Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction

Dei, Kazuyuki; Schlunk, Siegfried; Byram, Brett

doi:10.1109/tuffc.2019.2924824

Cited by 14 publications

(3 citation statements)

References 51 publications

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“…Spatial prediction filtering 9 , 10 employs an autoregressive model to the received signals prior to summation to remove noise and off-axis clutter in the final B-mode image. Furthermore, minimum variance, 11 , 12 delay-multiply-and-sum, and Wiener beamforming 13 , 14 have been used to suppress sidelobes and improve lateral resolution in B-mode images, whereas aperture domain model image reconstruction 15 , 16 applies a model and regularization scheme to detect and remove multipath scattering in the channel data prior to summation.…”

Section: Introductionmentioning

confidence: 99%

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum

et al. 2022

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“…In the field of US imaging, ICA has been mainly used for clutter filtering and noise suppression [25], [26], [27], [28], [29], [30]. Recently, ICA was used as a dimensionality reduction technique to speed up ADMIRE beamforming [31].…”

Section: Introductionmentioning

confidence: 99%

Plane-Wave Ultrasound Beamforming Through Independent Component Analysis

Goudarzi¹,

Asif²,

Rivaz³

2020

Preprint

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Beamforming in plane-wave imaging (PWI) is an essential step in creating images with optimal quality. Adaptive methods estimate the apodization weights from echo traces acquired by several transducer elements. Herein, we formulate plane-wave beamforming as a blind source separation problem. The output of each transducer element is considered as a nonindependent observation of the field. As such, beamforming can be formulated as the estimation of an independent component out of the observations. We then adapt the independent component analysis (ICA) algorithm to solve this problem and reconstruct the final image. The proposed method is evaluated on a set of simulation, real phantom, and in vivo data available from the PWI challenge in medical ultrasound. The performance of the proposed beamforming approach is also evaluated in different imaging settings. The proposed algorithm improves lateral resolution by as much as 36.5% and contrast by 10% as compared to the classical delay and sum. Moreover, results are compared with other well-known adaptive methods. Finally, the robustness of the proposed method to noise is investigated.

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“…Although effective, most of these advanced methods are computationally intensive and/or limited by user defined parameters. For example, despite achieving consistently superior image quality compared to DAS as well as other advanced techniques, a model-based approach called aperture domain model image reconstruction (ADMIRE) is exceedingly computationally complex, preventing real-time adjustable implementations [8].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation for Ultrasound Beamforming

Tierney¹,

Luchies²,

Khan³

et al. 2020

Preprint

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Ultrasound B-Mode images are created from data obtained from each element in the transducer array in a process called beamforming. The beamforming goal is to enhance signals from specified spatial locations, while reducing signal from all other locations. On clinical systems, beamforming is accomplished with the delay-and-sum (DAS) algorithm. DAS is efficient but fails in patients with high noise levels, so various adaptive beamformers have been proposed. Recently, deep learning methods have been developed for this task. With deep learning methods, beamforming is typically framed as a regression problem, where clean, ground-truth data is known, and usually simulated. For in vivo data, however, it is extremely difficult to collect ground truth information, and deep networks trained on simulated data underperform when applied to in vivo data, due to domain shift between simulated and in vivo data. In this work, we show how to correct for domain shift by learning deep network beamformers that leverage both simulated data, and unlabeled in vivo data, via a novel domain adaption scheme. A challenge in our scenario is that domain shift exists both for noisy input, and clean output. We address this challenge by extending cycle-consistent generative adversarial networks, where we leverage maps between synthetic simulation and real in vivo domains to ensure that the learned beamformers capture the distribution of both noisy and clean in vivo data. We obtain consistent in vivo image quality improvements compared to existing beamforming techniques, when applying our approach to simulated anechoic cysts and in vivo liver data.

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Computationally Efficient Implementation of Aperture Domain Model Image Reconstruction

Cited by 14 publications

References 51 publications

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum

Separation of mainlobe and sidelobe contributions to B-mode ultrasound images based on the aperture spectrum

Plane-Wave Ultrasound Beamforming Through Independent Component Analysis

Domain Adaptation for Ultrasound Beamforming

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