic Strain Imaging Device with Selectable Cost-Function, and in pending U.S. application Ser. No. 12/645,936 filed Dec. 23, 2009 and entitled: Elasticity Imaging Device with Improved Characterization of Seed Displacement Calculations, all assigned to the same assignee as the present invention and 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 in 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.

In ultrasonic imaging systems, the determination of displacement may be limited to an axial direction defined by the propagation of the ultrasonic signal. This is because motion tracking in the lateral direction (perpendicular to the axial direction) tends to be of low quality possibly because of the loss of phase information because sequential data in the lateral direction is assembled from multiple rather than a single beam.

Nevertheless lateral displacement information can be valuable because it provides a more complete picture of elasticity necessary for many types of measurement.

SUMMARY OF THE INVENTION

The present invention provides a method of improved lateral displacement determination in elasticity imaging by using a coupled determination of axial and lateral displacement that informs the determination of each axis of displacement with the other. In one embodiment, a multi-dimensional model function is fit to a comparably dimensioned map of correlation between the reference kernel and the target kernel and displacement is derived from the location of the fit model function. This fitting process beneficially combines the determination of axial and lateral displacement.

Specifically, the present invention provides in one embodiment, an apparatus for obtaining elasticity images indicating elastic properties of a material subject to periodic compression. The apparatus may include an imaging device for obtaining reference and target image information at different compressions, the image information having at least two spatial dimensions. An electronic computer that may be shared with the imaging device executes a stored program and receives the image information to compare corresponding portions of the reference and target image information. This comparison produces, for each compared portion, match-quality information indicating a matching between the corresponding portions at multiple points in at least two spatial dimensions. A match-model having at least two spatial dimensions is then fit to the match-quality information for each compared portion and a displacement value for at least one of the compared portions is extracted from the fit match model. Optionally, a human readable output is provided based on the multiple extracted displacement values.

It is thus a feature of at least one embodiment of the invention to provide a simultaneous fitting of axial and non-axial data to improve non-axial estimates of displacement and strain.

The match-model may be a Gaussian function representing expected matching in an elastic material.

It is thus a feature of at least one embodiment of the invention to employ a match model derived from the point spread function of a typical imaging machine.

The match-quality information and the match-model may have at least two spatial dimensions. In two spatial dimensions, the match-model is an ellipse fit to a contour line of the match-quality information following a line of constant match-quality, while in three dimension, such a model may be an ellipsoid fit to a contour surface of the match-quality information following a surface of constant match-quality.

It is thus a feature of at least one embodiment of the invention to provide a system applicable to at least two-dimensional image information.

The constant match-quality may be determined from the peak match-quality.

It is thus a feature of at least one embodiment of the invention to select a contour line or contour surface that best uses the obtainable signal, making a trade-off between a number of data points in the contour line and signal-to-noise ratio of the data of the contour line or contour surface.

The imaging apparatus may be an ultrasound machine collecting data along an axis by electronically manipulating received ultrasound data along the direction of acoustic waves, and wherein the three spatial dimensions are one aligned with the axis and the other two perpendicular to the axis.

It is thus a feature of at least one embodiment of the invention to provide a method of augmenting the lower resolution inherent in the non-axial dimensions of an ultrasound machine for improved displacement measurement.

The output may be images of at least one of displacement and strain related quantity.

It is thus a feature of at least one embodiment of the invention to provide improved elastographic images.

The electronic computer may select the portions of the reference and target image information for the comparison using pattern matching of the reference and target regions.

It is thus a feature of at least one embodiment of the invention to combine the rapid computational benefits of pattern matching with the present invention.

The electronic computer may up sample the portions before the comparison.

It is thus a feature released one embodiment of the invention to increase points of comparison for the matching process.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring now toFIG. 1, an elasticity imaging machine10per the present invention may include an ultrasonic array transducer12that may transmit and receive ultrasonic signals along a propagation axis20to acquire ultrasonic echo data15at corresponding volume elements17throughout a region of interest19in the tissue18.

