WO2007110669A1 - Systèmes de traitement de données images - Google Patents

Systèmes de traitement de données images Download PDF

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WO2007110669A1
WO2007110669A1 PCT/GB2007/050158 GB2007050158W WO2007110669A1 WO 2007110669 A1 WO2007110669 A1 WO 2007110669A1 GB 2007050158 W GB2007050158 W GB 2007050158W WO 2007110669 A1 WO2007110669 A1 WO 2007110669A1
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data
window
displacement
image data
determining
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PCT/GB2007/050158
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English (en)
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Joel Edward Lindop
Graham Michael Treece
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Cambridge Enterprise Limited
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
    • G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
    • G01S15/8977—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using special techniques for image reconstruction, e.g. FFT, geometrical transformations, spatial deconvolution, time deconvolution
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/485—Diagnostic techniques involving measuring strain or elastic properties
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
    • G01L1/00—Measuring force or stress, in general
    • G01L1/25—Measuring force or stress, in general using wave or particle radiation, e.g. X-rays, microwaves, neutrons
    • G01L1/255—Measuring force or stress, in general using wave or particle radiation, e.g. X-rays, microwaves, neutrons using acoustic waves, or acoustic emission
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52023—Details of receivers
    • G01S7/52036—Details of receivers using analysis of echo signal for target characterisation
    • G01S7/52042—Details of receivers using analysis of echo signal for target characterisation determining elastic properties of the propagation medium or of the reflective target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
    • G01S7/52017—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging
    • G01S7/52077—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 particularly adapted to short-range imaging with means for elimination of unwanted signals, e.g. noise or interference

Definitions

  • This invention generally relates to methods, apparatus and computer program code for processing data captured from imaging systems, in particular pulse-echo imaging systems such as ultrasonic imaging systems, in order to determine deformation data for an imaged object.
  • Ultrasonic strain imaging is usually based on displacement estimates computed using finite-length sections of the RF ultrasound signal. Amplitude variations in the ultrasound cause a perturbation in the location at which the displacement estimate is valid, and if this goes uncorrected, it is an important source of estimation noise, which is amplified when the displacement field is converted into a strain image.
  • Background prior art can be found in US 6,520,913, which shows time delay (strain) calculated on a uniform grid ( Figure 1), US 2005/165309, and US 2003/0200036.
  • Ultrasonic elasticity imaging spans a broad range of techniques that process ultrasound signals to extract information relating to tissue's mechanical properties. A majority of these techniques require high quality displacement tracking at the first stage of signal processing. Examples include quasistatic compression imaging, axial shear wave imaging and acoustic radiation force imaging in both quasistatic/impulsive and dynamic forms. The principal alternative, sonoelasticity imaging, employs Doppler velocity estimation in mechanically vibrated tissues. This is a practical technique, although the images it yields are relatively difficult to interpret. Displacement-based imaging systems have been investigated for a wide range of diagnostic purposes, spanning screening for soft tissue tumours, monitoring of atherosclerosis, assessment of skin pathologies, and examination of cardiac disease among other applications. The simplest form of meaningful visualisation is the strain image. This may be extended by analysing strain image sequences to infer material property estimates such as elastic and viscoelastic moduli.
  • the cornerstone of elasticity imaging is displacement tracking.
  • a pair of ultrasound frames recorded consecutively during a scan we refer to them as the pre- and post-deformation frames.
  • a window is placed around a point of interest in the pre- deformation frame, and the closest match in the post-deformation frame is located, hi practice, this is an optimisation problem, where the peak must be found in some suitable measure of signal similarity, such as the correlation coefficient (M. A. Lubinski, S. Y. Emelianov, and M. O'Donnell, Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation, IEEE Transactions on Ultrasonics, Ferro- electrics, and Frequency Control, 46(1): 82-96, January 1999; J. Ophir, I. Cespedes, H.
  • a strain image can be produced by displaying spatial derivatives from the estimated displacement field.
  • Strain estimation may be regarded as a stochastic process in which case the terms "mean squared error”, “estimation noise” and “estimation variance” may be used interchangeably when referring to the typical discrepancies between actual deformations and the estimates that are recorded and displayed. Errors in strain images arise mostly from two sources. The first is displacement estimation error, which is well understood. Following Carter (G. C. Carter, Coherence and time delay estimation. Proceedings of the IEEE, 75(2):236-255, 1987) and (W. F. Walker and G. E.
  • Figure Ia shows a B-mode image of RF data from a scan of a human arm.
  • the signal is temporally compressed to simulate a uniform compressive strain of 1%.
  • Figure Ib shows that the standard correlation coefficient maximiser produces a strain image that is severely degraded (and misleading) owing to the AM (amplitude modulation) effect (which we explain later), while Figure Ic shows the (near perfect) result from applying the correction technique we describe later (strain estimation for both images used windows of length 13.5 ⁇ ).
  • An ultrasound imaging system generally employs a one- dimensional or two-dimensional ultrasonic transducer array (although sometimes only a single transducer may be employed), the array comprising typically 20 to 256 transducers in each dimension.
  • Each transducer acts as both a transmitter and a receiver.
  • the transducers are generally driven by a pulse of RF energy, typically in the range 1-20 MHz; the signal may be considered narrow band in the sense that a pulse is sufficiently long to include a number of RF wavelengths thus having a relatively well- defined frequency.
  • the ultrasound transducer array is usually coupled to the tissue under investigation by an ultrasound gel or water; typically the ultrasound penetrates a few centimetres, for example up to 25cm, into the tissue under investigation, and the transducer array scans a region of a few centimetres in a lateral direction.
  • the axial resolution is generally much greater than the lateral resolution, for example of the order of 1000 samples (in time) as compared with of the order of 100 lines laterally.
  • So-called A-lines run actually from each transducer into the tissue under investigation; a so-called B-scan or B-mode image comprises a plane including a plurality of A-lines, thus defining a vertical cross section through the tissue.
  • a two-dimensional transducer array may be used to capture perpendicular B-scan images, for example to provide data for a three-dimensional volume.
