Patent Description:
Ultrasound imaging is increasingly being employed in a variety of different applications. It is important that the image produced by the ultrasound system is as clear and accurate as possible so as to give the user a realistic interpretation of the subject being scanned. This is especially the case when the subject in question is a patient undergoing a medical ultrasound scan. In this situation, the ability of a doctor to make an accurate diagnosis is dependent on the quality of the image produced by the ultrasound system.

Off-axis clutter is a significant cause of image degradation in ultrasound. Adaptive beamforming techniques, such as minimum variance (MV) beamforming, have been developed and applied to ultrasound imaging to achieve an improvement in image quality; however, MV beamforming is computationally intensive as an inversion of the spatial covariance matrix is required for each pixel of the image. In addition, even though MV beamforming is developed primarily for an improvement in spatial resolution, and is not ideal for reducing off-axis clutter, its performance in terms of improving spatial resolution often needs to be sacrificed by reducing the subarray size. Otherwise, image artifacts may occur in the speckle due to signal cancellation.

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), have been proposed but all require access to per-channel data to compute a weighting mask to be applied to the image. Further, these methods would only work for conventional imaging with focused transmit beams and are not suitable for plane wave imaging (PWI) or diverging wave imaging (DWI) involving only a few transmits.

In addition, spatial resolution in ultrasound images, particularly the lateral resolution, is often suboptimal. The -6dB lateral beamwidth at the focal depth is determined by the following equation: <MAT> where λ is wavelength, z is transmit focal depth, and D is the aperture size. The smaller the wavelength (or the higher center frequency), the better the lateral resolution will be; however, a smaller wavelength is achieved at the cost of penetration depth. On the other hand, a larger aperture size D is needed to achieve a better lateral resolution; however, the aperture size is often limited by the human anatomy, hardware considerations, and system cost.

Adaptive beamforming techniques, such as the previously mentioned minimum variance (MV) beamforming, have been a topic of active research. These methods are data-dependent beamforming methods that seek to adaptively estimate the apodization function that yields lateral resolution beyond the diffraction limit.

In addition to standard ultrasound imaging techniques, plane wave imaging (PWI) or diverging wave imaging (DWI) in the case of phased arrays is a relatively new imaging technique, which has the potential to perform imaging at very high frame rates above <NUM> and possibly several kHz. These techniques have also opened up many new possible imaging modes for different applications which were previously not possible with conventional focused transmit beams. For this reason, they have been some of the most actively researched topics in academia in recent years.

PWI/DWI can achieve high frame rates by coherently compounding images obtained from broad transmit beams at different angles. Since the spatial resolution in PWI/DWI is generally known to be only slightly worse than, or comparable to, conventional imaging with focused transmit beams, its main drawback is degradation in the image contrast, which is directly related to the number of transmit angles. The image contrast is generally low for a small number of transmit angles in PWI/DWI; therefore, many transmit angles are needed to maintain image quality comparable to that of conventional imaging with focused transmit beams.

PWI/DWI also suffers from motion artifacts, particularly when imaging fastmoving organs such as the heart, as individual image pixels are constructed from signals from different transmit beams. The effect of motion becomes more severe with increasing numbers of transmit angles; therefore, the dilemma in PWI/DWI systems is clear: more transmit angles are required to achieve high image contrast, but also result in more motion artifacts that degrade image quality.

Further, regardless of the number of transmit angles, PWI/DWI does not reduce reverberation clutter, which is one of the main sources of image quality degradation in fundamental B-mode ultrasound images.

The present invention provides systems and methods capable of performing off-axis clutter filtering, improving spatial resolution, and improving image contrast. In certain aspects, the present inventions proposes an extrapolation technique of the transmitted plane waves based on a linear prediction scheme, which allows for an extremely high frame rate while significantly improving image contrast. An additional benefit of the invention is that these benefits may be accomplished without significantly increasing the computational burden of the ultrasound system.

There is provided a method for performing off-axis clutter filtering in an ultrasound image, the method comprising:.

This method performs off-axis clutter filtering in an ultrasound image. By performing a spectral estimation of the channel data it is possible to identify the frequency content of the channel data. Typically, off-axis clutter signal will possess a high spatial frequency, which may be identified in the spectral estimation of the channel data. In this way, it is possible to selectively attenuate signals having high spatial frequencies, thereby reducing and/or eliminating off-axis clutter signals in the final ultrasound image.

