Hankel Subspace Self‑Supervised Reconstruction (HSSRecon) is a scan‑specific framework for parallel MRI that partitions data by acquisition units before Hankel lifting and normalizes repeated Hankel copies. Instead of predicting missing k‑space directly, the network learns a compact complex‑valued Hankel subspace operator, and reconstruction is performed with a conjugate‑gradient solver enforcing hard data consistency. The method provides theoretical guarantees on uniqueness and error bounds, and demonstrates competitive performance on fastMRI brain data across multiple contrasts and sampling masks.
By Mingyu Hu, Siquan Zhu, Xijun Zhong, Qiegen Liu
arXiv:2606. 00100v1 Announce Type: cross Abstract: Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring fully sampled data for training.
By Tongxi Song, Ziyu Li, Zihan Li, Wen Zhong, Congyu Liao, Yang Yang, Hua Guo, Wenchuan Wu, Qiyuan Tian
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
The paper introduces FLEX‑MRI, a method that accelerates MRI reconstruction by partitioning the recovery process into a few finite intervals defined by progressive k‑space support changes. Instead of learning fine‑grained local dynamics, FLEX‑MRI directly learns the reconstruction change across each interval and employs stochastic exploration during inference to sample alternative transition paths. Experiments on multi‑coil MRI datasets demonstrate that FLEX‑MRI outperforms competing methods while keeping computational efficiency comparable to few‑step baselines.
By Muhammad Usama Mirza, Esin \"Ozt\"urk I\c{s}{\i}k, Tolga \c{C}ukur
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