  • a captured image is generally built-up by successively capturing data from along each of the A-lines in turn, that is by capturing a column of data centred on each ultrasonic transceiver in turn (although beam steering may be employed).
  • a set of the transducers is driven, with gradually increasing phase away from the line on which the transducer is centred so as to create an approximately spherical ultrasonic wavefront converging on a focus on the line under investigation.
  • the signals received from the transducers are summed with appropriate amplitude and phases to reconstruct the line data.
  • This provides an RF (radio frequency) output which is usually time-gain compensated (because the amplitude of the received signal decreases with increasing probed depth) before being demodulated, optionally log-weighted and displayed as B-scan. Often the RF data is digitised at some point in the processing chain, for example prior to the demodulation, the remainder of the processing taking place in the digital domain.
  • both signal amplitude and signal phase are employed and therefore preferred examples of an ultrasound imaging apparatus suitable for implementing embodiments of the invention employ a pair of analogue-to-digital converters to provide in-phase and quadrature digitised signal components so that phase data is available.
  • FIG. Id An outline block diagram of an ultrasonic imaging system suitable for implementing embodiments of the invention is shown in Figure Id.
  • the ultrasonic image data which is processed in embodiments of the technique comprises the digitised RF signal data, as shown in Figure Id, optionally with pre-processing in the analogue domain. (Where such pre-processing is employed it may take many forms, one example of which is illustrated).
  • the demodulated data may be processed by envelope detection and log weighting to provide a B-mode display and/or strain determination may be employed to provide a strain display.
  • the demodulation illustrated in Figure Id extracts the amplitude (envelope) and phase information of the RF signal in a conventional manner and in some preferred embodiments the signal is digitised after demodulation so that there is no need to run the A/D at (twice) the RF frequencies employed - and thus in these embodiments the processed RF signal comprises a demodulated base band signal, hi other systems the RF signal may be digitised prior to demodulation.
  • a digitised I and Q (in-phase and quadrature) signal is frequently available in conventional ultrasonic imaging equipment and, conveniently, embodiments of the invention may be implemented by processing this signal using a suitably programmed general purpose computer or digital signal processor (DSP) and/or by using dedicated hardware.
  • DSP digital signal processor
  • the AM effect is present in all displacement tracking methods that use amplitude information, including methods based on the (normalised) correlation coefficient. To eliminate the AM effect, the amplitude must be entirely suppressed, as in one-bit compression, but this can bring unwanted side effects.
  • AMC AM correction method
  • T2 ⁇ X ⁇ s is the strain estimate
  • J 1 and J 2 are the displacement estimates for windows 1 and 2 respectively
  • f x and f 2 are assumed to be the estimation locations. It is commonly assumed that Equation 1 contains only two random variables: J 1 and J 2 . Here we examine the neglected variables, f 2 and f , . New variables D and F are defined to simplify the strain calculation.
  • ⁇ is the expectation of D , which for an unbiased estimator is equal to the actual difference, D , between the displacements of the two windows
  • ⁇ is the variance of D , which is approximately equal to the sum of the variances of the individual displacement estimates, J 1 and J 2 (it is exactly equal only if errors in J 1 and J 2 are uncorrelated, which is not the case for overlapping windows)
  • ⁇ is the expectation of the reciprocal location spacing estimate, F , which may correspond to the reciprocal of the spacing between consecutive windows.
  • is the mean squared error between F and the actual reciprocal spacing, F . In general, F is not equal to the reciprocal of the window spacing, since the actual estimation locations, ⁇ 2 and ⁇ ⁇ , do not generally correspond to the window centres.
  • ⁇ s is the mean strain estimate and ⁇ - is the standard deviation.
  • ⁇ 2 . in the third term of Equation 5 becomes important when SNR 6 is evaluated.
  • the noise contribution from the AM effect is therefore proportional to the strain, s , so the AM effect is expected to become the dominant source of strain estimation noise as the level of strain increases.
  • Equation 7 is derived by substituting the RHS of Equation 5 into Equation 6.
  • Window matching tracks the displacement of the enclosed signal. However, if displacement varies within the window, then the actual signal displacement cannot be matched at all points.
  • the location at which the actual displacement of the signal is equal to the displacement estimate varies depending on both signal and displacement field properties. In general, the estimation location comes from a random distribution throughout the window. It has low probability density at the ends, and in the absence of additional information its expectation is the window centre. Where the location cannot be estimated, it is best to assume that windows sample displacement at their centres. Unfortunately this means that the AM effect introduces displacement and strain estimation noise, as illustrated in Figures 1 and 2.
  • a real ultrasound signal is not generally a pulse train or the AM effect could be corrected by noting that displacement estimation occurs at the pulse locations.
  • real ultrasound signals do incorporate amplitude variations, which are often large even over small distances.
  • Lower amplitude sections usually have lower SNR, and a good displacement estimator should incorporate a mechanism for preferentially tracking the most reliable data. Ideally it should also be possible to estimate the actual displacement location when this is not equal to the window centre.
  • a method of processing at least one-dimensional image data captured by an imaging technique to determine deformation data defining an at least one-dimensional deformation in an imaged object, said deformation data comprising at least one data pair, said data pair comprising displacement data and corresponding displacement location data comprising: inputting first and second sets of said image data, a said set of image data comprising imaging signal data including at least one of signal magnitude data and signal phase data for an imaged region of said object; positioning a first window at a first position on said first set of image data and determining a corresponding position for said first window in said second set of image data; determining first displacement data defining a first displacement estimate from said positions of said first window in said first and second image data; and determining an estimated location of said first displacement estimate responsive to said imaging signal data within said first window for at least one of said first and second sets of image data; whereby said at least one data pair comprises said first displacement data and said estimated location of said first displacement estimate.
  • the deformation data may define, for example a displacement or strain field within the object (the sets of image data will then generally correspond to different deformations of the imaged object).
  • the deformation data need not be employed to determine a strain estimate per se as there are many ways in which this data may be processed (or it may simply be displayed in a substantially unprocessed form).
