Patent Description:
In medical ultrasound imaging, image contrast is often compromised as a result of acoustic clutter due to off-axis scattering, reverberation clutter due to near-field anatomical structures, and random electronic noise. Several techniques have been proposed in the literature to address these issues. The techniques can be broadly categorized into two main groups: <NUM>) Coherence-based adaptive weighting and <NUM>) Adaptive beamforming.

Adaptive weighting techniques such as the coherence factor (CF), the generalized coherence factor (GCF), the phase coherence factor (PCF) and the short-lag spatial coherence (SLSC) all require access to per-channel data to compute a weighting mask to be multiplied to the image. With apodization-based adaptive weighting methods, such as the dual apodization with cross-correlation (DAX) and its variants, weighting masks can be computed without having access to the per-channel data and hence, hardware implementation of these methods may be slightly easier. However, all adaptive weighting methods operate by weighting down the conventional image with a weighting mask. This may lead to problems such as reduced image brightness, removal of anatomical detail, and increased speckle variance.

Adaptive beamforming techniques such as the minimum variance (MV) beamforming typically involve adaptively calculating the complex apodization values from the channel data such that only mainlobe signals are passed and off-axis signals are rejected. However, MV beamforming is developed mainly for spatial resolution improvement. Currently, MV beamforming is not effective in suppressing reverberation clutter, which is often correlated with the mainlobe signals. In many cases, reverberation clutter is the dominant source of image quality degradation in vivo. Furthermore, MV beamforming is highly sensitive to phase aberration, element directivity, and signal-to-noise ratio. MV beamforming is also known to produce artifacts in speckle.

Recently, a spatial filtering technique based on autoregressive (AR) model was introduced in medical ultrasound. The AR model is described in detail in "<NPL>. However, the AR model-based filtering technique described by Shin and Huang converts an autoregressive-moving-average (ARMA) problem into an AR problem and applies linear prediction. The AR technique minimizes the prediction error energy to find a solution to a problem where both the prediction error filter (PEF) and the noise are unknowns. Thus, the technique of Shin and Huang may lead to inaccurate signal modelling and hence, a suboptimal performance.

<NPL>, discloses a frequency space (F-X) domain filter based on an autoregressive-moving-average (ARMA) model where a prediction error filter is estimated by solving an Eigen problem to enhance the signal-to-noise-ratio of 2D seismic wavefields. Noise is estimated by deconvolving the prediction error filter, and subtracted from the signal data to obtain clean signal data.

<NPL>, discloses to use an autoregressive-moving-average (ARMA) model for estimating an ultrasound imaging pulse based on a prediction error method, even in the presence of noise.

The systems, methods, and/or apparatuses described herein may improve image contrast with a spatial filtering technique based on autoregressive-moving average (ARMA) model. Generally, this technique adaptively computes a spatial predictive error filter (PEF) from channel data (a previous unknown from ARMA) and then estimates and subtracts an additive noise sequence that contains contributions from off-axis clutter, reverberation clutter, and/or random noise. This technique may filter out undesirable signals that contribute to reduced image contrast directly from the ultrasound channel data. In order to filter out the undesirable signals, noise may be treated as a sequence of random innovations instead of an additive process.

ARMA modeling of radio frequency (RF) signals from the channel data results in an eigenvalue problem. A prediction error filter (PEF) may be computed from Eigen decomposition of a covariance matrix of noisy channel data. The PEF may be applied to the noisy channel data to estimate a noise sequence (e.g. any data not modeled by the RF signals). The estimated noise sequence may be subtracted from the original channel data. The remaining signal may be "clean" data that may be used to generate an ultrasound image which may have improved contrast compared to the original channel data. The ARMA process described may be repeated one or more times on the resulting clean data, which may further improve the resulting image.

According to an exemplary embodiment of the disclosure, a method includes acquiring ultrasound channel data, generating a predictive error filter with an autoregressive moving average model, estimating, with the predictive error filter, noise in the ultrasound channel data, and subtracting the noise from the ultrasound channel data to obtain clean channel data, wherein the generating step, estimating step and subtracting step are performed in a time-space domain. Estimating the predictive error filter with the autoregressive moving average model may include generating a multi-order model of the predictive error filter, converting the multi-order model and the ultrasound channel data to matrix form, solving an eigenvalue problem for the predictive error filter, wherein the eigenvalue problem comprises a correlation matrix of a noisy sequence times the predictive error matrix equal to a variance of noise times the predictive error matrix, wherein a solution of the eigenvalue problem for the predictive error filter is an eigenvector corresponding to a minimum eigenvalue of the correlation matrix of the noisy sequence. Estimating the noise in the ultrasound channel data may include deconvolving the predictive error filter and the noisy sequence.