In an embodiment, the isolating of the channel data comprises processing a plurality of observations of the imaged point.

In this way, it is possible to perform the spectral estimation on channel data that has been averaged over a plurality of measurements. In this way, the accuracy of the channel data, and so of the final ultrasound image, is increased.

In an arrangement, the spectral estimation comprises decomposing the channel data into a finite sum of complex exponentials.

By decomposing the channel data into a finite sum of complex exponentials, it is simple to identify the components with high spatial frequencies, thereby making it easier to attenuate the off-axis clutter signals in the ultrasound image.

In a further arrangement, the complex exponentials comprise:.

In a further embodiment, the first model parameter is complex.

In a yet further embodiment, the second model parameter is inversely proportional to the distance between adjacent transducer elements of the ultrasonic probe.

The first and second model parameters may be used to describe the nature of the channel data. In the case that the first model parameter is complex, the imaginary component relates to the phase of the signals and the modulus, which may be a real, positive number, relates to the amplitude of the signal. The second model parameter may relate to the spatial frequency of the signal.

In a still yet further arrangement, the first and second model parameters are estimated by way of spectral estimation.

In some designs, the attenuation coefficient is Gaussian.

In this way, it is simple to implement attenuation for signals approaching higher spatial frequencies. The aggressiveness of the filtering may be tuned by altering the width of the Gaussian used.

In an embodiment, the attenuation coefficient is depth dependent.

By making the attenuation coefficient depth dependent, it is possible to control the amount of off-axis clutter signal filtering for different depths.

In an arrangement, the attenuation coefficient is dependent on the second model parameter.

In this way, the attenuation coefficient may be directly dependent on the spatial frequency of the signal, meaning that the attenuation coefficient may adapt to the signals on an individual basis rather than requiring an input from the user.

In some embodiments, the attenuation coefficient is adapted to attenuate the channel data to half the width of the receive beampattem.

In this way, it is possible to both improve the lateral resolution and decrease the off-axis clutter in the filtered ultrasound image.

In some arrangements, the spectral estimation is based on an autoregressive model.

There is provided a computer program comprising computer program code means which is adapted, when said computer program is run on a computer, to implement the method described above.

There is provided a controller for controlling the filtering of off-axis clutter in an ultrasound image, wherein the controller is adapted to:.

There is provided an ultrasound system, the system comprising:.

There is provided a method for generating an ultrasound image, the method comprising:.

This method performs aperture extrapolation on the channel data, thereby increasing the lateral resolution of the ultrasound image. By estimating the extrapolation filter based on the segmented channel data, the extrapolation filter may directly correspond to the channel data. In this way, the accuracy of the extrapolation performed on the segmented channel data is increased. In other words, this method predicts channel data from transducer elements that do not physically exist within the ultrasonic probe by extrapolating the existing channel data.

By performing the extrapolation of the segmented channel data in the temporal frequency domain, the accuracy of the extrapolated channel data may be further increased.

In an arrangement, the axial segment is less than <NUM> wavelengths in depth, for example less than or equal to <NUM> wavelengths in depth.

In this way, the performance of the system, which is largely dependent on the number of segments of channel data to be extrapolated, may be improved whilst maintaining the improvement in lateral resolution of the image, which is inversely proportional to the size of the axial segment.

In an embodiment, the extrapolation factor is less than or equal to 10x, for example less than or equal to 8x.

In this way, it is possible to achieve a substantial improvement in the lateral resolution of the ultrasound image whilst preserving the speckle texture within the image.

In some designs, the estimation of the extrapolation filter is performed using an autoregressive model.

In an arrangement, the estimation of the extrapolation filter is performed using the Burg technique.

In this way, the extrapolation filter may be simply estimated without requiring a significant amount of processing power.

In some embodiments, the filter order is less than or equal to <NUM>, for example less than or equal to <NUM>.

In an embodiment, the extrapolation occurs in the azimuthal direction in the aperture domain.

According to examples in accordance with a further aspect of the invention, there is provided a computer program comprising computer program code means which is adapted, when said computer program is run on a computer, to implement the method defined above.

There is provided a controller for controlling the generation of an ultrasound image, wherein the controller is adapted to:.

In an embodiment, the system further comprises a user interface having a user input.

In this way, it is possible for a user to provide an instruction to the ultrasound system.

