Detecting muscle activation using ultrasound speed of sound inversion with deep learning

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

doi:10.1109/embc44109.2020.9175237

Cited by 10 publications

(12 citation statements)

References 14 publications

(11 reference statements)

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“…For SoS reconstruction, acquiring sufficient measured data alongside its corresponding GT for training a deep neural network is challenging because there is no known gold-standard method capable of creating exact GT for reflection data (pulse-echo ultrasound) and there are only a few phantoms available with known heterogeneous SoS. Therefore, for deep learning-based approaches using simulated data for training is a common practice [7,10,11,12,13,14,15,16]. K-Wave toolbox [26] (Version 1.3) is used for the simulation of training data.…”

Section: Datasetmentioning

confidence: 99%

“…The size of the medium is considered to be 3.8 cm in depth and 7.6 cm in the lateral direction (twice the size of the probe head) and the probe head is placed above the central section of the medium. A single plane-wave with zero degrees is used for the simulations [11,13,15]. The medium is simulated on a 2D grid of size 1536 × 3072.…”

Section: Datasetmentioning

confidence: 99%

“…Similar techniques are employed for SoS reconstruction from ultrasound echo data. [10,11,12,13,14,15,16] investigated encoder-decoder networks with multiple or single steering angles for SoS reconstruction. In these studies, an encoder-decoder network takes sensor domain data as input and directly reconstructs the SoS map in the output.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound

Jush¹,

Biele²,

Dueppenbecker³

et al. 2022

Preprint

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Quantitative ultrasound, e.g., speed-of-sound (SoS) in tissues, provides information about tissue properties that have diagnostic value. Recent studies showed the possibility of extracting SoS information from pulse-echo ultrasound raw data (a.k.a. RF data) using deep neural networks that are fully trained on simulated data. These methods take sensor domain data, i.e., RF data, as input and train a network in an end-to-end fashion to learn the implicit mapping between the RF data domain and SoS domain. However, such networks are prone to overfitting to simulated data which results in poor performance and instability when tested on measured data. We propose a novel method for SoS mapping employing learned representations from two linked autoencoders. We test our approach on simulated and measured data acquired from human breast mimicking phantoms. We show that SoS mapping is possible using linked autoencoders. The proposed method has a Mean Absolute Percentage Error (MAPE) of 2.39% on the simulated data. On the measured data, the predictions of the proposed method are close to the expected values with MAPE of 1.1 %. Compared to an end-to-end trained network, the proposed method shows higher stability and reproducibility.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound

Jush¹,

Biele²,

Dueppenbecker³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Before we introduce the particular approach we follow in this work in more detail, we remark that as in many fields, there has been a recent flurry of interest into the question of whether deep neural networks can be utilized for ultrasonic imaging [89], see, e.g., [21,4,29,30,61,28,31,32,55,56,100,89] for approaches to improve the speed and accuracy of 2D UST reconstructions. While it seems likely that neural networks will find wide application in UST, there are reasons to think that, as in other imaging modalities, they will complement rather than supersede existing approaches [6].…”

Section: Image Reconstruction For Ultrasound Tomographymentioning

confidence: 99%

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

et al. 2021

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Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: Firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 103-104 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: Firstly, we exploit a gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442x442x222 voxel image with a resolution of 0.5mm using Matlab on a single GPU within 24 hours.

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“…Before we introduce the particular approach we follow in this work in more detail, we remark that as in many fields, there has been a recent flurry of interest into the question of whether deep neural networks can be utilized for ultrasonic imaging [83], see, e.g., [19,4,27,28,59,26,29,30,53,54,93,83] for approaches to improve the speed and accuracy of 2D UST reconstructions. While it seems likely that neural networks will find wide application in UST, there are reasons to think that, as in other imaging modalities, they will complement rather than supersede existing approaches [6].…”

Section: Image Reconstruction For Ultrasound Tomographymentioning

confidence: 99%

High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion

Lucka,

Pérez-Liva,

Treeby

et al. 2021

Preprint

View full text Add to dashboard Cite

Ultrasound tomography (UST) scanners allow quantitative images of the human breast's acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: Firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 10 3 − 10 4 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: Firstly, we introduce a novel gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442 × 442 × 222 voxel image with a resolution of 0.5 mm using Matlab on a single GPU within 24 hours.

show abstract

Detecting muscle activation using ultrasound speed of sound inversion with deep learning

Cited by 10 publications

References 14 publications

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound

AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound

High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

High Resolution 3D Ultrasonic Breast Imaging by Time-Domain Full Waveform Inversion

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