According to an exemplary embodiment of the disclosure, an ultrasound imaging system includes an ultrasound transducer array configured to transmit and receive ultrasound signals, at least one channel operatively coupled to the ultrasound transducer configured to transmit channel data based, at least in part, on the received ultrasound signals, and a signal processor operatively coupled to the at least one channel, wherein the signal processor is configured to acquire the channel data from the at least one channel, generate a predictive error filter with an autoregressive moving average model, estimate, with the predictive error filter, noise in the channel data, and subtract the noise from the channel data to obtain clean channel data, wherein the generating step, estimating step and subtracting step are performed in a time-space domain.

According to a further exemplary embodiment of the disclosure, a method includes acquiring beamsum data, the beamsum data responsive to a plurality of ultrasound transmit events in a plurality of directions, generating a predictive error filter with an autoregressive moving average model, estimating, with the predictive error filter, noise in the beamsum data, and subtracting the noise from the beamsum data to generate clean data, wherein the generating step, estimating step and subtracting step are performed in a time-space domain.

The following description of exemplary embodiments is merely exemplary in nature and is in no way intended to limit the invention or its applications or uses. In the following detailed description of embodiments of the present systems and methods, reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of illustration specific embodiments in which the described systems and methods may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the presently disclosed systems and methods, and it is to be understood that other embodiments may be utilized and that structural and logical changes may be made without departing from the scope of the present system.

The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present system is defined only by the appended claims. The leading digit(s) of the reference numbers in the figures herein typically correspond to the figure number, with the exception that identical components which appear in multiple figures are identified by the same reference numbers. Moreover, for the purpose of clarity, detailed descriptions of certain features will not be discussed when they would be apparent to those with skill in the art so as not to obscure the description of the present system.

According to principles of the disclosure, to overcome at least some of the shortcomings of the AR technique, a spatial filtering technique based on an ARMA model is applied which may suppress random noise, acoustic clutter, and/or reverberation clutter to enhance image contrast. This technique adaptively computes a spatial PEF from ultrasound channel data, and then estimates and subtracts the estimated noise sequence that contains contributions from off-axis clutter, reverberation clutter and/or random noise.

The ARMA filtering technique (or simply ARMA method) described herein is distinct from the adaptive weighting and adaptive beamforming techniques described in the background of the disclosure. For example, the ARMA filtering technique does not use weighting masks for pixel-by-pixel weighting of the original image, and the ARMA filtering technique does not adaptively compute the complex apodization values to form an image. Rather, the ARMA filtering technique filters out undesirable signals that may contribute to reduced image contrast directly from ultrasound channel data.

Briefly, the ARMA filtering technique described herein allows signals received from a given direction, which may appear as "linear events" immersed in noise in the time-space (T-X) domain (e.g., aperture domain), may be properly represented by means of an ARMA model. In the method described herein, the noise is treated as a sequence of random innovations instead of an additive process.

ARMA modelling of ultrasound channel radio frequency (RF) signals may result in an eigenvalue problem in which a PEF may be computed from Eigen-decomposition of a covariance matrix of the original, noisy channel data. The computed PEF may be applied to the noisy channel data to estimate a colored (e.g., non-white noise) noise sequence from which an additive noise sequence is estimated. The additive noise sequence may include all the signals that are not modelled by the ARMA model, for example, random noise, off-axis clutter, and/or reverberation clutter. The estimated additive noise sequence may be subtracted from the original, noisy channel data to yield "clean" data. The clean data may be used to form an ultrasound image. The clean data may be used as an input into an iterative ARMA model. That is, the ARMA filtering technique described may be performed multiple times to yield clean data.

An ultrasound imaging system capable of performing the ARMA filtering technique according principles of the current disclosure includes an ultrasound transducer array configured to transmit and receive ultrasound signals and at least one channel operatively coupled to the ultrasound transducer configured to transmit channel data based, at least in part, on the receive ultrasound signals. The ultrasound imaging system further includes a signal processor operatively coupled to the channel. The signal processor is configured to acquire the at least one channel data from the channel, generate a PEF with an ARMA model, estimate, with the PEF, noise in the channel data, and subtract the noise from the ultrasound data to obtain clean channel data, wherein the generating step, estimating step and subtracting step are performed in a time-space domain.

