arXiv Computer Vision By Valentin Bacher (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Pak Hei Yeung (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Quantitative Healthcare Analysis), Bernhard Kainz (Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg, Germany, Imperial College London, United Kingdom), Madeleine K. Wyburd (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Department of Computer Science, University of Copenhagen, Denmark), Nicola K. Dinsdale (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Michael Gray (Institute of Biomedical Engineering, University of Oxford, United Kingdom), Ana I. L. Namburete (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom)

Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition

Read the original on arXiv Computer Vision →

RFlash is a physics‑informed post‑processing technique that removes acoustic shadows in ultrasound by decomposing beamformed images into attenuation and scatter‑intensity maps using a differentiable radiance‑field model. It re‑renders images to eliminate depth‑dependent signal loss, effectively simulating a virtual transducer advance. Across thousands of fetal brain, abdominal, and liver scans, RFlash outperforms traditional attenuation correction, reduces prediction error by 40% in fetal brain imaging, and provides shadow‑confidence maps that enhance bone‑shadow segmentation.

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