In an arrangement, the user input is adapted to adjust the axial depth of the axial segment.

In a further arrangement, the user input is adapted to alter the extrapolation factor.

In a yet further arrangement, the user input is adapted to alter the filter order.

In this way, the user may empirically adapt the various parameters of the extrapolation method in order to maximize the image quality according to their subjective opinion.

According to the invention, there is provided a method for performing transmit extrapolation to generate an ultrasound image, the method comprising:.

This method performs transmit extrapolation on the beam-summed data, thereby improving the contrast of the ultrasound image. The beam-sum data corresponds to data summed across the aperture for several transmit beams overlapping on a point of interest. In addition, this method provides an increase in ultrasound image frame rate and a reduction in motion artifacts in the final ultrasound image by retaining contrast and resolution with fewer actual transmit events. By estimating the extrapolation filter based on the segmented beam-summed data, the extrapolation filter may directly correspond to the beam-summed data. In this way, the accuracy of the extrapolation performed on the segmented beam-summed data is increased. In other words, this method predicts beam-summed data from transmit angles outside of the range of angles used to obtain the original beam-summed data by extrapolation.

By performing the extrapolation of the segmented beam-summed data in the temporal frequency domain, the accuracy of the extrapolated beam-summed data may be further increased.

In an arrangement, the beam-summed data is obtained by way of at least one of plane wave imaging and diverging wave imaging.

In this way, it is possible to produce an ultrafast ultrasound imaging method with increased image contrast and frame rate.

In some embodiments, the axial segment is less than <NUM> wavelengths in depth, for example less than or equal to <NUM> wavelengths in depth.

In this way, the performance of the system, which is largely dependent on the number of segments of beam-summed data to be extrapolated, may be improved whilst maintaining the improvement in image quality, which is inversely proportional to the size of the axial segment.

In some arrangements, the plurality of steering angles comprises fewer than <NUM> angles, for examples less than or equal to <NUM> angles.

In this way, the computational performance of the ultrasound system may be improved, as there are fewer steering angle to process, whilst maintaining the detail of the final ultrasound image, which is proportional to the number of steering angle used.

In some designs, the filter order is less than or equal to half the number of steering angles.

In an embodiment, the extrapolation factor is less than or equal to <NUM>, for example less than or equal to <NUM>.

In this way, it is possible to achieve a substantial improvement in the contrast resolution of the ultrasound image whilst preserving the speckle texture within the image.

In an arrangement, the estimation of the extrapolation filter is performed using an autoregressive model.

In an embodiment, the estimation of the extrapolation filter is performed using the Burg technique.

According to the invention, there is provided a controller for controlling transmit extrapolation to generate an ultrasound image, wherein the controller is adapted to:.

According to the invention, there is provided an ultrasound system, the system comprising:.

In a further embodiment, the user input is adapted to adjust at least one of: the axial depth of the axial segment; the extrapolation factor; and the filter order.

The invention provides methods and systems for generating an ultrasound image. In a method, the generation of an ultrasound image comprises: obtaining channel data, the channel data defining a set of imaged points; for each imaged point: isolating the channel data; performing a spectral estimation on the isolated channel data; and selectively attenuating the spectral estimation channel data, thereby generating filtered channel data; and summing the filtered channel data, thereby forming a filtered ultrasound image.

In some examples, the method comprises aperture extrapolation. The aperture extrapolation improves the lateral resolution of the ultrasound image. In other examples, the method comprises transmit extrapolation. The transmit extrapolation improves the contrast of the image. In addition, the transmit extrapolation improves the frame rate and reduces the motion artifacts in the ultrasound image. In further examples, the aperture and transmit extrapolations may be combined.

The general operation of an exemplary ultrasound diagnostic imaging system will first be described, with reference to <FIG>, and with emphasis on the signal processing function of the system since this invention relates to the processing of the signals measured by the transducer array.

The system comprises an array transducer probe <NUM> which has a CMUT transducer array <NUM> for transmitting ultrasound waves and receiving echo information. The transducer array <NUM> may alternatively comprise piezoelectric transducers formed of materials such as PZT or PVDF. The transducer array <NUM> is a two-dimensional array of transducers <NUM> capable of scanning in a 2D plane or in three dimensions for 3D imaging. In another example, the transducer array may be a 1D array.

