Ray-based inversion accounting for scattering for biomedical ultrasound tomography

Javaherian, Ashkan; Cox, Ben

doi:10.1088/1361-6420/ac28ed

Cited by 14 publications

(8 citation statements)

References 72 publications

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“…2,3 However, the computational burden associate with solving the wave equation at each FWI iteration has hampered the use of the FWI reconstruction approach in a clinical setting, in favor of less accurate but faster reconstruction methods such as bent-ray approaches. 4,5 This work investigates a deep learning based image reconstruction method, InversionNet, to alleviate the computational burden of FWI. InversionNet is a convolutional neural network architecture originally proposed for seismic imaging applications.…”

Section: Introductionmentioning

confidence: 99%

Data driven methods for ultrasound computed tomography

Lozenski

Wang

Wohlberg

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues. However, FWI reconstruction is computationally expensive, which limits its application in a clinical setting. This contribution investigates using the use of a convolutional neural network (CNN) to learn a mapping from USCT data to speed of sound estimates. The CNN was trained using a supervised approach that employed a large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and simulated USCT measurements. Once trained, this CNN can then be evaluated for real-time FWI image reconstruction from USCT data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT images. Accuracy was measured using relative mean square error (RMSE) and structural self-similarity index measure (SSIM). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI, while significantly reducing computational time and memory requirements.

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Section: Introductionmentioning

confidence: 99%

Data driven methods for ultrasound computed tomography

Lozenski

Wang

Wohlberg

et al. 2023

Medical Imaging 2023: Physics of Medical Imaging

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“…A popular conventional approach for USCT image reconstruction is ray-based methods (Ozmen et al 2015). Ray-based methods reduce the wave propagation model into a ray-propagation problem which reduces the computation burden (Javaherian and Cox 2021). The ray-based methods achieve a good balance between…”

Section: Introductionmentioning

confidence: 99%

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction

Zhao¹,

Fan²,

Wang³

et al. 2023

Phys. Med. Biol.

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Objective: The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up the image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap. Approach: To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines. Main results: The experimental results show that our deep learning methods help to improve the neural networks' robustness against noise and the generalizability to real measurement data. Significance: Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for application of deep learning methods to practical ulrasound tomography image reconstruction based on simulation dataset.

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“…To account for the relevant wave physics and thereby achieve high spatial resolution images, full-waveform inversion (FWI) reconstruction methods [3], [13], [17], [18] are being developed. Such advanced reconstruction methods can circumvent the limitations of simplified physics methods [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Investigation of an elevation-focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography

Li¹,

Villa²,

Duric³

et al. 2022

Medical Imaging 2022: Ultrasonic Imaging and Tomography

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Ultrasound computed tomography (USCT) is an emerging medical imaging modality that holds great promise for improving human health. Full-waveform inversion (FWI)-based image reconstruction methods account for the relevant wave physics to produce high spatial resolution images of the acoustic properties of the breast tissues. A practical USCT design employs a circular ring-array comprised of elevation-focused ultrasonic transducers, and volumentric imaging is achieved by translating the ring-array orthogonally to the imaging plane. In commonly deployed slice-by-slice (SBS) reconstruction approaches, the three-dimensional (3D) volume is reconstructed by stacking together two-dimensional (2D) images reconstructed for each position of the ring-array. A limitation of the SBS reconstruction approach is that it does not account for 3D wave propagation physics and the focusing properties of the transducers, which can result in significant image artifacts and inaccuracies. To perform 3D image reconstruction when elevation-focused transducers are employed, a numerical description of the focusing properties of the transducers should be included in the forward model. To address this, a 3D computational model of an elevation-focused transducer is developed to enable 3D FWI-based reconstruction methods to be deployed in ring-array-based USCT. The focusing is achieved by applying a spatially varying temporal delay to the ultrasound pulse (emitter mode) and recorded signal (receiver mode). The proposed numerical transducer model is quantitatively validated and employed in computer-simulation studies that demonstrate its use in image reconstruction for ringarray USCT.

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Ray-based inversion accounting for scattering for biomedical ultrasound tomography

Cited by 14 publications

References 72 publications

Data driven methods for ultrasound computed tomography

Data driven methods for ultrasound computed tomography

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction

Investigation of an elevation-focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography

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