Lateral Resolution Improvement in Ultrasound Imaging System using Compressed Sensing: Initial Results
01 Jul 2019-Vol. 2019, pp 2727-2730
TL;DR: The results indicate that the proposed framework of choosing a limited number of receive elements from a receive aperture length that is three or four times the corresponding active aperture size obtained from the same number of consecutive receive elements yields nearly twice an improvement in LR and about 27% increase to that of CFB reference image.
Abstract: Compressed-Sensing (CS) has been applied to ultrasound imaging to reduce data or to reduce the data acquisition time. There appears to be no report that uses CS framework to reduce the number of active receive elements in Conventional Focused Beamforming (CFB). Thus, in our previous work, a novel undersampling scheme based on Gaussian distribution was investigated and reported for reducing the number of active receive elements and data in CFB. In this paper, we exploit the Gaussian sampling based CS framework to improve the lateral resolution (LR) of the ultrasound system without increasing the system’s complexity and cost. A notable difference from our previous work being the use of waveatom as the sparsifying basis, instead of 2D-Fourier basis, and analysis of the proposed framework for different receive aperture sizes. Simulation data for this study were generated using Field II, and experimental data were acquired from an in-vitro cyst phantom using Verasonics V-64 ultrasound scanner. The results indicate that the proposed framework of choosing a limited number of receive elements from a receive aperture length that is three or four times the corresponding active aperture size obtained from the same number of consecutive receive elements yields nearly twice an improvement in LR and about 27% increase in contrast to that of CFB reference image. Thus, the findings suggest a possibility to improve the LR of the current ultrasound system without increasing the system complexity, which will be significant for affordable point-of-care ultrasound systems.
Citations
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TL;DR: This work introduces a novel framework, namely Gaussian undersampling-based CS framework (GAUCS) with wave atoms as a sparsifying basis for CFB imaging method and finds that the GAUCS framework can play a significant role in improving the performance of affordable point-of-care ultrasound systems.
Abstract: In conventional focused beamforming (CFB), there is a known tradeoff between the active aperture size of the ultrasound transducer array and the resulting image quality. Increasing the size of the active aperture leads to an increase in the image quality of the ultrasound system at the expense of increased system cost. An alternate approach is to get rid of the requirement of having consecutive active receive elements and instead place them in a random order in a larger aperture. This, in turn, creates an undersampled situation where there are only $M$ active elements placed in a larger aperture, which can accommodate $N$ consecutive receive elements (with $M ). It is possible to formulate and solve the above-mentioned undersampling situation using a compressed sensing (CS) approach. In our previous work, we had proposed Gaussian undersampling strategy for reducing the number of active receive elements. In this work, we introduce a novel framework, namely Gaussian undersampling-based CS framework (GAUCS) with wave atoms as a sparsifying basis for CFB imaging method. The performance of the proposed method is validated using simulation and in vitro phantom data. Without an increase in the active elements, it is found that the proposed GAUCS framework improved the lateral resolution (LR) and image contrast by 27% and 1.5 times, respectively, while using 16 active elements and by 39% and 1.1 times, respectively, while using 32 active elements. Thus, the GAUCS framework can play a significant role in improving the performance, especially, of affordable point-of-care ultrasound systems.
21 citations
TL;DR: This work adopts a CS framework to MSTA, with a motivation to reduce the number of receive elements and data and finds that the images recovered using CS were comparable to those of reference full-aperture case in terms of estimated lateral resolution, contrast-to-noise ratio, and structural similarity indices.
