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

TL;DR

This paper presents a comprehensive computational strategy for high-resolution 3D ultrasonic breast imaging using time-domain full waveform inversion, overcoming key challenges in memory, computation, and convergence to produce detailed images efficiently.

Contribution

It introduces novel methods including time reversal-based gradient computation, source encoding for stochastic gradients, and multi-grid schemes, enabling efficient high-resolution 3D UST imaging on a single GPU.

Findings

Achieved 0.5mm resolution in a 442x442x222 voxel image.

Reduced memory footprint significantly with time reversal gradient computation.

Completed high-resolution 3D imaging within 24 hours on a single GPU.

Abstract

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 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 24h.