The transducer array <NUM> is coupled to a microbeamformer <NUM> in the probe which controls reception of signals by the CMUT array cells or piezoelectric elements. Microbeamformers are capable of at least partial beamforming of the signals received by subarrays (or "groups" or "patches") of transducers as described in <CIT>), <CIT>), and <CIT>.

Note that the microbeamformer is entirely optional. The examples below assume no analog beamforming.

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 when a microbeamformer is not used and the transducer array is operated directly by the main system beamformer. The transmission of ultrasound beams from the transducer array <NUM> is directed by a transducer controller <NUM> coupled to the microbeamformer by the T/R switch <NUM> and a main transmission beamformer (not shown), which receives input from the user's operation of the user interface or control panel <NUM>.

The transducer controller <NUM> can be coupled to control a DC bias control <NUM> for the CMUT array. The DC bias control <NUM> sets DC bias voltage(s) that are applied to the CMUT cells.

In the reception channel, partially beamformed signals are produced by the microbeamformer <NUM> and are coupled to a main receive beamformer <NUM> where the partially beamformed signals from individual patches of transducers are combined into a fully beamformed signal. For example, the main beamformer <NUM> may have <NUM> channels, each of which receives a partially beamformed signal from a patch of dozens or hundreds of CMUT transducer cells or piezoelectric elements. In this way the signals received by thousands of transducers of a transducer array can contribute efficiently to a single beamformed signal.

The signal processor <NUM> can process the received echo signals in various ways, such as band-pass filtering, decimation, I and Q component separation, and harmonic signal separation which acts to separate linear and nonlinear signals so as to enable the identification of nonlinear (higher harmonics of the fundamental frequency) echo signals returned from tissue and micro-bubbles. The band-pass filter in the signal processor can be a tracking filter, with its pass band sliding from a higher frequency band to a lower frequency band as echo signals are received from increasing depths, thereby rejecting the noise at higher frequencies from greater depths where these frequencies are devoid of anatomical information.

The transmission and reception channels use the same transducer array <NUM>' which has a fixed frequency band. However, the bandwidth that the transmission pulses occupy can vary depending on the transmission beamforming that has been used. The reception channel can capture the whole transducer bandwidth (which is the classic approach) or by using bandpass processing it can extract only the bandwidth that contains the useful information (e.g. the harmonics of the main harmonic).

The processed signals are coupled to a B mode (i.e. brightness mode, or 2D imaging mode) processor <NUM> and a Doppler processor <NUM>. The B mode processor <NUM> employs detection of an amplitude of the received ultrasound signal for the imaging of structures in the body such as the tissue of organs and vessels in the body. B mode images of structure of the body may be formed in either the harmonic image mode or the fundamental image mode or a combination of both as described in <CIT>. ) and <CIT>. ) The Doppler processor <NUM> processes temporally distinct signals from tissue movement and blood flow for the detection of the motion of substances such as the flow of blood cells in the image field. The Doppler processor <NUM> typically includes a wall filter with parameters which may be set to pass and/or reject echoes returned from selected types of materials in the body.

The structural and motion signals produced by the B mode and Doppler processors are coupled to a scan converter <NUM> and a multi-planar 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 may arrange the echo signal into a two dimensional (2D) sector-shaped format, or a pyramidal three dimensional (3D) image. The scan converter can overlay a B mode structural image with colors corresponding to motion at points in the image field with their Doppler-estimated velocities to produce a color Doppler image which depicts the motion of tissue and blood flow in the image field. The multi-planar reformatter will convert echoes which are received from points in a common plane in a volumetric region of the body into an ultrasound 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 as described in <CIT>.

The 2D or 3D images are coupled from the scan converter <NUM>, multi-planar 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>. In addition to being used for imaging, the blood flow values produced by the Doppler processor <NUM> and tissue structure information produced by the B mode processor <NUM> are coupled to a quantification processor <NUM>. The quantification processor produces measures of different flow conditions such as the volume rate of blood flow as well as structural measurements such as the sizes of organs and gestational age. The quantification processor may receive input from the user control panel <NUM>, such as the point in the anatomy of an image where a measurement is to be made. Output data from the quantification processor is coupled to a graphics processor <NUM> for the reproduction of measurement graphics and values with the image on the display <NUM>, and for audio output from the display device <NUM>. The graphics processor <NUM> can also generate graphic overlays for display with the ultrasound images. These graphic overlays can contain 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 patient name. The user interface is also coupled to the transmit controller <NUM> to control the generation of ultrasound signals from the transducer array <NUM>' and hence the images produced by the transducer array and the ultrasound system. The transmit control function of the controller <NUM> is only one of the functions performed. The controller <NUM> also takes account of the mode of operation (given by the user) and the corresponding required transmitter configuration and band-pass configuration in the receiver analog to digital converter. The controller <NUM> can be a state machine with fixed states.