The echo data15, and its corresponding volume elements17, may be identified by logical rows14, columns16and planes40, wherein the rows14are generally different times in the echo data15distinguishing volume elements17extending perpendicularly to the propagation axis20, and the columns16and planes40are generally different rays of the echo data15distinguishing volume elements17extending parallel to the propagation axis20(for the columns) or volume elements17extending perpendicularly to the rows14and columns16(for the planes). These terms should be understood generally to describe data acquired through a variety of acquisition geometries including those which provide for fan beams of ultrasound and the like, and should not be limited to rectilinear rows, columns and planes.

In addition to transmitting and receiving ultrasonic signals along the propagation axis20, the transducer12may also provide an instrument to provide deformation along deformation axis20′ generally aligned with a propagation axis20of ultrasound from the transducer12. This can be done by varying a downward pressure of the transducer12against the tissue18. Generally, echo data15will be obtained with the tissue18in a first state of deformation with respect to a second state of deformation (indicated by tissue18′), to provide pre-deformation and post-deformation tissue18measurements. 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 more deformed by the transducer12. The introduction of mechanical stimuli in elasticity imaging is generally understood in the art, and is not be limited to deformation induced by transducer12but may include other external or internal compression systems and/or the exploitation of physiological compression mechanisms such as cardiac or resperatory mechanisms.

The transducer12communicates with a processing unit22that both provides waveform data to the transducer12used to control the ultrasonic beam and collects the ultrasonic echo signals (radio-frequency data) that form the echo data15. As is understood in the art, processing unit22provides for necessary interface electronics24that may sample the ultrasonic echo signals to produce computer readable echo data15. The interface electronics24may operate under the control of one or more processors26communicating with a memory28, the latter which may store the echo data15identified to rows14, columns16, and optionally planes40, to form pre-deformation “reference” data sets32of echo data15and post-deformation “target” data sets32′ as will be described further below.

As will be appreciated by those of ordinary skill in the art, reference data set32and target data set32′ are generally two- or three-dimensional images that include “speckles” being image characteristics associated with underlying small-scale features to the tissue18and18′ that can be used to deduce the displacement of the tissue18,18′ between states of deformation.

Generally, the processors26may execute a stored program30contained in memory28as will also be described below. The processors26also may communicate with an output screen34on which may be displayed a strain or elasticity image36and with a keyboard or other input device38for controlling the processing unit22and allowing for user input as will be understood to those of skill in the art.

Referring now toFIG. 2, the program30executed by the processors26may first perform a pattern matching, as indicated by process block42, between the reference data set32and the target data set32′. Such a pattern matching may be a so called block matching of a type generally understood in the art in which the reference data set32and target data set32′ are compared in a series of sequential blocks. In this process, a reference kernel44is identified in the reference data set32comprising a collection of adjacent data points46being sample points of the underlying ultrasonic signal. This reference kernel44is compared to data within a search window48in the target data set32′, for example, by sliding the reference kernel44through the area of the search window48to find the best match. The best match may be indicated, for example, by the highest correlation and defines a displacement of the data points46from the reference data set32and target data set32′ and may be expressed as a two- or three-dimensional vector.

The block matching may employ a cost function, for example, of the type described in the above referenced U.S. patent application Ser. No. 12/645,936. In particular, a cost function may be of the form of:

where α and φ are empirically selected scale factors, ECis a measure of similarity in the speckle of the regions and ESis a measure of continuity (de-correlation and motion continuity, respectively) and path is a small neighborhood at the point of displacement. In one embodiment, ECis set to 1−NCC, where NCC is the normalized cross-correlation coefficient (of the kernel44centered at one of candidate location within the search window48) and α is set to 1.

The block matching is repeated for multiple kernels44(as indicated by an arrow43) together covering the data of the reference data set32to produce a first displacement image50per process block52. The displacement image50may be limited to integer portion of displacement values (because the fraction portion of integer displacement of the kernel44in scanning the window is less accurate). This displacement image provides a set of displacement vectors54for each kernel44and generally at regular locations over the entire area of the reference data set32or corresponding target data set32′ indicating the shift necessary in each kernel44in the reference data set32necessary to produce the highest correlation in a corresponding window48of the target data set32′.

