DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging

• URL: http://arxiv.org/abs/2508.06768v1
• Date: Sat, 09 Aug 2025 01:04:11 GMT
• Title: DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging
• Authors: Noe Bertramo, Gabriel Duguey, Vivek Gopalakrishnan,
• Abstract summary: Intraoperative ultrasound imaging provides real-time guidance during numerous surgical procedures.<n>But its interpretation is complicated by noise, volumetric artifacts, and poor alignment with high-resolution preoperative MRI/CT scans.<n>We present DiffUS, a physics-based reflection that synthesizes realistic B-mode images from volumetric imaging.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intraoperative ultrasound imaging provides real-time guidance during numerous surgical procedures, but its interpretation is complicated by noise, artifacts, and poor alignment with high-resolution preoperative MRI/CT scans. To bridge the gap between reoperative planning and intraoperative guidance, we present DiffUS, a physics-based, differentiable ultrasound renderer that synthesizes realistic B-mode images from volumetric imaging. DiffUS first converts MRI 3D scans into acoustic impedance volumes using a machine learning approach. Next, we simulate ultrasound beam propagation using ray tracing with coupled reflection-transmission equations. DiffUS formulates wave propagation as a sparse linear system that captures multiple internal reflections. Finally, we reconstruct B-mode images via depth-resolved echo extraction across fan-shaped acquisition geometry, incorporating realistic artifacts including speckle noise and depth-dependent degradation. DiffUS is entirely implemented as differentiable tensor operations in PyTorch, enabling gradient-based optimization for downstream applications such as slice-to-volume registration and volumetric reconstruction. Evaluation on the ReMIND dataset demonstrates DiffUS's ability to generate anatomically accurate ultrasound images from brain MRI data.

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