Stride: a flexible platform for high-performance ultrasound computed tomography

TL;DR

Stride is an open-source Python library that simplifies high-performance ultrasound tomography, enabling advanced techniques like full-waveform inversion to be more accessible, scalable, and reproducible in research and clinical applications.

Contribution

The paper introduces Stride, a novel open-source platform that integrates high-level optimization interfaces with scalable wave-equation solvers and parallelization for ultrasound tomography.

Findings

Successfully handles 2D and 3D realistic tomographic problems.

Provides scalable performance from local to high-performance clusters.

Facilitates reproducible research in ultrasound tomography.

Abstract

Advanced ultrasound computed tomography techniques like full-waveform inversion are mathematically challenging and orders of magnitude more computationally expensive than conventional ultrasound imaging methods. This computational and algorithmic complexity, and a lack of open-source libraries in this field, represent a barrier preventing the generalised adoption of these techniques, slowing the pace of research and hindering reproducibility. Consequently, we have developed Stride, an open-source Python library for the solution of large-scale ultrasound tomography problems. On one hand, Stride provides high-level interfaces and tools for expressing the types of optimisation problems encountered in medical ultrasound tomography. On the other, these high-level abstractions seamlessly integrate with high-performance wave-equation solvers and with scalable parallelisation routines. The wave-equation solvers are generated automatically using Devito, a domain specific language, and the parallelisation routines are provided through the custom actor-based library Mosaic. Through a series of examples, we show how Stride can handle realistic tomographic problems, in 2D and 3D, providing intuitive and flexible interfaces that scale from a local multi-processing environment to a multi-node high-performance cluster.