Per process block56, the displacement image50may optionally then be used to warp the target data set32′ to compensate for the relative distortion caused by the measured displacement and representing the deformation, for example, imposed by compression of the transducer12. This warping produces a corrected target data set32″ that roughly matches the reference data set32in terms of the relative separation and position of data points46in each data set. The warping may assume local incompressibility of the tissue18and thus for a given axial compressive strain ε may provide an axial stretching and lateral compression by the amounts of ε and 0.5ε respectively. The warping may use an interpolation, for example, using a fast B-spline algorithm of the type described in M. Unser, “Fast B-spline Transforms for Continuous Image Representation and Interpolation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 277-285, 1991 hereby incorporated by reference.

As indicated by process block58, the program30then analyzes a set of reference kernels60regularly spaced in the reference data set32with respect to corresponding windows62centered about the anticipated point of highest correlation determined by the displacement vector54of the displacement image50. Each reference kernel60is scanned in a pattern61with respect to the windows62and at each scanning point, a match-quality (for example, a correlation) is determined to create a match-quality image64generally either equal to or smaller than the area of the search window48. The match-quality image64provides a surface66indicating a degree of correlation at each scanning point in the windows62when the reference kernel60is centered on that point. The reference kernel60and windows62may be up-sampled prior to this comparison process.

Theoretically, the surface66will be a two-dimensional Gaussian function (for two-dimensional reference data set32) or a three-dimensional Gaussian function (for a three-dimensional reference data set32) being a function closely related to the inherent point spread function in the imaging system. The particular aspect ratio (height to width, or height to width and depth) will depend on the fundamental bandwidth or resolution of the elasticity imaging machine10in the different axes of the columns, rows (and optionally planes) and compression parameters in those directions.

For each match-quality image64(corresponding to a particular kernel60) an iso-contour line68is identified for a constant match-quality. The constant match-quality may for example be a set based on an identification of the match-quality of a peak70of the surface66, for example as defined by:
K=ρmax−0.04m(2)
where K is the constant match-quality of the iso-contour line;
ρmaxis the value of the quality match at the peak70;
and m is one standard deviation of quality match values.

At process block72, a multi-dimensional model74may be fit to the iso-contour lines68where the multi-dimensional model74has generally the same dimensions as the reference data sets32and target data set32′. Thus, for a two-dimensional data set, the multi-dimensional model will be an ellipse76having an aspect ratio as defined above representing a theoretical shape of a cross section through Gaussian correlation surface66. This fitting process will yield a two-dimensionally located center-point78of the ellipse76.

The matching process may, for example for a two-dimensional case, use the published algorithm described in A. W. Fitzgibbon, M. Pilu, and R. B. Fisher, “Direct Least Squares Fitting of Ellipses,” in Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I—Volume 7270: IEEE Computer Society, 1996 and hereby incorporated in its entirety by reference.

Alternatively, and referring momentarily toFIG. 3, for a three-dimensional reference data set32and target data set32′, the model may be an ellipsoid76′ fit to an ellipsoidal iso-contour surface68′ to yield a three-dimensionally located center-point78′ of the ellipsoid.

In both cases, a distance and direction between this center-point78and a center-point80derived from the first displacement image50defines a refinement vector82that when added to the vector54produces a corrected displacement vector84.

This process of producing surfaces66and points78(78′) is repeated for the multiple kernels60as indicated by arrow87until corrected vectors84are produced for each such kernel60to provide a second displacement map96providing corrected displacement vector84per process block88. It will be noted that the use of a multi-dimensional model74allows precision in the axial direction to augment precision in the non-axial direction providing improved axial and non-axial estimation accuracy.

The displacement map86may then be output as an elastographic or strain image per process block90by making certain assumptions about the stress field according to techniques well known in the art. The elastographic image may, for example, produce axial, lateral, and shear strains or other similar measures indicating elasticity, strain or local biomechanical environment.

References to “a microprocessor” and “a processor” or “the microprocessor” and “the processor,” can be understood to include one or more microprocessors that can communicate in a stand-alone and/or a distributed environment(s), and can thus be configured to communicate via wired or wireless communications with other processors, where such one or more processor can be configured to operate on one or more processor-controlled devices that can be similar or different devices. These processors 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. Furthermore, references to memory, unless otherwise specified, can include one or more processor-readable and accessible memory elements and/or components that can be internal to the processor-controlled device, external to the processor-controlled device, and can be accessed via a wired or wireless network.