  • the technique is applied to a plurality of windows, for example at successive positions in the one (or more) dimensional image.
  • the window positions may overlap, as described further later. In some applications, however, a single window position may suffice. For example where tissue is imaged there may be zero motion at the probe surface, which may be taken as a reference to provide, say, an estimate of a mean strain between the probe surface and the window position.
  • the deformation data comprises a set of data pairs
  • the method further comprises positioning a second window at a second position on said first set of image data and determining a corresponding position for said second window in said second set of image data; determining second displacement data defining a second displacement estimate from said positions of said second window in said first and second image data; and determining an estimated location of said second displacement estimate responsive to said imaging signal data within said second window for at least one of said first and second sets of image data.
  • a second data pair of the set of data pairs may comprise the second displacement data and the estimated location of said second displacement estimate.
  • the image data comprises data captured by a pulse-echo imaging technique such as ultrasonic imaging.
  • a pulse-echo imaging technique such as ultrasonic imaging.
  • embodiments of the technique may also be applied to CT (computer tomography) elasticity imaging and even, for example, to optical imaging techniques, for example for inspecting strain in skin.
  • the at least one-dimensional image data preferably comprises digitised RF (radio frequency) data, either before or after demodulation.
  • this data is in the form of in-phase and quadrature digital signals, although other data formats may also be employed.
  • one of the sets of image data defines a pre-deformation frame (here frame including one-dimensional data) and the other a post-deformation frame. It will be understood that either of these may be considered as a reference (depending upon whether positive or negative strain is considered) for the estimated displacement estimate location.
  • the deformation data may be used in any convenient manner; there are many ways in which such data may be used.
  • information derived from this data is displayed to an operator of the system, for example as an image of a displacement or strain field in the imaged object.
  • the deformation data defines strain at a plurality of locations within the imaged object although, at its simplest, only a single strain value for the imaged object may be defined.
  • a displacement estimate location for displacement data determined from a window is determined dependent upon the signal magnitude (envelope) data for corresponding windows in both the first and second sets of image data, in embodiments by determining a centroid using the signal envelopes in corresponding windows.
  • the centroid may be determined by evaluating SUM ⁇ weightings x locations ⁇ / SUM ⁇ weightings ⁇ . More particularly the method determines the centroid of signal magnitude squared when using only one set of image data or frame for the estimate or, in some preferred embodiments, the centroid of the product of the pre- and post- deformation signal magnitudes, that is using the signal magnitude data for both the first and second sets of image data or frames.
  • the determining of an estimated displacement estimate location may additionally (or alternatively) be responsive to signal phase, more particularly a relative phase difference between signals for two corresponding windows (a difference in signal phase between the first and second sets of image data).
  • estimate for d (SUM: weightings*displacements) / (SUM: weightings), where the sum is over a window.
  • this may also take into account signal phase.
  • (time or position) data can be included in the numerator and this leads to an expression for the determination of a displacement estimate location as shown, for example, in equations (28) and (31) later.
  • any signal property that can be measured may be incorporated in such a weighting approximation; signal magnitude may just be one component of the weighting expression.
  • phase weighting ((7T - ⁇ )/7r) n , which deweights large phase differences (large ⁇ ) at which, for example, phase wrapping errors are more likely.
  • phase data alone may be used to determine a displacement estimate location.
  • the determining of the window positions may include interpolating between positions corresponding to (digital) data samples. For example using the gradient at the zero phase point a window position may be determined to an accuracy of better than Vi 0 , V 100 or Viooo of a sample (for example, dependent on the level of decorrelation).
  • locating the matching window involves linearly interpolating between samples, for example envelope values of samples, to determine a more accurate position.
  • the techniques we describe generally involve cross-correlating the received image data to determine corresponding window positions in the two sets of data.
  • cross-correlation methods which determine the maximum value of a cross-correlation or, more preferably, of a cross-correlation coefficient
  • phase- based methods which estimate a window position from the phase of a value of the cross-correlation function.
  • phase-based methods which estimate a window position from the phase of a value of the cross-correlation function.
  • the captured data comprises data from a two-dimensional transducer array and two-dimensional deformation data is determined.
  • the deformation data may be used to determine an actual strain for the object or to infer or image a property of the object such as elasticity (including one or more viscoelastic moduli).
  • a greyscale or colour image of the raw data may simply be displayed.
  • the embodiments of the technique we describe are sufficiently sensitive to rely upon stresses induced by an operator's hand to generate a strain field.
  • a device such as a controlled vibration device may be employed to provide a controlled stress to the imaged object and this stress data may then be combined with deformation, in particular strain field data determined using the above described techniques in order to calculate and/or display/image one or more properties of the imaged object, such as elasticity.
  • the object comprises biological tissue, preferably living human or animal tissue although other biological material such as foodstuffs, for examples meat or fruit may be imaged.
  • MRI magnetic resonance imaging
  • MRE magnetic resonance elastography
  • the invention further provides processor control code to implement the above-described methods, for example on a general purpose computer system or on a digital signal processor (DSP).
  • the code may be provided on a carrier such as a disk, CD- or DVD- ROM, programmed memory such as read-only memory (Firmware), or on a data carrier such as an optical or electrical signal carrier.
  • Code (and/or data) to implement embodiments of the invention may comprise source, object or executable code in a conventional programming language (interpreted or compiled) such as C, or assembly code, code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array), or code for a hardware description language such as Verilog (Trade Mark) or VHDL (Very high speed integrated circuit Hardware Description Language).
  • a conventional programming language interpreted or compiled
  • code code for setting up or controlling an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array)
  • code for a hardware description language such as Verilog (Trade Mark) or VHDL (Very high speed integrated circuit Hardware Description Language).