Referring to <FIG>, an ultrasound imaging system <NUM> constructed in accordance with the principles of the present disclosure is shown in block diagram form. In the ultrasonic diagnostic imaging system of <FIG>, an ultrasound probe <NUM> includes a transducer array <NUM> for transmitting ultrasonic waves and receiving echo information. A variety of transducer arrays are well known in the art, e.g., linear arrays, convex arrays or phased arrays. The transducer array <NUM>, for example, can include a two dimensional array (as shown) of transducer elements capable of scanning in both elevation and azimuth dimensions for 2D and/or 3D imaging. The transducer elements of transducer array <NUM> may be coupled via channels <NUM> to a microbeamformer <NUM> in the probe <NUM> in some embodiments. A separate channel may be provided for each transducer element of the transducer array <NUM> or for each patch of transducer elements. However, for clarity of the diagram, only one line is illustrated for the channels <NUM> in <FIG>. The microbeamformer <NUM> may control transmission and reception of signals by the transducer elements in the array. In this example, the microbeamformer <NUM> is coupled by the probe cable to a transmit/receive (T/R) switch <NUM>, which switches between transmission and reception and protects the main beamformer <NUM> from high energy transmit signals. In some embodiments, the T/R switch <NUM> and other elements in the system can be included in the transducer probe rather than in a separate ultrasound system base. The transmission of ultrasonic beams from the transducer array <NUM> under control of the microbeamformer <NUM> is directed by the transmit controller <NUM> coupled to the T/R switch <NUM> and the beamformer <NUM>, which receives input from the user's operation of the user interface or control panel <NUM>. One of the functions controlled by the transmit controller <NUM> is the direction in which beams are steered. The partially beamformed signals produced by the microbeamformer <NUM> are coupled to a main beamformer <NUM> where partially beamformed signals from individual patches of transducer elements are combined into a fully beamformed signal.

In some embodiments, the microbeamformer <NUM> is omitted. The transmit controller <NUM> may control the transducer array <NUM> directly through the T/R switch <NUM>. Data from the transducer array <NUM> elements may be transmitted via channels <NUM> to the main beamformer <NUM>.

The beamformed signals are coupled to a signal processor <NUM>. The signal processor <NUM> can process the received echo signals in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. The signal processor <NUM> may also perform additional signal enhancement such as speckle reduction, signal compounding, and noise elimination. The signal processor <NUM> may be implemented in hardware (e.g., Application Specific Integrated Circuit (ASIC)), software, or a combination thereof.

As shown in <FIG>, in some embodiments, the signal processor <NUM> may receive ultrasound signals from the channels <NUM> prior to beamforming by beamformer <NUM>. For example, the signal processor <NUM> may apply the ARMA filtering technique described above to the signals from the channels <NUM> (e.g., channel data). In some embodiments, the beamformer <NUM> may provide appropriate delays and/or geometrical alignment to each channel <NUM>, and signal processing may be performed by the signal processor <NUM> prior to summing of the channel signals. In some embodiments, the signal processor <NUM> may perform the ARMA filtering technique on the channel data and receive commands from the beamformer <NUM> for summing of the channel signals. The signal processor <NUM> may apply any desired and/or required summations prior to providing the signals to the B-mode processor <NUM>.

In some embodiments, as shown by block 22a in dashed lines in <FIG>, beamformer <NUM> may be implemented as two units, one for providing appropriate delays and/or geometrical alignment and another unit may be included for providing summing and/or other combining of channel data if necessary. In some embodiments, microbeamformer <NUM> may provide delays and/or geometrical alignment to the channels <NUM> and the beamformer <NUM> may be implemented after the signal processor <NUM> to provide summing and/or other combining of channel data. In some embodiments, as shown by blocks 26a in dashed lines in <FIG>, signal processor <NUM> may be implemented as multiple units. For example, a first signal processor may be implemented prior to beamformer <NUM> to perform signal processing directly to the channel data, and a second signal processor may be implemented after the beamformer <NUM> for additional processing (e.g., bandpass filtering).

Alternatively, in the context of plane wave imaging (PWI) or diverging wave imaging (DWI), the ARMA filtering technique may be implemented in the transmit beamspace domain rather than in the channel domain. In PWI/DWI, a broad transmit beam in the form of either a plane wave or a diverging wave is emitted in a particular direction by the transducer array <NUM> and all scan lines (e.g. beamsum signals) may be generated using the received per-channel data via a delay-and-sum (DAS) beamforming approach performed by the beamformer <NUM> and/or microbeamformer <NUM>. This may result in the acquisition by the ultrasound imaging system <NUM> of a low quality ultrasound image, but the image maybe acquired with a single transmit event. In PWI/DWI, such a process may be repeated for multiple transmit directions and the beamsum signals obtained from each transmit direction may be coherently compounded to produce a high quality ultrasound image. In some pre-existing ultrasound imaging systems, this version of the ARMA filtering technique may be easier to implement.

