Fully Differentiable Ultrasound Simulation Utilizing Ray-Tracing

TL;DR

This paper introduces a fully differentiable ultrasound simulation framework based on ray tracing, enabling efficient gradient-based optimization for ultrasound imaging tasks.

Contribution

It presents a novel end-to-end differentiable ultrasound simulation method combining ray tracing with image formation, supporting inverse problems and optimization.

Findings

Successfully reproduces geometric and complex anatomical features in simulations.

Recovers known parameters in simulated-reference inverse problems.

Aligns simulated and experimental images effectively in real-world settings.

Abstract

Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with respect to meaningful material and system parameters. While full-wave solvers offer high fidelity, they are often too expensive for iterative optimization, and existing ray-based methods have mostly been limited to forward simulation. In this work, we present a fully differentiable end-to-end ultrasound simulation framework based on full-path Monte Carlo ray tracing. Building on UltraRay, the method propagates gradients from image-space losses back through acoustic transport, beamforming, and post-processing, enabling gradient-based optimization over scene and acquisition parameters. The framework combines differentiable ray transport in Mitsuba 3/Dr.Jit with a custom differentiable bridge through the ultrasound image-formation pipeline. Forward examples reproduce expected geometric image features and capture more complex anatomical structures. In inverse problems, the method recovers known parameters in a simulated-reference setting and identifies effective parameters that improve agreement between simulated and experimental B-mode images in a simulation-to-real setting. Finite-difference comparisons further support the consistency of the computed gradients. Overall, this work provides a practical foundation for differentiable, physics-based ultrasound simulation and optimization.