  • Verilog Trade Mark
  • VHDL Very high speed integrated circuit Hardware Description Language
  • the invention provides apparatus for processing at least one- dimensional image data captured by an imaging technique to determine deformation data defining an at least one-dimensional deformation in an imaged object, said deformation data comprising at least one data pair, said data pair comprising displacement data and corresponding displacement location data
  • the apparatus comprising: an input for first and second sets of said image data, a said set of image data comprising imaging signal data including at least one of signal magnitude data and signal phase data for an imaged region of said object; a system for positioning a first window at a first position on said first set of image data and determining a corresponding position for said first window in said second set of image data; a system for determining first displacement data defining a first displacement estimate from said positions of said first window in said first and second image data; and a system for determining an estimated location of said first displacement estimate responsive to said imaging signal data within said first window for at least one of said first and second sets of image data; and wherein said at least one data pair comprises said first displacement data and said estimated location of said first displacement estimate.
  • Figures Ia to Id show, respectively, a B-mode image of a human arm, a conventionally generated strain image, a strain image generated using an embodiment of a method according to the invention illustrating a near-perfect result, and a block diagram of an ultrasonic imaging system for implementing embodiments of the invention;
  • Figure 2 shows sources of error in strain data estimation from captured RF image data
  • FIGS. 3 a and 3b illustrate the origin of an amplitude modulation (AM) effect
  • Figure 4 shows an example RF ultrasound signal
  • Figures 5a and 5b show, respectively, a procedure for determining estimated displacement and displacement location data from ultrasound scan data, and a procedure for displacement location correction according to an embodiment of the invention
  • Figure 6 shows an example B-scan image from simulated RF data
  • Figure 7 illustrates displacement offsets in adaptive strain estimators
  • Figure 8 shows signal-to-noise ratio against window length for Efficient Phase Zero Search (EPZS)-based techniques
  • Figures 9a and 9b show, respectively, signal-to-noise ratio against window length for CCM (Correlation Coefficient Maximiser) and EPZS and ASE (Adaptive Strain Estimator)-based techniques;
  • Figure 11 shows SNR 6 against compression factor for EPZS and CCM-based techniques at 0.01% strain
  • Figure 12 shows SNR 6 against compression factor for EPZS and CCM-based techniques at 0.5% strain
  • Figure 13 shows SNR e against compression factor for EPZS and CCM-based techniques at 4% strain
  • Figure 14 shows SNR e -strain characteristics for EPZS-based techniques
  • Figure 15 shows SNR e -strain characteristics for CCM-based techniques
  • Figure 16 compares SNR e -strain characteristics for EPZS, CCM and ASE-based techniques
  • phase-based methods operate on analytic signals with real and imaginary parts, which are produced by applying the Hubert transform (or some approximation thereof).
  • the complex cross- correlation function and its phase may be expressed as follows.
  • Ci x and ⁇ 2 are analytic ultrasound signals, * denotes the complex conjugate, nt ⁇ is the location of the beginning of the analysis window in the pre-deformation signal, T is the window length, and d is the candidate displacement applied to the post-deformation window to look for a match.
  • J n the match or displacement estimate
  • the technique is to slide the window along until a zero-crossing is detected.
  • the sliding of each successive window starts from a position which is determined by the pre-deformation window position plus the sum of the displacements of previous post- deformation windows along the A-line (so that the searching does not get progressively longer).
  • the post-deformation signal, a 2 is produced by an arbitrary temporal warping of a x , such that every point, ⁇ , (t) , undergoes a displacement, d ⁇ t) .
  • a 2 (t + d(t)) a 1 (t) (12)
  • Equation 18 may be simplified by applying the small angle approximation. iiAl+T
  • Equation 19 can be converted to an expression with clearer relevance to the physical deformation by examining the term t 2 - 1 . This is performed as follows, employing the relation from Equation 13, and expanding a Maclaurin series about d(t) .
  • t 2 -t Q n - d(t) ⁇ - ⁇ d ⁇ t 2 )- d(t) ⁇ (20)
  • phase-based displacement estimate is a weighting of point displacements by the cross power of the local signal envelope.
  • Equation 24 We substitute this into Equation 24, and rearrange to produce a convenient form for the approximation.
  • equation (28)/( ⁇ and / ⁇ are the envelopes of the received ultrasound signal in the pre- and post-deformation frames, which may be obtained from the magnitude of the digitized 1 and Q signals, either before or after demodulation. The sum is over the respective windows and, in effect, calculates the centroid of the product of these two signals. In embodiments, however some benefit may nonetheless be derived from using a simplified approximation to equation (24), for example using only one or the other envelope.
  • Equation 228 The location estimates of equation (28) are substituted into Equation 1 to refine the strain estimates.
  • the displacement estimate is weighted at each location within a window by a combination of the RF signal envelopes in a window and its corresponding window on the pre- and post-deformation frames.
  • This amplitude modulation correction also allows a more accurate identification of the image region corresponding to the space between successive displacement estimates, thereby producing a more accurate correspondence between the physical locations of tissue features, and their apparent locations in strain or displacement images.
  • equation (25) may be replaced with a higher order polynomial fit to the deformation field. Then, solving the simultaneous equations this produces (in place of equation (26)) can potentially be used to produce a more accurate estimate.
  • equation (26) can potentially be used to produce a more accurate estimate.
  • the location of the displacement estimate may be determined by the intercept of the actual displacement on this curve.
  • one or more of the parameters of the above expansion may be used directly (or indirectly) to provide the deformation data.
  • the term a ⁇ in the above expansion provides strain data directly.
  • the location of the displacement estimate may be considered as effectively comprising one or more of these parameters.
  • the above described approach can also be applied to correlation coefficient methods which have to date been the most popular approach for displacement tracking, at least for ultrasonic strain imaging.
  • the correlation coefficient for real RF signals r ⁇ and r 2 at window n with a candidate shift d is evaluated as follows:
  • d H arg max /?,.,. (n&t, d) (30)
  • the starting point is to identify the properties of stationary points (including the maximum) by differentiating p with respect to d .
  • this shows a procedure which may be employed in embodiments of the invention for determining displacement estimation data and estimation location data (in time or equivalently, spatial position).
  • scan data that is RF signal data comprising magnitude and phase data
  • a frame of data may be one-dimensional, two- dimensional or even three-dimensional.