Continuing the description of the elements of <FIG>, the processed signals are coupled to a B mode processor <NUM>, which can employ amplitude detection for the imaging of structures in the body. The signals produced by the B mode processor are coupled to a scan converter <NUM> and a multiplanar reformatter <NUM>. The scan converter <NUM> arranges the echo signals in the spatial relationship from which they were received in a desired image format. For instance, the scan converter <NUM> may arrange the echo signal into a two dimensional (2D) sector-shaped format, or a pyramidal three dimensional (3D) image. The multiplanar reformatter <NUM> can convert echoes which are received from points in a common plane in a volumetric region of the body into an ultrasonic image of that plane, as described in <CIT>). A volume renderer <NUM> converts the echo signals of a 3D data set into a projected 3D image as viewed from a given reference point, e.g., as described in <CIT>) The 2D or 3D images are coupled from the scan converter <NUM>, multiplanar reformatter <NUM>, and volume renderer <NUM> to an image processor <NUM> for further enhancement, buffering and temporary storage for display on an image display <NUM>. The graphics processor <NUM> can generate graphic overlays for display with the ultrasound images. These graphic overlays can contain, e.g., standard identifying information such as patient name, date and time of the image, imaging parameters, and the like. For these purposes the graphics processor receives input from the user interface <NUM>, such as a typed patient name. The user interface can also be coupled to the multiplanar reformatter <NUM> for selection and control of a display of multiple multiplanar reformatted (MPR) images.

Returning to the ARMA filtering technique, a method according to principles of the present disclosure includes acquiring ultrasound channel data as described above, generating a predictive error filter with an autoregressive moving average model, estimating, with the predictive error filter, noise in the ultrasound channel data, and subtracting the noise from the ultrasound channel data to obtain clean channel data, wherein the generating step, estimating step and subtracting step are performed in a time-space domain.

Estimating the predictive error filter with the autoregressive moving average model may include generating a multi-order model of the predictive error filter, converting the multi-order model and the ultrasound channel data to matrix form, and solving an eigenvalue problem for the predictive error filter. As described in more detail below, the eigenvalue problem may include a correlation matrix of a noisy sequence times the predictive error matrix equal to a variance of noise times the predictive error matrix, and a solution of the eigenvalue problem for the predictive error filter is an eigenvector corresponding to a minimum eigenvalue of the correlation matrix of the noisy sequence. After the PEF has been generated, estimating the noise in the ultrasound channel data may include deconvolving the PEF and the noisy sequence.

Expanding the brief explanation of an ARMA filtering technique in more mathematical detail, a linear (or a linear phased) array (e.g., transducer array <NUM> of <FIG>) may have a pitch g and a linear event with slope ψ in the per-channel data domain (or the T-X domain). The (x + <NUM>)th channel signal st(x + <NUM>) for this array can be described as:
<MAT>.

Wherein the maximum value of x is based on the number of channels.

Applying Fourier transforms to both sides of Equation (<NUM>), the following equation is obtained:
<MAT>.

Where f is the temporal frequency, e is the Euler's number. The time shift in the time-space (T-X) domain shown in Equation (<NUM>) becomes a phase shift in the frequency-space (F-X) domain as shown in Equation (<NUM>). For each temporal frequency f<NUM> over the bandwidth of the transducer, Equation (<NUM>) may be expressed as an autoregressive (AR) model:
<MAT>.

For an AR model of order <NUM> (e.g., p = <NUM>), the model becomes:.

Where p denotes the number of filter coefficients and determines the dominant spatial frequency components. A lower value of p (e.g., p=<NUM>) results in a more aggressive filter. A higher value of p (e.g., p = <NUM>) results in a less aggressive filter. A filter that is too aggressive may result in artifacts in the final image, whereas a less aggressive filter may allow in so much noise that image contrast is not improved. The inventors found that the value of p may be empirically selected based, at least in part, on applying the method multiple times with different values of p and selecting the value of p that provides the best image quality. For some medical ultrasound imaging applications, a value of p equal to or about <NUM> may provide images with improved contrast with minimal filter artifacts.

Returning to the ARMA filtering technique, Equation (<NUM>) in prediction error form is:
<MAT>.

