A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Feigin, Micha; Freedman, Daniel Z.; Anthony, Brian W.

doi:10.1109/tbme.2019.2931195

Cited by 87 publications

(65 citation statements)

References 67 publications

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“…Luchies and Byram [23] proposed a frequency domain deep learning method for suppressing offaxis scattering in ultrasound channel data. In [24], a deep neural network is designed to estimate the attenuation characteristics of sound in human body. In [25], [26], ultrasound image denoising method is proposed for the B-mode and single angle plane wave imaging.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

Khan

Huh

2020

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

105

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In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast to noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here we propose a deep learning-based end-to-end beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or sub-sampled radio-frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high quality ultrasound images using a single beamformer. The origin of such adaptivity is also theoretically analyzed. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

Khan

Huh

2020

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

105

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“…The success of the presented results has implications for providing multiple (i.e., more than two) DNN outputs from a single network input. For example, in addition to beamforming and segmentation, deep learning ultrasound image formation tasks have also been proposed for sound speed estimation [60], speckle reduction [43], reverberation noise suppression [61], and minimum-variance directionless response beamforming [62], as well as to create ultrasound elastography images [63], CT-like ultrasound images [64], B-mode images from echogenecity maps [65], and ultrasound images from 3D spatial locations [66]. We envisage the future use of parallel networks that output any number of these or other mappings to provide a one-step approach to obtain multimodal information, each originating from a singular input of raw ultrasound data.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

Nair

Washington

Tran

et al. 2020

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

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Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this limitation, we are exploring the use of deep neural networks (DNNs) as an alternative to delay-and-sum beamforming. The objectives of this work are to obtain information directly from raw channel data and to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. We focus on visualizing and segmenting anechoic targets surrounded by tissue and ignoring or de-emphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo datasets that were not included during training. With unfocused input channel data (i.e., prior to the application of receive time delays), simulated, experimental phantom, and in vivo test datasets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92±0.03, and 0.77±0.07, respectively, and generalized contrastto-noise ratios (gCNR) of 0.95±0.08, 0.93±0.08, and 0.75±0.14, respectively. With subaperture beamformed channel data and a modification to the input layer of the DNN architecture to accept these data, the fidelity of image reconstruction increased (e.g., mean gCNR of multiple acquisitions of two in vivo breast cysts ranged 0.89-0.96), but DNN display frame rates were reduced from 395 Hz to 287 Hz. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.

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“…In these works, the authors propose neural-network-based models that produce spatially-varying linear elastic material properties from forcedisplacement measurements, free from prior assumptions on the underlying constitutive models or material properties. In [118], a deep convolutional neural network is used for speedof-sound estimation from (single-sided) B-mode channel data. In [119], the authors address the problem by introducing an unfolding strategy to yield a dedicated network based on the iterative wave reflection tracking algorithm.…”

Section: Other Applications Of Deep Learning In Ultrasoundmentioning

confidence: 99%

Deep Learning in Ultrasound Imaging

2020

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Deep learning is taking an ever more prominent role in medical imaging. This paper discusses applications of this powerful approach in ultrasound imaging systems along with domain-specific opportunities and challenges.ABSTRACT | We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage.As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

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A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Cited by 87 publications

References 67 publications

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data

Deep Learning in Ultrasound Imaging

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