  • data for two B-scan images is input, pre- and post-compression, each comprising a plurality of A-lines (although at minimum a single A- line for each image will suffice).
  • pre- and post-compression images i.e. images 1 and 2, then images 2 and 3, and so forth.
  • a substantially real time strain data display may be provided.
  • the first window position is initialised on an A-line, generally beginning at a point in the imaged object adjacent the transducer.
  • a window has a length of at least twice the wavelength of the ultrasound in the object.
  • the procedure steps a window in the first (pre-deformation, for example pre-compression) frame down the A-line, generally at regular intervals or according to a grid, and for each successive position looks for a match in the second (post-deformation, for example post-compression) frame; successive window positions may overlap.
  • a window is positioned in the first frame and then, at step S 506, the procedure searches for a match for the window in the second frame, typically by cross-correlation.
  • the procedure begins searching at an estimated displacement - for example for the second matching window position in the second frame, the procedure beings searching at the position in the first frame plus the displacement of the matching first window in the second frame plus the estimated displacement of the matching second window in the second frame.
  • This type of iterative procedure is described in Pesavento (ibid) and US6,520,913 (hereby incorporated by reference).
  • the procedure records the displacement (S 508), and then increments to the next window position (S510) the procedure then looping back to step S504 until there are no more window positions to process. At this point a set of window positions (time or spatial location) and corresponding displacement values has been determined.
  • the position and displacement data may be processed in a number of ways or merely recorded, for example in a data file. Typically these data are employed to calculate strain values (S512) which are then converted to greyscale data and displays (S514), although sometimes the raw displacement data is displayed (in which case the image gets progressively darker down the screen).
  • the calculation of strain values may comprise a straight forward application of equation (1) above, or may employ a more sophisticated technique, for example fitting a straight line to three, five, seven or more adjacent points, for example with a least squares fit, to determine a strain value. Other techniques may also be employed.
  • the strain may be generated by small motions of an ultrasonic probe or a substantially known stress field may be generated automatically.
  • Figure 5b shows a procedure for implementing an embodiment of a technique according to the invention. Although this is shown at a separate procedure, in practice it may often be convenient to run the steps of the procedure in parallel with steps S504 to S510 of the procedure of Figure 5a, that is in parallel with the window positioning and matching described above. Alternatively the procedure may be performed between steps S508 and S510 of Figure 5a, or between steps S510 and S512.
  • inputs to the procedure comprise a set of displacement and displacement location data and either raw or demodulated RF signal data, preferably as in-phase quadrature digitised RF signal data, to provide the envelope and phase information shown in Figure 4 (step S550).
  • the procedure then operates with successive pairs of windows in the first and second (pre-and post-deformation) frames mentioned with respect to Figure 5a above, generally employing at least the envelope of the RF signal data from each window of a pair (S 552).
  • the procedure calculates a weighted average estimated distance into the relevant window for the displacement estimate, that is a position within the window at which the displacement estimate is considered to apply, hi one embodiment the procedure determines
  • the procedure may determine an estimated location for the displacement estimate based on a sum of a product of weightings and signal value samples, in particular based on:
  • weightings have been described above and employ the magnitude and/or phase information from one or both windows of a pair, hi general W(m) comes from an approximation for d as SUM (weightings * d) / SUM weightings.
  • the weighting effectively comprises a centroid of the envelopes of both windows of a pair of matching windows in the pre- and post- compression frames. This is illustrated in step S554, where the sum is over the number of (A/D) samples, N in each window, the weighted average estimated distance into a window being calculated in terms of a number of samples (time, or equivalently spatial position).
  • interpolation between samples may be employed for increased accuracy.
  • other weightings may be employed, for example in a simpler calculation the squared envelope of a single window of a pair.
  • step S554 the estimated distance through the window in terms of number of samples ( m ) defines a percentage of the linear length of the window (N samples) which is used to modify the relevant displacement location (S 556).
  • the procedure then continues (S558) as before (steps S512, S514), making use of the corrected data in any desired manner, for example to display a corrected strain image of an object as shown in Figure Ic.
  • AMC increases the utility of displacement estimates from a spatially varying displacement field by estimating the actual estimation location.
  • An alternative approach for handling the AM effect is to reduce the level of amplitude variation, for example by log compression of the signal envelope. This may be a useful technique in some circumstances, but the AM effect may actually be beneficial for high quality displacement estimation.
  • Adaptive strain estimators work on the principle of reversing the deformation that has occurred, to obtain the best match to the pre-deformation signal.
  • ID an adaptive strain estimator uniformly stretches the post-deformation window until its similarity to the pre-deformation window is maximised. If the local strain is actually uniform, adaptive strain estimation has the advantage of being able to correctly match the displacement at every point within the window. This means that for uniform strains the question of estimation location is ⁇ elevant, because the correct displacement can be found everywhere. Tests of adaptive strain estimation on uniform strain simulations are therefore expected to be independent of the AM effect, and it is for this reason that we employ an adaptive strain estimator as our AM suppression benchmark.
  • Simulated RF ultrasound data has been generated using Field II (J. A. Jensen. Field: a program for simulating ultrasound systems, In Proceedings of the l ⁇ " Nordic-Baltic Conference on Biomedical Imaging, volume 4, pages 351-353, 1996).
  • the simulations have 2x lO 5 scatterers positioned at random according to a uniform distribution throughout a 50x 50x 6 mm volume, with random scattering strengths distributed uniformly over the range [0, ⁇ max ] .
  • the probe parameters model the 5-10 MHz probe of the Dynamic Imaging Ltd (UK) Diasus ultrasound machine, for which the point spread function has been measured experimentally — the pulse has a centre frequency of 6.0 MHz and bandwidth 2.1 MHz — and the sampling frequency is 66.7 MHz.
