Adaptive Ultrasound Beamforming Using Deep Learning

Luijten, Ben; Cohen, Regev; Bruijn, Frederik J. de; Schmeitz, Harold A.W.; Mischi, Massimo; Eldar, Yonina C.; Sloun, Ruud J. G. van

doi:10.1109/tmi.2020.3008537

Cited by 145 publications

(71 citation statements)

References 25 publications

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“…In the decoder path, 2×2 transposed convolutions with ReLU activations are used as up-sampling operators. The number of channels is progressively increased in the encoder path (32,64,128,256, and 256 filters) and then decreased in the decoder (256, 128, 64, and 32 filters). The output layer is a single-channel 1×1 convolution block.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by recent advances in the use of convolutional neural networks (CNNs) for the reconstruction and processing of biomedical images from sparse data [18][19][20] , in this paper we propose Deep-fUS, an end-to-end deep learning approach to reconstruct power Doppler images from sub-optimally sampled compound datasets. Recently, CNN solutions have been proposed in medical ultrasound imaging for applications including contrast improvement 21 and image de-speckling 22 , ultrasound contrast agent localization and tracking 23,24 , and for undersampled and adaptive beamforming [25][26][27][28] . Here we implement a deep CNN based on the U-Net architecture 29 that learns a reconstruction mapping between the sequence of compound ultrasound data and the power Doppler output image (Figure 1g-h).…”

Section: Introductionmentioning

confidence: 99%

“…Variants of this model have been used for biomedical image reconstruction in applications spanning compressed sensing MRI 21 , sparse-projection photoacoustic imaging 22,23 , and sparse X-ray computed tomography 24,25 . Prior CNN applications in medical ultrasound imaging include contrast improvement 26 and image de-speckling 27 , ultrasound contrast agent localization and tracking 28,29 , and under-sampled and adaptive beamforming [30][31][32][33] . Our network learns a reconstruction mapping between the sparse sequence of compound ultrasound data and the power Doppler output image, without requiring any prior model-based knowledge (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Ianni

Airan

2020

Preprint

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Functional ultrasound imaging is rapidly establishing itself as a state-of-the-art neuroimaging modality owing to its ability to image neural activation in awake and mobile small animals, its relatively low cost, and its unequaled portability. To achieve high blood flow sensitivity in the brain microvasculature, functional ultrasound relies on long sequences of ultrasound data acquired at high frame rates, which pose high demands on the hardware architecture and effectively limit the practical utility and clinical translation of this imaging modality. Here we propose Deep-fUS, a deep learning approach that aims to significantly reduce the amount of ultrasound data necessary, while retaining the imaging performance. We trained a neural network to learn the power Doppler reconstruction function from sparse sequences of compound ultrasound data, using ground truth images from high-quality in vivo acquisitions in rats, and with a custom loss function. The network produces highly accurate images with restored sensitivity in the smaller blood vessels even when using heavily under-sampled data. We tested the network performance in a task-evoked functional neuroimaging application, demonstrating that time series of power Doppler images can be reconstructed with adequate accuracy to compute functional activity maps. Notably, the network reduces the occurrence of motion artifacts in awake functional ultrasound imaging experiments. The proposed platform can facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or even in clinical scanners, opening the way to new potential applications based on this technology. To our knowledge, this is the first attempt to implement a convolutional neural network approach for power Doppler reconstruction from sparse ultrasonic datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Ianni

Airan

2020

Preprint

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“…The CUBDL organizers then downloaded the submitted models and launched a Python script to perform evaluation [15] on the internationally crowd-sourced database of test data [16]. Evaluation metrics advertised since the launch of the CUBDL website [14] were pre-selected by the CUBDL organizers based on literature from multiple groups reporting beamforming with deep learning (e.g., [8], [9], [11], [23]) and based on common computer vision literature containing assessments of network complexity (e.g., [24]- [26]).…”

Section: Challenge Summary and Timelinementioning

confidence: 99%

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

Hyun

Wiacek

Goudarzi

et al. 2021

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This paper introduces the largest known international database of ultrasound channel data and describes associated evaluation methods that were initially developed for the Challenge on Ultrasound Beamforming with Deep Learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-andsum beamforming, compared to a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm *CUBDL Organizers **Post-CUBDL Evaluator and Post-CUBDL Data Curator M.A.L.B. and A.W. acknowledge the National Institutes of Health (NIH) Trailblazer Award R21 EB025621 for partial support of their time on this project. H.R. and S.G. acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-2020-04612.

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“…Over decades, the evolution in the commercial ultrasound apparatus allowed it to offer high-quality images at a frame rate span above any other imaging technique [12]. Recently, exploiting artificial intelligence techniques, new solutions to adaptive beamforming are being proposed to lower reconstruction time and computational burden [13,14].…”

Section: Ultrasound Imaging 21 Piezoelectric Transducers Technologymentioning

confidence: 99%

The Core of Medical Imaging: State of the Art and Perspectives on the Detectors

et al. 2021

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In this review, the roles of detectors in various medical imaging techniques were described. Ultrasound, optical (near-infrared spectroscopy and optical coherence tomography) and thermal imaging, magnetic resonance imaging, computed tomography, single-photon emission tomography, positron emission tomography were the imaging modalities considered. For each methodology, the state of the art of detectors mainly used in the systems was described, emphasizing new technologies applied.

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Adaptive Ultrasound Beamforming Using Deep Learning

Cited by 145 publications

References 25 publications

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets

The Core of Medical Imaging: State of the Art and Perspectives on the Detectors

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