Abstract: Recently, researchers have shown an increased interest in ultrasound imaging methods alternate to conventional focused beamforming (CFB). One such approach is based on the synthetic aperture (SA) scheme; more popular are the ones based on synthetic transmit aperture (STA) schemes with a single-element transmit or multielement STA (MSTA). However, one of the main challenges in translating such methods to low-cost ultrasound systems is the tradeoffs among image quality, frame rate, and complexity of the system. These schemes use all the transducer elements during receive, which dictates a corresponding number of parallel receive channels, thus increasing the complexity of the system. A considerable amount of literature has been published on compressed sensing (CS) for SA imaging. Such studies are aimed at reducing the number of transmissions in SA but still recover images of acceptable quality at high frame rate and fail to address the complexity due to full-aperture receive. In this work, we adopt a CS framework to MSTA, with a motivation to reduce the number of receive elements and data. The CS recovery performance was assessed for the simulation data, tissue-mimicking phantom data, and an example in vivo biceps data. It was found that in spite of using 50% receive elements and overall using only 12.5% of the data, the images recovered using CS were comparable to those of reference full-aperture case in terms of estimated lateral resolution, contrast-to-noise ratio, and structural similarity indices. Thus, the proposed CS framework provides some fresh insights into translating the MSTA imaging method to affordable ultrasound scanners.
8 citations
04 Jun 2023
TL;DR: In this article , a low-rank and joint-sparse model is proposed to reduce the amount of sampled channel data of focused beam imaging by considering all the received data as a 2D matrix.
Abstract: Ultrasound plane wave imaging is widely used in many applications thanks to its capability in reaching high frame rates. However, the amount of data acquisition and storage in a period of time can become a bottleneck in ultrasound system design for thousands frames per second. In our previous study, we proposed a low-rank and joint-sparse model to reduce the amount of sampled channel data of focused beam imaging by considering all the received data as a 2D matrix. However, for a single plane wave transmission, the number of channels is limited and the low-rank property of the received data matrix is no longer achieved. In this study, a L 0 -norm based Hankel structured low-rank and sparse model is proposed to reduce the channel data. An optimization algorithm, based on the alternating direction method of multipliers (ADMM), is proposed to efficiently solve the resulting optimization problem. The performance of the proposed approach was evaluated using the data published in Plane Wave Imaging Challenge in Medical Ultrasound (PICMUS) in 2016. Results on channel and plane wave data show that the proposed method is better adapted to the ultrasound channel signal and can recover the image with fewer samples than the conventional CS method.
References
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TL;DR: It is shown that a valid CS framework can be derived from ultrasound propagation theory, and that this framework could be used to compute images of scatterers using only one plane wave as a transmit beam.
Abstract: Ultrasound imaging is a wide spread technique used in medical imaging as well as in non-destructive testing. The technique offers many advantages such as real-time imaging, good resolution, prompt acquisition, ease of use, and low cost compared to other techniques such as x-ray imaging. However, the maximum frame rate achievable is limited as several beams must be emitted to compute a single image. For each emitted beam, one must wait for the wave to propagate back and forth, thus imposing a limit to the frame rate. Several attempts have been made to use less beams while maintaining image quality. Although efficiently increasing the frame rate, these techniques still use several transmit beams. Compressive Sensing (CS), a universal data completion scheme based on convex optimization, has been successfully applied to a number of imaging modalities over the past few years. Using a priori knowledge of the signal, it can compute an image using less data allowing for shorter acquisition times. In this paper, it is shown that a valid CS framework can be derived from ultrasound propagation theory, and that this framework can be used to compute images of scatterers using only one plane wave as a transmit beam.
67 citations
"Lateral Resolution Improvement in U..." refers background in this paper
...On the other hand, another cluster of research focuses on using the under-sampling framework of CS to reduce the number of transmit beams without compromising the image quality, which increases the frame rate [7-8]....
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TL;DR: This paper applies Compressed Sensing techniques to analog ultrasound signals, following the recently developed Xampling framework, resulting in a system with significantly reduced sampling rates which, in turn, means significantly reduced data size while maintaining the quality of the resulting images.
Abstract: Recent developments of new medical treatment techniques put challenging demands on ultrasound imaging systems in terms of both image quality and raw data size. Traditional sampling methods result in very large amounts of data, thus, increasing demands on processing hardware and limiting the exibility in the post-processing stages. In this paper, we apply Compressed Sensing (CS) techniques to analog ultrasound signals, following the recently developed Xampling framework. The result is a system with significantly reduced sampling rates which, in turn, means significantly reduced data size while maintaining the quality of the resulting images.