  • phase, correlation coefficient and adaptive strain estimators For comparative purposes, we tested phase, correlation coefficient and adaptive strain estimators. The performance of phase and correlation coefficient estimators is compared for several variations: uncorrected strain estimation, log compression, limit log compression and AMC. Quantitative tests use simulation data, where the performance is measured by evaluating SNR 6 ; the strain standard deviation is calculated from the raw strain estimates, where no smoothing has been applied. For a qualitative assessment, we also present example images from in vitro and in vivo scans. Fair comparison is made possible by fixing the window parameters across all of the estimators in each test.
  • the efficient phase zero search adapts the concept of Pesavento et al. (ibid).
  • a 5-10 MHz filter is applied to the RF data (T 1 , r 2 ) before converting to analytic signal representations ( «, , a 2 ), which are modulated to the baseband (a bl , a b2 ) to enhance the accuracy of linear interpolation.
  • a b2 is estimated at subsample locations by baseband linear interpolation, to enable accurate subsample estimation of d (for a discussion of interpolation frequency responses, see Proakis and Manolakis (J. G. Proakis and D. G.
  • phase is preserved but the amplitude is partially suppressed when the signal is log compressed according to the following formula.
  • c is the compression factor. The larger the value of c , the smaller the amount of amplitude information that is retained, since the size of variations in the log compressed amplitude becomes smaller compared to the mean value.
  • EPZS_L1 As c — > oo all of the amplitude information is discarded, since log compressed amplitude variations become infinitely smaller than the mean. Limit log compression has a simpler form.
  • EPZS_L2 For phase-based methods, EPZS_L2 is the counterpart of one-bit compression or zero crossing techniques in correlation coefficient methods. We also present results for EPZS with AMC, referred to as EPZS_A.
  • EPZS_A In addition to producing analytic signals, we detect the signal envelope, ⁇ a ⁇ , which is exploited as follows for AMC estimation of f fry (c.f. Equation 28). ⁇ t
  • EPZS_L2 uses no amplitude information; so the AMC version of fonne following equation (35) can be identical to the window centre assumption. However, EPZSJLl still exhibits a degree of AM susceptibility, so results arc presented for an algorithm combining EPZS_L1 with AMC (operating on the log compressed signal envelope), referred to as EPZS_LA.
  • the correlation coefficient maximiser searches initially at integer sample locations for the maximum value of the cross correlation coefficient (see Equation 29).
  • the estimate is refined by allowing subsample values of d and interpolating r 2 at subsample locations.
  • a complex baseband representation of r 2 allows highly accurate subsample interpolation, as with EPZS, but in CCM it is converted back to a subsample real signal for the correlation coefficient calculation. This requires the following calculation, where ⁇ m is the modulation frequency that was used earlier to shift the analytic signal down to the baseband.
  • ⁇ is again usually assumed to be the window centre (Equation 32).
  • Log compression (CCM_L1) is tested as a means of reducing the error in f , using the following formula:
  • the full RF signal is used for subsample interpolation of r 2 , which is only log compressed at the moment of computing the correlation coefficient.
  • r 2 which is only log compressed at the moment of computing the correlation coefficient.
  • c ⁇ ⁇ variations in the log compressed signal magnitude become infinitely smaller than the mean magnitude, so only the sign is important.
  • a simpler expression may be used.
  • CCM_L2 Notes that Subsample interpolation actually still employs the full RF signal, so zero crossings are identified with high accuracy.
  • CCM_L2 We call this variation CCM_L2, although it could be described as one-bit compression and is similar to a zero-crossing method.
  • AMC is applied to CCM following Equation 31, which is referred to as CCM_A.
  • AMC is also applied alongside non-limiting log compression in CCM_LA.
  • EPZS The initialisation of EPZS depends on the fact that the displacement at the top of each A-line is zero. Similarly, ASE searches only over strain (and not over displacement) in the top window of each A-line. This utilises the knowledge that a search over displacement could only degrade the accuracy of the estimate in the event that a nonzero displacement were found for the top of the window.
  • the displacement at subsequent windows is estimated accurately by integrating the estimated strains, where it will be recognised that integration is a noise-suppressing operation.
  • the offset of the first sample in a succeeding overlapping window is, of course, not equal to the displacement at the end of the first window. Rather, the relationship we assume is illustrated in Figure 7, where estimated strains are displayed as gradients on a plot of displacement against time.
  • the window strain estimate multiplied by the window length, Ts,,- ⁇ > provides the best estimate for the displacement difference between the end and the start.
  • the following window is therefore pinned at this end point, and stretched on either side to find the next estimate, S n ⁇
  • This means that the offset displacement at the start of window n depends on: the offset of window n -l , the previous window stretch, and the candidate window stretch, S .
  • H 1 and n 2 have zero mean, with power . They are mutually uncorrelated, and both noise signals are uncorrelated with r .
  • n x and n 2 consist not only of electronic noise — other sources of uncorrelated signal components include morphological changes to the speckle pattern and non-axial scatterer motion. The SNR can be expressed in terms of these signal components.
  • Equation 44 The constant of proportionality in Equation 44, C 1 , must be a large number, since the short windows produce inaccurate estimates. However, the generic estimator actually uses longer windows, yielding a weighted average of the single-sample estimates.
  • d n is the final displacement estimate at window n
  • W(t) is the weighting for estimate d'(t) . If errors in the single-sample estimates are mutually uncorrelated, then the variance of the overall estimate is as follows.
  • the expected noise term is assumed constant ( C 3 ). More sophisticated noise estimates are possible if assumptions can be made about the statistical properties of the noise source, but we restrict ourselves to the most general approach (note, E[V 1 j ⁇ E[ ⁇ ] "1 , so
  • the expectation of the local cross power of the recorded signals is equal to the expected signal power.
  • Weighting by this formula minimises the expected value of ⁇ 2 - .
  • Results for EPZS, EPZS_A, CCM, CCM_A and ASE with window lengths, T , in the range 2.8-27.1 ⁇ indicate a suitable choice of T for the later tests. They also serve as a first opportunity for assessing the AMC technique.
  • Figures 8 and 9a show performance against window length at 0.5% strain, while Figure 9b shows the effect of window length on EPZS_A and ASE at a higher strain. 13.52 is employed for all other results herein.
