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Proceedings Paper

Time-domain spectral-element ultrasound waveform tomography using a stochastic quasi-Newton method
Author(s): Christian Boehm; Naiara Korta Martiartu; Nicolas Vinard; Ivana Jovanović Balic; Andreas Fichtner
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Paper Abstract

Waveform inversion for ultrasound computed tomography (USCT) is a promising imaging technique for breast cancer screening. However, the improved spatial resolution and the ability to constrain multiple parameters simultaneously demand substantial computational resources for the recurring simulations of the wave equation. Hence, it is crucial to use fast and accurate methods for numerical wave propagation, on the one hand, and to keep the number of required simulations as small as possible, on the other hand. We present an efficient strategy for acoustic waveform inversion that combines (i) a spectral-element continuous Galerkin method for solving the wave equation, (ii) conforming hexahedral mesh generation to discretize the scanning device, (iii) a randomized descent method based on mini-batches to reduce the computational cost for misfit and gradient computations, and (iv) a trust-region method using a quasi-Newton approximation of the Hessian to iteratively solve the inverse problem. This approach combines ideas and state-of-the-art methods from global-scale seismology, large-scale nonlinear optimization, and machine learning. Numerical examples for a synthetic phantom demonstrate the efficiency of the discretization, the effectiveness of the mini-batch approximation and the robustness of the trust-region method to reconstruct the acoustic properties of breast tissue with partial information.

Paper Details

Date Published: 6 March 2018
PDF: 9 pages
Proc. SPIE 10580, Medical Imaging 2018: Ultrasonic Imaging and Tomography, 105800H (6 March 2018); doi: 10.1117/12.2293299
Show Author Affiliations
Christian Boehm, ETH Zurich (Switzerland)
Naiara Korta Martiartu, ETH Zurich (Switzerland)
Nicolas Vinard, ETH Zurich (Switzerland)
TU Delft (Netherlands)
Ivana Jovanović Balic, SonoView Acoustic Sensing Technologies (Switzerland)
Andreas Fichtner, ETH Zurich (Switzerland)

Published in SPIE Proceedings Vol. 10580:
Medical Imaging 2018: Ultrasonic Imaging and Tomography
Neb Duric; Brett C. Byram, Editor(s)

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