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
More filters
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
More filters
01 Oct 2018
TL;DR: The results clearly demonstrate that it is possible to reduce the active channel count and data size using CS framework and obtain better-quality image compared to that obtained from corresponding fully-sampled data.
Abstract: Affordable point-of-care ultrasound systems use Conventional Focused Beamforming (CFB) with limited number of active elements, which leads to reduction in the image quality. In addition, these systems have limited storage capacity. Therefore, reduction in the number of active elements and data in CFB without accompanying compromise in the image quality is highly desired. For data reduction, axial undersampling of the channel RF data and recovery of the fully-sampled RF data using Compressed Sensing (CS) has been proposed earlier. However, there is no detailed investigation on aperture undersampling and its recovery using CS framework, which leads to data reduction as well as reduction in the number of active receive elements. The central theme of this work is to investigate two sampling schemes to undersample the receive aperture (lateral undersampling) as well as axial undersampling of the selected channel RF data for CS framework. Experimental data for this study were acquired from a wire phantom and in-vitro cyst phantom using Sonix Touch $\text{Q}+^{(\text{R})}$ ultrasound scanner. The results indicate that CS with Gaussian undersampling outperforms the uniform undersampling. In spite of discarding 90% of samples from the original RF frame data, B-mode images from CS framework had better LR and comparable contrast to that of reference image. Further, the results suggest that as the size of receive aperture from which the subset of channels are chosen increases, LR improves but contrast deteriorates. Thus, the results clearly demonstrate that it is possible to reduce the active channel count and data size using CS framework and obtain better-quality image compared to that obtained from corresponding fully-sampled data.
5 citations
Additional excerpts
...Also, the impact of the sampling scheme on the LR of the CFB system was briefly attempted [11]....
[...]
Posted Content•
TL;DR: In this article, a sparsity-promoting method is proposed to recover the spatial compressibility fluctuations in weakly-scattering soft tissue structures, where an orthonormal basis meets condition.
Abstract: Established image recovery methods in fast ultrasound imaging, e.g. delay-and-sum, trade the image quality for the high frame rate. Cutting-edge inverse scattering methods based on compressed sensing (CS) disrupt this tradeoff via a priori information. They iteratively recover a high-quality image from only a few sequential pulse-echo measurements or less echo signals, if (i) a known dictionary of structural building blocks represents the image almost sparsely, and (ii) their individual pulse echoes, which are predicted by a linear model, are sufficiently uncorrelated. The exclusive modeling of the incident waves as steered plane waves or cylindrical waves, however, has so far limited the convergence speed, the image quality, and the potential to meet condition (ii). Motivated by the benefits of randomness in CS, a novel method for the fast compressed acquisition and the subsequent recovery of images is proposed to overcome these limitations. It recovers the spatial compressibility fluctuations in weakly-scattering soft tissue structures, where an orthonormal basis meets condition (i), by a sparsity-promoting $\ell_{q}$-minimization method, $q \in [0; 1]$. A realistic $d$-dimensional model, $d \in \{2, 3\}$, accounting for diffraction, single monopole scattering, the combination of power-law absorption and dispersion, and the specifications of a planar transducer array, predicts the pulse echoes of the individual basis functions. Three innovative types of incident waves, whose syntheses leverage random apodization weights, time delays, or combinations thereof, aid in meeting condition (ii). In two-dimensional numerical simulations, single realizations of these waves outperform the prevalent quasi-plane wave for both the canonical and the Fourier bases. They significantly reduce the full extents at half maximum of the point spread functions by up to 73.7 %.
3 citations
01 Sep 2017
TL;DR: This study leverages three types of random ultrasonic waves to better conform with the requirements of CS, which increases both the image quality and the speed of convergence.
Abstract: Multiple research groups have recently innovated image recovery methods for fast pulse-echo ultrasound imaging (UI) that combine inverse scattering techniques with compressed sensing (CS). These methods alleviate the inherent tradeoff between the image quality and the image acquisition rate. The choice of the incident sound field is a crucial degree of freedom to implement the specific requirements of CS, e.g. incoherent measurements. Previous publications exclusively investigated steered plane waves (PWs). In this study, we leverage three types of random ultrasonic waves to better conform with the requirements of CS. This increases both the image quality and the speed of convergence.
3 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]....
[...]