  • FIG. 8 shows SNR e against window length for EPZS and EPZS__A, with both 71 dB and 20 dB data at 0.5% strain.
  • SNR e is initially a linear function of window length, and it continues to increase for long windows, although the gradient becomes less steep.
  • Figure 9a shows SNR e against window length for CCM and CCM_A, with both 71 dB and 20 dB data at 0.5% strain. Uncorrected CCM is almost identical to EPZS. However, AMC is obviously less accurate for CCM, since the improvement with CCM_A is much smaller and the results are erratic for long windows.
  • Figure 9b shows SNR e against window length for EPZS_A and ASE, with 20 dB data at 4% strain. ASE performs less well with short windows, but it reaches a high and fairly constant level of performance for T > 10/t .
  • EPZS_A by contrast, performs well with short windows and has a higher peak SNR e . However, windows with T > l ⁇ have a differential displacement of
  • the performance of EPZS and CCM is similar, though EPZS A performs considerably better than CCM_A.
  • the characteristics of the images can be compared with the corresponding SNR 6 results from the graphs.
  • the images have a linear scale with 0 (black) representing zero strain, 127.5 (mid-grey) is the simulated strain of 0.5% and 255 (white) represents 1%. Saturation occurs at 0 and 255, and no smoothing has been applied, so each section between successive estimation locations has constant brightness. An ideal estimator would yield a uniform greyscale level, but this is unachievable in practice.
  • Figure 11 shows SNR 6 results for EPZS-Ll, EPZS_LA, CCM_L1 and CCM_LA with 20 dB data at 0.01% strain as a function of c , the compression factor. At low strains, the main effect of log compression is increased noise. This effect is more pronounced with CCMJLl. AMC has almost no effect in these images.
  • Figure 12 shows SNR e results for EPZS-Ll, EPZSJLA, CCM_L1 and CCM_LA with
  • EPZS-Ll, EPZS_LA, CCM_L1 and CCM_LA with 20 dB data at 4% strain as a function of c , the compression factor. At this strain all of the algorithms can be improved by applying an appropriate level of log compression. The greatest improvement is exhibited by EPZS-Ll, while the ACM algorithms are still degraded by high compression factors, and they eventually converge with the curves where AMC has not been applied. Strain dependence
  • Figures 14-16 compare the performance of EPZS, EPZS-Ll, EPZS_L2, EPZS_LA, EPZS_A, CCM, CCMJLl, CCM JL2, CCM_LA, CCM_A and ASE across a range of strains. More particularly, Figure 14 shows SNR e -strain characteristics for the EPZS family of algorithms with 20 dB data. EPZS_A has the best performance across a wide range of strains, although the SNR 6 is lower at high strains and at 4% the best performance is from EPZS_LA.
  • Figure 15 shows SNR 6 -strain characteristics for the CCM family of algorithms with 20 dB data.
  • EPZS_A CCM_A and ASE with 20 dB data. These are the best algorithms from each of the three families. EPZS_A performs best across most strains, though ASE does slightly better at 4%, where the other algorithms have lower SNR e owing to significant decorrelation.
  • Equation 31 was based on intuition based on our previous insights since the derivation of a superior CCM_A algorithm is a considerably more challenging mathematical problem than EPZS_A.
  • Figure 9b confirms that ASE offers an alternative route to high-performance strain estimation, hi particular, it is possible to achieve good performance using arbitrarily long windows. This means that locations of extremely high strain will not be subject to reduced SNR e when the window length has been chosen for optimal performance at a range of lower expected strains. It is also interesting to note that EPZS_A actually outperforms ASE for short window lengths, and EPZS_A has the higher peak performance. EPZS_A performs less well with longer windows, where high strains cause significant decorrelation.
  • AMC is of less benefit.
  • CCM and CCM_A are degraded less severely by log compression, since the retention of phase information makes these algorithms more robust.
  • CCM only uses the real signal, so ⁇ & 2 increases rapidly with log compression as information is discarded.
  • EPZS_L1 and slight log compression also improves CCM_L1. Better performance is achieved by the AM corrected algorithms, although these are still degraded by log compression. EPZS_A and CCM_A eventually converge with the uncorrected curves as c -> co . Log compression is most beneficial at the higher strain in Figure 13. Estimation noise here comes mostly from ⁇ ? , so EPZSJLl performs much better when a high level of log compression is applied. CCM_L1 is also improved by high log compression, although it peaks at a relatively low value of c . The AMC algorithms are also improved by slight log compression, indicating that the AMC formulae are less accurate at high strain, so a combination of AMC and log compression yields the lowest location variance.
  • EPZS LA is the best at 4% strain, so the combination of AMC with moderate log compression may be the best noise suppression strategy at high strains.
  • Figure 16 compares the best estimators from each family of algorithms.
  • EPZS_A has the best performance at most strains by a large margin.
  • the worst algorithm is ASE. This may indicate that the signal stretching technique is inherently more noisy, although at higher strains its advantages are the absence of the AM effect and lower signal decorrelation. Therefore, ASE outperforms CCM_A for strain >2%, at 4% it also outperforms EPZS_A, and the gradient of the curve is still positive, so ASE may offer further performance benefits at yet higher strains.
  • the main advantage of ASE is the relative independence of performance and window length.
  • EPZS_A can (depending on window length) outperform ASE by a large margin.
  • EPZSJL2 is provably unaffected by amplitude variations, so real tissue features must also appear in Figure 18d.
  • the dark patch is absent, proving that it is actually an artefact.
  • a mild artefact is also observed with EPZS_A in Figure 18e, where the local sparseness of estimation locations around the reflection causes a textural change in its vicinity.
  • EPZS_L2 can outperform some of the other estimators and there are computational advantages if all of the amplitude information can be discarded.
  • EPZS_A is inferior to the EPZS_A algorithm incorporating AMC, which is marginally more computationally expensive, but still suitable for real-time strain imaging.
  • EPZS_A outperformed all of the other algorithms tested in this study throughout the typical range of strains encountered in practical ultrasonic strain imaging systems.