Where gf<NUM>(<NUM>) = <NUM> and gf<NUM>(k) = -af<NUM>(k), k = <NUM>,.

If noise is added, Equation (<NUM>) becomes:
<MAT>.

Where W(x) is the random white noise sequence and Yf<NUM>(x) is the noisy sequence for the temporal frequency f<NUM>. Substituting Sf<NUM>(x - k + <NUM>) = Yf<NUM>(x - k + <NUM>) - W(x - k + <NUM>) in Equation (<NUM>), results in:
<MAT>.

Where E is error, and <MAT> is a non-white innovation noise sequence (i.e. colored noise). Equation (<NUM>) in matrix form with the subscript f<NUM> omitted is:
<MAT>.

Where Y is the convolution matrix of the noisy sequence Yf<NUM>(x), W is the convolution matrix of the noise sequence Wf<NUM>(x), g is the matrix form of the prediction error filter (PEF), and e is the error matrix.

If zero-mean white noise that is spatially uncorrelated with signal is assumed, the PEF, g, can be estimated by transforming Equation <NUM> into the following eigenvalue problem of the form Av = λv:
<MAT>
<MAT>.

Where H is the conjugate transpose operator, Ry is the correlation matrix of the noisy sequence (e.g., Ry = YHY), and σW<NUM> is the variance of the noise.

The desired PEF is the eigenvector corresponding to the minimum eigenvalue of RY. Therefore the PEF, g may be estimated. The minimum eigenvalue is an estimate of the noise variance σW<NUM>. An estimate Ŵ(x), the normalization of the colored noise sequence, can be obtained by deconvolving the PEF from the non-white noise innovation term:
<MAT>.

Where G is the convolution matrix of the PEF g, y is the noisy sequence, and w is the additive noise sequence.

Formulating a constrained minimization problem:
<MAT>.

Equation <NUM> is subject to the condition:
<MAT>.

The solution for ŵ, the normalization of the additive noise sequence, in the above minimization problem is:
<MAT>.

Where I is the identity matrix and µ is a diagonal loading factor that controls the weight given to the identity matrix. From Equation <NUM>, the estimated noise sequence ŵ can then be subtracted from the noisy sequence y to yield clean data ŝ:
<MAT>.

Note that the equation above yields ŵ = y, that is ŝ = <NUM>, when µ = <NUM> and ŵ = <NUM>. Furthermore, ŝ = y when µ » <NUM>. Therefore an appropriate value for µ should be selected. Similar to p in Equation <NUM>, µ may be selected empirically. For some medical ultrasound imaging applications a value equal to or about <NUM> may be used for µ. In the examples provided in the present disclosure, µ = <NUM> was used.

The above eigenvalue problem was solved for f<NUM>. The same problem is solved for each temporal frequency and/or temporal frequency bin over the bandwidth of the transducer array. Once the "clean" signal ŝ is obtained for each temporal frequency and/or frequency bin, the inverse Fourier transform may be applied to transform the clean signals from the F-X domain to the T-X domain. Prior to applying the inverse Fourier transform, ŝ for each temporal frequency may be fed back into Equation <NUM> for S, if desired, resulting in "cleaner" signal ŝ'. The process may be repeated more than once (e.g., <NUM>, <NUM>, <NUM> times).

The steps of the ARMA filtering technique described thus far are applied to data from an axial segment of predetermined size (e.g., a single depth of the ultrasound scan). The steps are repeated for all desired depths of the ultrasound scan.

<FIG> is a flowchart <NUM> of a method according to principles of the present disclosure, although not claimed as such. Flowchart <NUM> summarizes the steps of the ARMA filtering technique described mathematically above. The method illustrated in flowchart <NUM> is performed at a single depth (e.g., axial segment) of the ultrasound scan. The entire method of flowchart <NUM> (except Step <NUM>, as explained below) may be repeated for all depths and/or desired depths of the ultrasound scan. The ARMA filtering technique may be performed at least in part by a signal processor, for example, signal processor <NUM> in <FIG>. Ultrasound channel data is acquired at Step <NUM>. As discussed in reference to <FIG>, the channel data may be acquired by the signal processor after delays and/or geometric alignment have been applied to the channel data but prior to signal summation. At Step <NUM>, the channel data is converted from the time-space (T-X) domain to the frequency domain (F-X) (e.g., Fourier transform). A predictive error filter (PEF) is generated at Step <NUM>. The PEF may be generated by the method described above in reference to Equations <NUM>-<NUM>. Once the PEF has been generated, the noise in the channel data may be estimated at Step <NUM>. The noise may be estimated by the method described above in reference to Equations <NUM>-<NUM>. The noise may then be subtracted from the channel data at Step <NUM> to obtain the "clean" data.

