Noe Bertramo, Gabriel Duguey, Vivek Gopalakrishnan
{"title":"DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging.","authors":"Noe Bertramo, Gabriel Duguey, Vivek Gopalakrishnan","doi":"10.1007/978-3-032-06329-8_3","DOIUrl":"10.1007/978-3-032-06329-8_3","url":null,"abstract":"<p><p>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 preoperative 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. Our implementation is available at https://github.com/gduguey/DiffUS.</p>","PeriodicalId":524256,"journal":{"name":"Simplifying medical ultrasound. International Workshop on Advances in Simplifying Medical Ultrasound","volume":"16165 ","pages":"23-32"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}