<FIG> shows a method <NUM> of performing selective attenuation on an ultrasound image.

In step <NUM>, channel data is obtained from an ultrasonic probe. The channel data defines a set of imaged points within a region of interest.

In step <NUM>, the channel data is isolated for a given imaged point, meaning that it may be operated on independently of the remaining channel data.

In step <NUM>, a spectral estimation is performed on the isolated channel data. For example, the channel data may be decomposed into a finite sum of complex exponentials as follows: <MAT> where: x is the lateral coordinate along the array of transducer elements of the ultrasonic probe; S(x) is the measured channel data signal at x; N is the model order, which is the number of sinusoidal components used to describe the channel data; ai is the first model parameter; and ki is the second model parameter. Any spectral estimation method may be performed on the isolated channel data. For example, a Fourier transform may be performed in combination with a Total Variation method. In another example, a complex L1/L2 minimization may be used to decompose the channel data signal as a sparse sum of off-axis and off-range components. In the example above, an autoregressive model is used.

In this case, the ai are complex parameters, wherein the phase may be between -π and π and wherein the modulus is a real, positive number, indicative of the strength of the channel data signal.

The ki may also theoretically be complex; however, in this example they are real numbers. They may theoretically range from -∞ to ∞ but in practice, due to sampling restrictions; they range from <MAT> to <MAT>, where dx is the element spacing in the transducer array of the ultrasonic probe.

In this example, the first and second model parameters may be estimated through any known method in the art spectral estimation techniques. For example, the parameters may be estimated by way of a non-parametric method, such as a fast Fourier transform (FFT) or discrete Fourier transform (DFT), or a parametric method, such as the autoregressive (AR) or autoregressive moving average (ARMA) methods.

In step <NUM>, the channel data is selectively attenuated by including an attenuation coefficient in the above formulation: <MAT>.

Alternative functions may also be used as an attenuation coefficient. For example, it may be possible to estimate the angular transmit, or round-trip, beampattern of the channel data, such as through simulation, to use as a weighting mask. Further, a rectangular function may be used, wherein the width of the function dictates a cutoff frequency above which all signals are rejected. Further still, an exponential decay function may also be used.

Steps <NUM> to <NUM> are repeated for each axial segment of the channel data. When the final segment of channel data has undergone selective attenuation, the method may progress to step <NUM>.

In step <NUM>, the filtered channel data is summed to form the final clutter filtered ultrasound image.

<FIG> shows a comparison between an ultrasound image of a heart at various stages in the method describe above with reference to <FIG>.

The first image <NUM> shows the original ultrasound image captured from the raw channel data. As can be seen, the image contains a high level of noise and the details are difficult to make out.

The second image <NUM> shows the ultrasound image at stage <NUM> of the method, where the image has been reconstructed with the sparse sinusoidal decomposition as described above. In this example, the order of the model, N = <NUM>, meaning that the channel data at each depth is modeled as a sum <NUM> of sinusoids. The improvement in image clarity can already be seen; however, the signal is still noisy and the finer details, particularly towards the top of the image, remain largely unclear.

The third image <NUM> shows the ultrasound image at stage <NUM> of the method, after the application of the selective attenuation, thereby eliminating the sinusoidal signals with the highest spatial frequencies. As can be seen from the image, the signal noise has been significantly reduced by the attenuation of the high spatial frequency signals.

<FIG> shows a method <NUM> for applying aperture extrapolation to an ultrasound image.

In step <NUM>, channel data is obtained by way of an ultrasonic probe.

In step <NUM>, the channel is segmented based on an axial imaging depth of the axial data.

The axial window size of the segmented channel data may be empirically determined to suit the visual preferences of the user. For example, an axial window size of in the range of <NUM> - <NUM> wavelengths may produce a preferred image quality improvement. A larger axial window may provide a more reliable improvement in image quality and better preservation of speckle texture; however, it may adversely affect the axial resolution of the ultrasound image.