Steps <NUM>-<NUM> are performed on a single temporal frequency and/or temporal frequency bin. Accordingly, as illustrated by box <NUM>, Steps <NUM>-<NUM> are repeated for each temporal frequency and/or temporal frequency bin. After Steps <NUM>-<NUM> have been performed for all temporal frequencies and/or temporal frequency bins, the "clean" data is converted from the F-X domain back to the T-X domain (e.g., inverse Fourier transform) at Step <NUM>.

Once method <NUM> has been completed for all depths, Steps <NUM>-<NUM> of method <NUM> may be repeated for all depths by substituting the "clean" channel data from Step <NUM> for the originally acquired channel data from Step <NUM> if desired. Method <NUM> is then repeated for each depth of the "clean" channel data. The resulting "cleaner" channel data may then be fed back into Step <NUM> for another iteration, and so on. The number of iterations may be set by a user (e.g., via a user interface such as user interface <NUM> in <FIG>) and/or determined by an ultrasound imaging system (e.g., ultrasound imaging system <NUM> in <FIG>). In some embodiments, the ultrasound imaging system may determine the number of iterations based on a desired level of contrast, imaging application, signal-to-noise ratio, and/or other factors (e.g., probe type, T-X or F-X ARMA filtering technique used, default setting).

The equations and <FIG> above describe transforming from the time-space (T-X) domain to the frequency-space (F-X) domain and back again. However, according to the invention, the ARMA filtering technique is directly applied in the aperture or the T-X domain without having to transform the channel data to F-X domain. Hence, Steps <NUM> and <NUM> of the method illustrated in flowchart <NUM> in <FIG> are omitted. This may increase the efficiency of the method and reduce the computational burden in some applications. The T-X domain method of the ARMA filtering technique could be applied to the aperture-domain analytic signal depth-by-depth. This may allow for a reduction of the computational burden as computation does not need to be performed at individual temporal frequencies. The T-X domain method of the ARMA filtering technique may lead to results that are similar to or slightly less accurate than the F-X domain method of the ARMA filtering technique because the current ARMA modelling framework is formulated based on a narrowband assumption, and the subdivision into each temporal frequency and/or frequency bin in the F-X domain method may yield a more accurate estimation of the PEF and the noise sequence. However, the gains in computation time of the T-X method may be preferential to the more accurate results of the F-X method in some applications. Similar to the F-X method, the T-X method may also be applied iteratively if desired to maximize image contrast enhancement.

As discussed previously in reference to <FIG>, the proposed ARMA filtering technique summarized in <FIG> may be implemented with plane wave imaging (PWI) or diverging wave imaging (DWI) applications. The ARMA filtering technique may be directly applied to beamsum signals obtained from different directions. For example, in PWI/DWI, the dataset may be stored in a 3D matrix of size [# of axial samples × # of scanlines × # of PW/DW transmits] as opposed to [# of axial samples × # of scanlines × # of channels] in the case of focused transmit imaging methods. Instead of applying the ARMA filtering technique at all depths in the channel domain for every scanline, the technique may be applied at all depths in the PW/DW transmit domain for every scanline in PWI/DWI. This may increase the ease of implementation on some existing ultrasound systems as access to per-channel data may be limited in some systems.

<FIG> show example images generated by conventional techniques and the ARMA filtering technique according to principles of the present disclosure. Images generated by the ARMA filtering technique may be provided on a display of an ultrasound imaging system (e.g., display <NUM> in <FIG>). Images may also be stored to a computer readable medium and/or provided to another display (e.g., a personal computer for post-exam review). The examples described below are illustrative and should not be interpreted to limit the implementations or applications of the ARMA filtering technique to the examples disclosed herein.

<FIG> shows images <NUM> of a simulated phantom containing a <NUM>-diameter anechoic cyst lesion. The images were simulated for a <NUM>-element P4-<NUM> phased array. All images are shown on a <NUM> dB dynamic range. Image <NUM> was generated using standard delay-and-sum (DAS) beamforming. Image <NUM> was generated using the frequency-space domain (F-X) ARMA filtering technique according to principles of the present disclosure. The F-X ARMA filtering technique was performed once. That is, the process was not repeated with the resulting clean data. Both images <NUM> and <NUM> were acquired in the absence of random noise. Comparing images <NUM> and <NUM>, F-X ARMA filtering suppresses off-axis clutter and yields enhanced image contrast. Image <NUM> was generated using standard DAS beamforming and image <NUM> was generated using F-X ARMA. In images <NUM> and <NUM>, random noise was present in the channel data. Comparing images <NUM> and <NUM>, F-X ARMA filtering suppresses both random noise and off-axis clutter and yields enhanced image contrast.

