The paper introduces AWR-Net, a two‑stage framework that decouples anatomy and appearance to synthesize realistic 3D fetal brain ultrasound volumes from anatomical label maps. The first stage uses wavelet diffusion to generate volumes from atlas pairs in the wavelet domain, while the second stage applies residual refinement in the image domain to adapt to real ultrasound appearance. Experiments on real fetal brain ultrasound data show that AWR‑Net outperforms existing synthesis methods, improving metrics such as normalized cross‑correlation and Fréchet Inception Distance, and also enhances downstream segmentation, especially for severe abnormal cases.
By Yuhuan Lu, Sergio Valencia, Yuanji Zhang, Yuhao Huang, Camilo Jaimes, P. Ellen Grant, Davood Karimi
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.
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)
This study introduces a self‑supervised, physics‑guided deep‑learning framework that converts standard clinical T1‑, T2‑, and FLAIR MRIs into quantitative T1, T2, and proton‑density maps. Trained on 4,121 scan sessions from four different 3 T scanners over six years, the method produces maps whose white‑ and gray‑matter values fall within literature ranges and shows minimal variation across scanner hardware and acquisition protocols (coefficients of variation ≤ 1.1 %). Voxel‑wise reproducibility is high, with Pearson and concordance correlation coefficients above 0.82 for T1 and T2 and mean relative differences below 6 % for T2.
By Jelmer van Lune, Stefano Mandija, Oscar van der Heide, Matteo Maspero, Martin B. Schilder, Jan Willem Dankbaar, Cornelis A. T. van den Berg, Alessandro Sbrizzi
arXiv:2606. 19651v1 Announce Type: new Abstract: Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing.
By Max Van Puyvelde, Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert
arXiv:2609.14312v1 Announce Type: new
Abstract: Multi-shell diffusion MRI (dMRI) unlocks more expressive microstructural modeling than single-shell scans, yet its longer acquisition time hinders depl...
By Binghua Li, Christina Andica, Tong Liang, Ziqing Chang, Chao Li, Wataru Uchida, Kaito Takabayashi, Qibin Zhao, Toshihisa Tanaka, Zhe Sun, Shigeki Aoki
Three-dimensional multi-echo MRI provides rich anatomical and quantitative information, but repeated volumetric encoding prolongs acquisition and motivates k-space undersampling. Reconstructing unders...