29 citations
"Lateral Resolution Improvement in U..." refers methods in this paper
...These include sparse representation of RF data in wave atoms basis [4], a combination of complementary information from adjacent RF data using distributed sampling [5] and Nyquist sub-sampling using Finite rate of Innovation framework [6]....
[...]
TL;DR: In this paper, the authors apply compressed sensing (CS) techniques to analog ultrasound signals, following the recently developed Xampling framework, which results in significantly reduced sampling rates which, in turn, means significantly reduced data size while maintaining the quality of the resulting images.
Abstract: Recent developments of new medical treatment techniques put challenging demands on ultrasound imaging
systems in terms of both image quality and raw data size. Traditional sampling methods result in very large
amounts of data, thus, increasing demands on processing hardware and limiting the flexibility in the postprocessing
stages.
In this paper, we apply Compressed Sensing (CS) techniques to analog ultrasound signals, following the recently
developed Xampling framework. The result is a system with significantly reduced sampling rates which, in turn,
means significantly reduced data size while maintaining the quality of the resulting images.
26 citations
TL;DR: A novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate is presented.
Abstract: High efficient acquisition of the sensor array signals and accurate reconstruction of the backscattering medium are important issues in ultrasound imaging instrument. This paper presents a novel measurement-domain adaptive beamforming approach (MABF) based on distributed compressed sensing (DCS) which seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated with much few measurements under the Nyquist rate. Instead of sampling conventional backscattering signals at the Nyquist rate, few linear projections of the returned signal with random vectors are taken as measurements, which can reduce the amount of samples per channel greatly and makes the real-time transmission of sensor array data possible. Then high resolution ultrasound image is reconstructed from the few measurements of DCS directly by the proposed MABF algorithm without recovering the raw sensor signals with complex convex optimization algorithm. The simulated results show the effectiveness of the proposed method.
19 citations
"Lateral Resolution Improvement in U..." refers methods in this paper
...These include sparse representation of RF data in wave atoms basis [4], a combination of complementary information from adjacent RF data using distributed sampling [5] and Nyquist sub-sampling using Finite rate of Innovation framework [6]....
[...]
TL;DR: CS with the proposed Gaussian sampling scheme for channel data subsampling not only reduces the data size significantly, but also strategically uses only a few active receive elements in the process; thus, it can provide an attractive option for the affordable point-of-care ultrasound system.
Abstract: Recently, compressed sensing (CS) has been applied to ultrasound imaging for either data reduction or frame rate improvement. However, there are no detailed reports yet on strategies for lateral undersampling of channel data in conventional focused beamforming (CFB) and its recovery exploiting the CS approach. We propose a strategic lateral undersampling approach for channel data using the Gaussian sampling scheme and compare it with a direct extension of the often-used uniform undersampling reported for axial undersampling to the lateral direction and 2-D random sampling reported in the literature. As opposed to the reported 2-D random undersampling, we explore undersampling of channel data in the lateral direction by acquiring radiofrequency data from only a reduced number of chosen receive elements and subjecting these data to further undersampling in the axial direction. The effect of the sampling schemes on CS recovery was studied using data from simulations and experiments for various lateral and axial undersampling rates. The results suggest that CS-recovered data from the Gaussian distribution-based channel data subsampling yielded better recovery and contrast in comparison to those obtained from the often-used uniform distribution-based undersampling. Although 90% of the samples from the original data using the proposed sampling scheme were discarded, the contrast of the CS-recovered image was comparable to that of the reference image. Thus, CS with the proposed Gaussian sampling scheme for channel data subsampling not only reduces the data size significantly, but also strategically uses only a few active receive elements in the process; thus, it can provide an attractive option for the affordable point-of-care ultrasound system.
10 citations
"Lateral Resolution Improvement in U..." refers background in this paper
...In our previous work, a novel lateral undersampling scheme based on Gaussian distribution was proposed to reduce the number of active receive elements and data for CFB [10]....
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