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

arXiv: https://arxiv.org/abs/2110.03345 · HTML: https://mlsanity.com/paper/2110.03345/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [j-Wave: An open-source differentiable wave simulator](https://mlsanity.com/paper/2207.01499/) (2022)
- [A Skull-Adaptive Framework for AI-Based 3D Transcranial Focused Ultrasound Simulation](https://mlsanity.com/paper/2505.12998/) (2025)
- [Transforming Breast Cancer Diagnosis: Towards Real-Time Ultrasound to Mammogram Conversion for Cost-Effective Diagnosis](https://mlsanity.com/paper/2308.05449/) (2023)

## Abstract (arXiv)

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.