In step <NUM>, an extrapolation filter of order p, aj where <NUM> ≤ j ≤ p, is estimated based on the segmented channel data. In this case, the extrapolation filter may be estimated by way of the well-known Burg technique for autoregressive (AR) parameter estimation.

In step <NUM>, the segmented channel data is extrapolated using the extrapolation filter estimated in step <NUM>. In this case, a <NUM>-step linear prediction extrapolator to obtain the <NUM>st forward-extrapolated sample XN+<NUM>, using the extrapolation filter and the previous p samples, as shown below: <MAT> where: XN is the current sample; XN+<NUM> is the forward-extrapolated sample; and p is the order of the extrapolation filter, aj.

The forward extrapolation may be generalized as follows: <MAT> where k is the number of forward extrapolations performed.

By reversing the filter order and taking the complex conjugate of the filter coefficients, it is possible to backward-extrapolate the value up to the kth channel as a linear combination of the first p channels: <MAT>.

Using both the forward and backward extrapolation formulae, it is possible to fully extrapolate the segmented channel data. Steps <NUM> and <NUM> are repeated for each axial segment of the segmented channel data.

In step <NUM>, the fully extrapolated channel data segments are summed to obtain the beamsum signal and generate the final ultrasound image.

<FIG> shows an illustration <NUM> of an embodiment of the method of <FIG>.

In step <NUM>, channel data is obtained by way of an ultrasonic probe. The plot shows signal intensity, by way of the shading, for each channel at a given axial depth. In the plots of steps <NUM>, <NUM>, <NUM> and <NUM>, the horizontal axis represents the channel being measured and the vertical axis represents the axial depth of the measured channel data, wherein the axial depth is inversely proportional to the height of the vertical axis. In the plots of steps <NUM> and <NUM>, the horizontal axis represents the channel being measured and the vertical axis represents the temporal frequency of the channel data.

In step <NUM>, a fast Fourier transform (FFT) is applied to the channel data, thereby transforming the channel data into the temporal frequency domain. In this case, the extrapolation filter is also estimated in the temporal frequency domain. The estimation is once again performed by the Burg technique.

In step <NUM>, the estimated extrapolation filter is used to extrapolate the temporal frequency domain channel data beyond the available aperture. The extrapolated data is highlighted by the boxes to the right, representing the forward extrapolated channel data, and the left, representing the backward extrapolated channel data, of the plot.

In step <NUM>, an inverse Fourier transform is applied to the extrapolated temporal frequency channel data, thereby generating spatial channel data. As can be seen from the plot, the channel data now covers a wider aperture than in step <NUM>.

In step <NUM>, the axial window is moved to a new axial segment and steps <NUM> to <NUM> are repeated.

In step <NUM>, the fully extrapolated channel data is obtained. This may then be used to generate the final ultrasound image.

<FIG> show examples of the implementation of embodiments of the method of <FIG>.

<FIG> shows a comparison between an ultrasound image before and after the implementation of two embodiments of the method of <FIG>. In <FIG>, the horizontal axes of the images represent the lateral position, measured in mm, of the signals and the vertical axes represent the axial position, measured in mm, of the signals. The gradient scales indicate the signal intensity at a given signal location.

The first image <NUM> shows a conventional delay and sum (DAS) beamformed image of a simulated phantom. In this case, a <NUM>-element aperture was used in both transmit and receive steps. As can be seem from the image, the two simulated cysts introduce heavy scattering and cause a large amount of noise in the ultrasound image. This results in the cysts appearing poorly defined and unclear.

The second image <NUM> shows the same ultrasound image after the application of the aperture extrapolation technique described above. In this case, the receive aperture was extrapolated by a factor of <NUM> using an extrapolation filter of order <NUM>. As can be seen from the image, the extrapolation has substantially improved the lateral resolution of the image whilst maintaining the quality of the speckle texture.

The third image <NUM> once again shows the same ultrasound image after the application of the aperture extrapolation technique described above; however, in this case, the receive aperture was extrapolated by a factor of <NUM> using an extrapolation filter of order <NUM>. As can be seen from the image, the extrapolation by this additional factor has further increased the lateral resolution of the ultrasound image.

<FIG> and <FIG> show a comparison between an ultrasound image before and after the application of an aperture extrapolation method as described above. The images are shown in the 60dB dynamic range.