<FIG> shows post-processing channel RF data <NUM> taken from the center scan line (e.g., <NUM>° steering angle) from the images <NUM> shown in <FIG>. Image <NUM> is the channel data for image <NUM>; image <NUM> is the channel data for image <NUM>; image <NUM> is the channel data for image <NUM>; and image <NUM> is the channel data for image <NUM>. Every other channel signal is displayed for clearer visualization. Axial samples from sample number <NUM> to <NUM> correspond to the anechoic cyst lesion shown in images <NUM>. In the absence of random noise, image <NUM> generated using DAS displays small-amplitude off-axis clutter signals. In contrast, image <NUM> illustrates that the F-X ARMA filtering suppresses much of these off-axis clutter signals while preserving most of the signals in the speckle region (e.g., outside of <NUM>~<NUM> sample region). In the presence of random noise, comparing DAS image <NUM> and F-X ARMA image <NUM>, the F-X ARMA filtering provides better suppression of both the random noise and the acoustic clutter contributions from the channel RF data.

<FIG> shows images <NUM> of an apical <NUM>-chamber view of a heart. All images are shown on a <NUM> dB dynamic range. Images <NUM> and <NUM> were acquired from an "easy" patient. Images <NUM> and <NUM> were acquired from a "difficult" patient. Easy patients have less noisy images than difficult patients. For example, in general, it is often difficult to obtain clear images from overweight patients. That is, an easy patient may be a normal weight patient and a difficult patient may be an overweight patient in some cases. Images <NUM> and <NUM> were generated using DAS and images <NUM> and <NUM> were generated using the F-X ARMA filtering technique according to principles of the present disclosure. The F-X ARMA filtering technique was performed once. That is, the process was not repeated with the resulting clean data. In both patients, image contrast is significantly reduced in the DAS images <NUM> and <NUM> due to the presence of off-axis clutter and reverberation clutter. In comparison, the FX-ARMA images <NUM> and <NUM> show a significant amount of image contrast enhancement.

As shown in <FIG>, filtering the ultrasound channel data to acquire clean data using the ARMA filtering technique may provide enhanced image contrast compared to typical image processing techniques such as DAS.

<FIG> shows images <NUM> of a simulated phantom containing a <NUM>-diameter anechoic cyst lesion. The images were simulated for a <NUM>-element P4-<NUM> phased array. All images are shown on a <NUM> dB dynamic range. Image <NUM> was generated using standard delay-and-sum (DAS) beamforming. Image <NUM> was generated using the time-space domain (T-X) ARMA filtering technique according to principles of the invention. The T-X ARMA filtering technique was performed once. That is, the process was not repeated with the resulting clean data. Both images <NUM> and <NUM> were acquired in the absence of random noise. Comparing images <NUM> and <NUM>, T-X ARMA filtering suppresses off-axis clutter and yields enhanced image contrast. Image <NUM> was generated using standard DAS beamforming and image <NUM> was generated using T-X ARMA. In images <NUM> and <NUM>, random noise was present in the channel data. Comparing images <NUM> and <NUM>, T-X ARMA filtering suppresses both random noise and off-axis clutter and yields enhanced image contrast. Although the T-X ARMA images <NUM> and <NUM> have slightly less contrast than F-X ARMA images <NUM> and <NUM> shown in <FIG>, the T-X ARMA images still show improved contrast compared to DAS images. Thus, given the faster computation time of T-X ARMA images compared to F-X ARMA images, the T-X ARMA filtering technique may be acceptable and/or preferred in some applications.

<FIG> shows images <NUM> of an apical <NUM>-chamber view of a heart. All images are shown on a <NUM> dB dynamic range. Images <NUM>, <NUM>, and <NUM> were acquired from an "easy" patient. Images <NUM>, <NUM>, and <NUM> were acquired from a "difficult" patient. Images <NUM> and <NUM> were generated using DAS beamforming. Images <NUM> and <NUM> were generated by applying one iteration of the T-X ARMA filtering technique. Images <NUM> and <NUM> were generated by applying five iterations of the T-X ARMA filtering technique. That is, the "clean" data from the T-X ARMA filtering technique on the first iteration was fed back into the technique four times. In the images acquired for both easy and difficult patients, image contrast is significantly reduced in the DAS images <NUM> and <NUM> due to the presence of off-axis clutter and reverberation clutter. However, a significant amount of image contrast enhancement is observed with T-X ARMA filtering for both single and multiple iterations. As shown in images <NUM> and <NUM>, performing multiple iterations of T-X ARMA filtering technique may help further suppress clutter signal that remain after a single iteration of T-X ARMA filtering technique.

