The paper introduces SIMS-MRI, a self‑supervised framework that performs single‑subject multi‑view MRI super‑resolution using implicit neural representations. It processes anisotropic multi‑view scans without pre‑ or post‑processing, employing a multi‑resolution hash‑encoded representation and learned inter‑view alignment to produce isotropic reconstructions. The method is validated on simulated brain and clinical prostate MRI datasets, and the code will be publicly released.
By Heejong Kim, Abhishek Thanki, Roel van Herten, Daniel Margolis, Mert R Sabuncu
arXiv:2609.24468v2 Announce Type: replace
Abstract: Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long ac...
By Jingran Xu, Dong Liang, Hairong Zheng, Yuanyuan Liu, Yanjie Zhu
The paper introduces a transfer‑learning framework that pre‑trains an implicit neural representation (INR) on a high‑resolution diffusion MRI template and then adapts it to individual subjects through registration and fine‑tuning. This approach enables native single‑subject super‑resolution, achieving a 4× through‑plane up‑sampling from 5 mm to 1.25 mm on Human Connectome Project data. Compared to a recent baseline, the method reduces NRMSE by 36–49 % and increases FSIM by 24–43 %, while training 6× faster and outperforming other INR‑based techniques on both image quality and domain‑specific metrics.
By Abdulkader Ghandoura, Marsil Zakour, William Consagra, Yogesh Rathi
The paper introduces a 3D residual wavelet diffusion model for super‑resolving ultra low‑field MRI scans. By using a lossless wavelet reparameterisation, residual shifting, and domain randomisation, the method fits whole‑brain data on a single GPU, speeds up sampling, and generalises across scanners. It achieves volumetric accuracy comparable to leading regression approaches while producing per‑voxel uncertainty maps that reveal under‑determined regions and preserves disease‑relevant atrophy in cognitively impaired subjects.
By Rui W. Yeow, Millie Beament, Fred Dick, Raha Razin, Martina Bocchetta, David L. Thomas, Henry F. J. Tregidgo, Daniel C. Alexander, James H. Cole
arXiv:2505.05855v4 Announce Type: replace
Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pa...
By Yinzhe Wu, Hongyu Rui, Fanwen Wang, Jiahao Huang, Zhenxuan Zhang, Haosen Zhang, Zi Wang, Guang Yang
arXiv:2603. 04438v3 Announce Type: replace-cross Abstract: Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction.
By Qingyong Zhu, Yumin Tan, Xiang Gu, Dong Liang
arXiv:2607.11295v2 Announce Type: replace
Abstract: Biomedical imaging data exhibit substantial acquisition variability, where identical biological structures can appear markedly different due to dif...
By Mehmet Yigit Avci, Pedro Borges, Virginia Fernandez, Natalia Glazman, Paul Wright, Mehmet Yigitsoy, Sebastien Ourselin, Jorge Cardoso
Three-dimensional multi-echo MRI provides rich anatomical and quantitative information, but repeated volumetric encoding prolongs acquisition and motivates k-space undersampling. Reconstructing unders...
arXiv:2607. 29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns.
By Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang, Richard Osuala, Noah M\'arquez Varaa, Alejandro Guzman, Grzegorz Skorupko, Sebastian Ibarra Arregui, Lidia Garrucho, Akane Ohashi, Dimitra Ntoula, Eugen Divjak, O\u{g}uz Lafc{\i}, Jan C. Peeken, Julia A. Schnabel, Fredrik Strand, Oliver Diaz, Karim Lekadir
The paper introduces τ+C+Mag, a physics‑driven deep learning method that incorporates auxiliary k‑space magnitude information into accelerated steady‑state dynamic MRI reconstruction. By observing strong consistency of k‑space magnitudes across time‑frames, the authors develop an ADMM‑based unrolling framework with a magnitude‑aware data‑fidelity term, using quadratically smoothed optimization and momentum updates to handle non‑differentiability and non‑convexity. Experiments on retrospectively and prospectively undersampled cine and phase‑contrast flow MRI datasets show improved artifact suppression, sharper anatomical detail, and better phase preservation compared to conventional PD‑DL approaches, as confirmed by blinded expert readers.
By Mahdi Saberi, Ya\c{s}ar Utku Al\c{c}alar, Merve G\"{u}lle, Chetan Shenoy, Mehmet Ak\c{c}akaya
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. 19303v1 Announce Type: new Abstract: High-fidelity simulation of spatiotemporal dynamics is computationally prohibitive, necessitating efficient super-resolution techniques to reconstruct high-resolution data from coarse-grained inputs.
By Xizhuo (Cici), Zhang, Zekai Wang, Fei Liu, Bing Yao