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Abstract

L'invention se rapporte en général à des procédés, à des appareils et à un code de programme informatique permettant de traiter des données capturées par des systèmes d'imagerie, en particulier par des systèmes d'imagerie par échos d'impulsions tels que des systèmes d'imagerie ultrasonores, afin de déterminer des données de déformation pour un objet imagé. L'invention concerne un procédé destiné à traiter des données au moins unidimensionnelles capturées à l'aide d'une technique d'imagerie pour déterminer des données de déformation définissant une déformation au moins unidimensionnelle dans un objet imagé, lesdites données de déformation contenant au moins une paire de données, laquelle comporte des données de déplacement et des données de localisation de déplacement correspondantes. Ledit procédé consiste : à entrer des premier et second jeux de données images, lesdits jeux de données images contenant des données de signal d'imagerie comportant des données d'amplitude de signal et/ou des données de phase de signal pour une région imagée dudit objet; à placer une fenêtre à une position donnée sur le premier jeu de données images, et à déterminer une position correspondante pour ladite fenêtre dans le second jeu de données images; à déterminer des données de déplacement définissant une estimation de déplacement à partir desdites positions de la fenêtre dans les premières et secondes données images; et à déterminer un emplacement estimé de ladite estimation de déplacement en réponse aux données de signal d'imagerie contenues dans ladite fenêtre pour le premier jeu de données images et/ou le second jeu de données images. La ou les paires de données contiennent les données de déplacement et l'emplacement estimé de l'estimation de déplacement.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7905835B2 (en) 2008-01-15 2011-03-15 General Electric Company Method for assessing mechanical properties of an elastic material
US8416301B2 (en) 2007-05-01 2013-04-09 Cambridge Enterprise Limited Strain image display systems
WO2015162543A1 (fr) * 2014-04-23 2015-10-29 Imagistx Inc Systèmes de traitement d'image médicale, et leurs procédés
CN114283086A (zh) * 2021-12-23 2022-04-05 深圳航天科技创新研究院 一种基于信号域与图像域联合滤波的磁共振影像降噪方法
DE102013002065B4 (de) 2012-02-16 2024-02-22 Siemens Medical Solutions Usa, Inc. Visualisierung von zugehörigen lnformationen bei der Ultraschall-Scherwellenbildgebung
DE102009033286B4 (de) 2008-07-16 2024-04-04 Siemens Medical Solutions Usa, Inc. Scherwellenbildgebung

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111449684B (zh) * 2020-04-09 2023-05-05 济南康硕生物技术有限公司 心脏超声标准扫查切面快速获取方法及***

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6520913B1 (en) 1998-05-29 2003-02-18 Lorenz & Pesavento Ingenieurbüro für Informationstechnik System for rapidly calculating expansion images from high-frequency ultrasonic echo signals
US20030200036A1 (en) 2002-04-22 2003-10-23 Seshadri Srinivasan Method and apparatus for feature tracking strain estimation for elastography
US20050165309A1 (en) 2004-01-27 2005-07-28 Tomy Varghese Ultrasonic elastography providing axial, orthogonal, and shear strain

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6520913B1 (en) 1998-05-29 2003-02-18 Lorenz & Pesavento Ingenieurbüro für Informationstechnik System for rapidly calculating expansion images from high-frequency ultrasonic echo signals
US20030200036A1 (en) 2002-04-22 2003-10-23 Seshadri Srinivasan Method and apparatus for feature tracking strain estimation for elastography
US20050165309A1 (en) 2004-01-27 2005-07-28 Tomy Varghese Ultrasonic elastography providing axial, orthogonal, and shear strain

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ANDREAS PESAVENTO ET AL: "A Time-Efficient and Accurate Strain Estimation Concept for Ultrasonic Elastography Using Iterative Phase Zero Estimation", IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS AND FREQUENCY CONTROL, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 46, no. 5, September 1999 (1999-09-01), pages 1057 - 1067, XP011090108, ISSN: 0885-3010 *
CESPEDES I ET AL: "REDUCTION OF IMAGE NOISE IN ELASTOGRAPHY", ULTRASONIC IMAGING, DYNAMEDIA INC., SILVER SPRING, MD, US, vol. 15, April 1993 (1993-04-01), pages 89 - 102, XP000861591, ISSN: 0161-7346 *
KAISAR ALAM S ET AL: "An Adaptive Strain Estimator for Elastography", IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS AND FREQUENCY CONTROL, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 45, no. 2, March 1998 (1998-03-01), pages 461 - 472, XP011062956, ISSN: 0885-3010 *
LINDOP ET. AL.: "Estimation of displacement location for enhanced strain imaging", INTERNET, 30 March 2006 (2006-03-30), pages 1 - 28, XP002445835 *
LINDOP, J.E.; TREECE, G.M.; GEE, A.H.; PRAGER, R.W.: "3D Elastography using freehand ultrasound", INTERNET, 28 July 2005 (2005-07-28), pages 1 - 21, XP002446313 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8416301B2 (en) 2007-05-01 2013-04-09 Cambridge Enterprise Limited Strain image display systems
US7905835B2 (en) 2008-01-15 2011-03-15 General Electric Company Method for assessing mechanical properties of an elastic material
DE102009033286B4 (de) 2008-07-16 2024-04-04 Siemens Medical Solutions Usa, Inc. Scherwellenbildgebung
DE102013002065B4 (de) 2012-02-16 2024-02-22 Siemens Medical Solutions Usa, Inc. Visualisierung von zugehörigen lnformationen bei der Ultraschall-Scherwellenbildgebung
WO2015162543A1 (fr) * 2014-04-23 2015-10-29 Imagistx Inc Systèmes de traitement d'image médicale, et leurs procédés
US10492763B2 (en) 2014-04-23 2019-12-03 Exact Imaging Inc. Medical image processing systems and methods thereof
CN114283086A (zh) * 2021-12-23 2022-04-05 深圳航天科技创新研究院 一种基于信号域与图像域联合滤波的磁共振影像降噪方法

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