As shown in <FIG> and <FIG>, filtering the ultrasound channel data to acquire clean data using the ARMA filtering technique may provide enhanced image contrast compared to typical image processing techniques such as DAS, even when using the T-X ARMA method.

According to principles of the disclosure, an ARMA filtering technique (e.g., ARMA method) as described herein may be applied to ultrasound channel data to suppress random noise, acoustic clutter, and/or reverberation clutter which may enhance image contrast. This may improve a clinician's ability to locate, recognize, and/or measure anatomical features in the image. The improved contrast may improve a clinician's ability to make diagnoses based on the ultrasound image.

In various embodiments where components, systems and/or methods are implemented using a programmable device, such as a computer-based system or programmable logic, it should be appreciated that the above-described systems and methods can be implemented using any of various known or later developed programming languages, such as "C", "C++", "FORTRAN", "Pascal", "VHDL" and the like. Accordingly, various storage media, such as magnetic computer disks, optical disks, electronic memories and the like, can be prepared that can contain information that can direct a device, such as a computer, to implement the above-described systems and/or methods. Once an appropriate device has access to the information and programs contained on the storage media, the storage media can provide the information and programs to the device, thus enabling the device to perform functions of the systems and/or methods described herein. For example, if a computer disk containing appropriate materials, such as a source file, an object file, an executable file or the like, were provided to a computer, the computer could receive the information, appropriately configure itself and perform the functions of the various systems and methods outlined in the diagrams and flowcharts above to implement the various functions. That is, the computer could receive various portions of information from the disk relating to different elements of the above-described systems and/or methods, implement the individual systems and/or methods and coordinate the functions of the individual systems and/or methods described above.

In view of this disclosure it is noted that the various methods and devices described herein can be implemented in hardware, software and firmware. Further, the various methods and parameters are included by way of example only and not in any limiting sense. In view of this disclosure, those of ordinary skill in the art can implement the present teachings in determining their own techniques and needed equipment to affect these techniques, while remaining within the scope of the invention. The functionality of one or more of the processors described herein may be incorporated into a fewer number or a single processing unit (e.g., a CPU) and may be implemented using application specific integrated circuits (ASICs) or general purpose processing circuits which are programmed responsive to executable instruction to perform the functions described herein.

Although the present system has been described with reference to an ultrasound imaging system, the present system may be extended to other imaging techniques. Additionally, the present system may be used to obtain and/or record image information related to, but not limited to renal, testicular, prostate, breast, ovarian, uterine, thyroid, hepatic, lung, musculoskeletal, splenic, nervous, cardiac, arterial and vascular systems, as well as other imaging applications related to ultrasound-guided interventions and other interventions which may be guided by real-time medical imaging. Further, the present system may also include one or more elements which may be used with non-ultrasound imaging systems with or without real-time imaging components so that they may provide features and advantages of the present system.

Further, the present methods, systems, and apparatuses may be applied to existing imaging systems such as, for example, ultrasonic imaging systems. Suitable ultrasonic imaging systems may include a Philips® ultrasound system which may, for example, support a conventional broadband linear array transducer that may be suitable for small-parts imaging.

Certain additional advantages and features of this invention may be apparent to those skilled in the art upon studying the disclosure, or may be experienced by persons employing the novel system and method of the present invention, chief of which is reduction of acoustic clutter and random noise by ultrasound imaging systems and method of operation thereof is provided. Another advantage of the present systems and method is that conventional medical imaging systems may be easily upgraded to incorporate the features and advantages of the present systems, devices, and methods.

Of course, it is to be appreciated that any one of the above embodiments or processes may be combined with one or more other embodiments and/or processes or be separated and/or performed amongst separate devices or device portions in accordance with the present systems, devices and methods.

Claim 1:
A method (<NUM>) comprising:
acquiring (<NUM>) ultrasound channel data;
generating (<NUM>) a predictive error filter with an autoregressive moving average model;
estimating (<NUM>), with the predictive error filter, noise in the ultrasound channel data; and
subtracting (<NUM>) the noise from the ultrasound channel data to obtain clean channel data,
wherein the generating step, estimating step and subtracting step are performed in a time-space domain.