In both cases, the top images, <NUM> and <NUM>, show a conventional DAS beamformed cardiac image. The bottom images, <NUM> and <NUM>, show the ultrasound images after an aperture extrapolation by a factor of <NUM>. In both Figures, it can be seen that the aperture extrapolation leads to an improvement in both lateral resolution and image contrast.

<FIG> shows a comparison between an ultrasound image of a leg before and after the application of an aperture extrapolation method as described above. The images are shown in the 60dB dynamic range.

The first image <NUM> shows a conventional DAS beamformed ultrasound image. The second image <NUM> shows the same ultrasound image after the application of an aperture extrapolation method as described above. The aperture was extrapolated by a factor of <NUM>. As can be seen from the second image, the aperture extrapolation leads to an improvement in the lateral resolution of the ultrasound image.

<FIG> show an improvement in lateral resolution due to the aperture extrapolation method across a wide variety of imaging scenarios.

<FIG> shows a method <NUM> for applying transmit extrapolation to an ultrasound image according to the invention.

In step <NUM>, beam-summed data is obtained using an ultrasonic probe, wherein the beam-summed data is obtained at a plurality of steering angles.

In step <NUM>, the beam-summed data is segmented for each steering angle based on the axial depth of the beam-summed data.

In step <NUM>, an extrapolation filter of order p, aj where <NUM> ≤ j ≤ p, is estimated based on the segmented beam-summed data. In this case, the extrapolation filter may be estimated by way of the well-known Burg technique for autoregressive (AR) parameter estimation.

In step <NUM>, the segmented beam-summed data is extrapolated using the extrapolation filter estimated in step <NUM>. In this case, a <NUM>-step linear prediction extrapolator to obtain the <NUM>st forward-extrapolated sample XN+<NUM>, using the extrapolation filter and the previous p samples of the available transmit angles, as shown below: <MAT> where: XN is the current sample; XN+<NUM> is the forward-extrapolated sample; and p is the order of the extrapolation filter, aj.

By reversing the filter order and taking the complex conjugate of the filter coefficients, it is possible to backward-extrapolate the value up to the kth transmit angle as a linear combination of the first p transmit angles: <MAT>.

Using both the forward and backward extrapolation formulae, it is possible to fully extrapolate the segmented beam-summed data. Steps <NUM> and <NUM> are repeated for each axial segment of the segmented beam-summed data.

In step <NUM>, the fully extrapolated beam-summed data segments are coherently compounded to obtain the final beamsum signal and generate the final ultrasound image.

The transmit scheme used in the above method may be: planar; diverging; single-element; or focused.

<FIG> shows an illustration of the method of <FIG>. For each of a plurality of steering angles <NUM>, a low contrast ultrasound image <NUM> is obtained. By coherently compounding <NUM> the plurality of low contrast ultrasound images, it is possible to generate a single high contrast ultrasound image <NUM>. The improvement in image contrast is proportional to the number of steering angles used, and so the number of low contrast ultrasound images coherently compounded to generate the high contrast ultrasound image; however, a large number of initial steering angles may result in a decreased computational performance of the ultrasound system. By extrapolating from a small number of initial steering angles, it is possible to increase the image contrast without significantly degrading the performance of the ultrasound system.

<FIG> shows a comparison between an ultrasound image before and after the implementation of the transmit extrapolation and a reference image.

The first image <NUM> shows a reference image of a simulated phantom containing a <NUM> diameter anechoic cyst lesion. The phantom contains <NUM> strong point scatterers in the speckle background and <NUM> weak point scatterers inside of the anechoic lesion. The image was formed using <NUM> diverging waves.

The second image <NUM> shows an image of the same simulated phantom formed from only the <NUM> central angles of the <NUM> steering angles of the reference image. The <NUM> steering angles are evenly spaced between -<NUM>° and <NUM>°, meaning that the central <NUM> steering angles are separated by an angle of <NUM>°. It is clear to see from this image that the reduced number of steering angles results in a lower image contrast.

The third image <NUM> shows the result of applying the transmit extrapolation described above to the second image <NUM>. In this case, an extrapolation filter of order <NUM> was used to extrapolate the number of transmit angles by a factor of <NUM>. The image is generated by coherently compounding the data from the initial <NUM> transmit angles with the <NUM> predicted beamsum data from the extrapolation. As can be seen from the third image, the contrast is significantly improved from the second image and is comparable to that of the reference image. In addition, the contrast enhancement does not result in any artifacts suppressing the weak point scatterers inside the anechoic lesion.

