Author

Daniel Rueckert

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Review Open access Aug 2026

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review.

PURPOSE To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. METHODS Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) fo...

Amna Gillani, R. von Eisenhart-Rothe, K. Woertler et al. · Knee Surgery, Sports Traumat... · 0 citations

Preprint Aug 2026

NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views

Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudinal directions. Despite these planes being 2D cross-sectional images of the heart, radiologists understand the 3D spatial and continuous temporal nature of the organ bein...

Nil Stolt-Ans'o, Maik Dannecker, Steven Jia et al. · 3 citations

Jul 2026

PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping

PRIME-SVR is presented, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI, which improves reconstruction sharpness, anatomical accuracy, and cross-TE structural consistency by 14%, and accelerates quantitative imaging by reducing the data needed for multi-TE recons...

Busra Bulut, Maik Dannecker, Thomas Sanchez et al. · arXiv.org · 0 citations