The first image <NUM> shows a reference image of an apical <NUM>-chamber view of the heart form a patient. The image was formed using <NUM> diverging waves.

The second image <NUM> shows an image of the same data set formed with only <NUM> diverging waves. As before, the <NUM> diverging waves were selected as the central <NUM> of the <NUM> steering angles. As can be seen from the image, the image contrast of the second image is significantly lower than that of the first image.

The third image <NUM> shows the result of applying a transmit extrapolation to the second image as described above. In this case, an extrapolation filter of order <NUM> was used to extrapolate the number of steering angles by a factor of <NUM>. Once again, the image is generated by coherently compounding the data from the initial <NUM> transmit angles with the <NUM> predicted beamsum data from the extrapolation. It is clear to see from the third image that the image contrast has been improved by the transmit extrapolation.

The first image <NUM> shows a reference image of an apical <NUM>-chamber view of the heart from a different patient to <FIG>. The image was formed using <NUM> diverging waves. Unlike <FIG>, the initial reference image for this patient has a poor image contrast.

The second image <NUM> shows an image of the same data set formed with only <NUM> diverging waves. As before, the <NUM> diverging waves were selected as the central <NUM> of the <NUM> steering angles. As can be seen from the image, the image contrast of the second image is significantly lower than that of the first image, which in this case renders a lot of the finer detail extremely unclear.

It should be noted that any combination of extrapolation factor and extrapolation filter order may be used in any of the methods described above. In addition, any number of steering angles may be selected in the final method.

In some ultrasound systems, a combination of the above methods may be employed in order to further increase the image quality of the final ultrasound image.

For example, the aperture extrapolation method, described with reference to <FIG>, may be performed on a set of channel data followed by the transmit extrapolation method, described with reference to <FIG>. In this case, the aperture extrapolation may be performed for each transmit signal of the ultrasonic probe and the extrapolated channel data summed over the aperture, thereby generating a set of aperture extrapolated channel data. Following the summation, the transmit extrapolation may be performed on the aperture extrapolated channel data. In this way, the lateral resolution and image contrast of the final ultrasound image may be improved. In addition, for PWI and DWI ultrasound systems, the use of the transmit extrapolation method may allow for an increase in the frame rate of the ultrasound image.

In another example, the transmit extrapolation method may be performed before the aperture extrapolation method. In this case, the transmit extrapolation may be performed for each transducer element of the ultrasonic probe and the extrapolated channel data summed over all transmit angles, thereby generating a set of transmit extrapolated channel data. The aperture extrapolation method may then be performed over the transmit extrapolated channel data.

In both cases, the selective attenuation method, described with reference to <FIG> and <FIG>, may be employed to reduce the amount of off-axis clutter in the final ultrasound image. As this method simply attenuates the signals possessing a high spatial frequency, it may be performed in any order with the aperture extrapolation and transmit extrapolation methods. Alternatively, the selective attenuation method may be combined with only the aperture extrapolation method or the transmit extrapolation method.

It should be noted that the signals used to form the channel, and beam-summed, data may be geometrically aligned prior to performing the methods described above.

As discussed above, embodiments make use of a controller for performing the data processing steps.

The controller can be implemented in numerous ways, with software and/or hardware, to perform the various functions required. A processor is one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform the required functions. A controller may however be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.

The storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at the required functions.

Claim 1:
A method for performing transmit extrapolation to generate an ultrasound image, the method comprising:
obtaining (<NUM>) beam-summed data using an ultrasonic probe, wherein the beam-summed data is obtained at a plurality of steering angles;
for each steering angle of the beam-summed data, segmenting (<NUM>) the beam-summed data based on an axial imaging depth of the beam-summed data;
for each segment of the segmented beam-summed data:
estimating (<NUM>) an extrapolation filter, based on the segmented beam-summed data, the extrapolation filter having a filter order; and
extrapolating (<NUM>), by an extrapolation factor, the segmented beam-summed data based on the extrapolation filter, thereby generating extrapolated beam-summed data; and
coherently compounding (<NUM>) the extrapolated beam-summed data across all segments, thereby generating the ultrasound image.