arXiv cs.AI· Mahdi Saberi, Ya\c{s}ar Utku Al\c{c}alar, Merve G\"{u}lle, Chetan Shenoy, Mehmet Ak\c{c}akaya·· 12 小时前AI 评分27
利用纯幅值与复数测量提升学习先验的动态 MRI 重建
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
AI 导读
研究提出 C+Mag，一种幅值信息驱动的物理驱动深度学习重建方法，将辅助 k 空间幅值信息用于加速稳态动态 MRI 重建。该方法采用基于 ADMM 的展开框架和幅值感知数据保真项，并用二次平滑优化与动量更新处理幅值约束的不可微与非凸问题。
正文 · 原文
Abstract:MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction remains largely unexplored, due to lack of practical settings where informative magnitude measurements can be obtained without additional scan time. In this work, we investigate the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrate strong consistency of k-space magnitudes across time-frames. Building on this observation, we propose $\mathbb{C}+\text{Mag}$, a magnitude-informed physics-driven deep learning reconstruction method. The proposed method employs an ADMM-based unrolling framework with a novel magnitude-aware data-fidelity formulation, where quadratically smoothed optimization and momentum-based updates are introduced to address the non-differentiability and non-convexity of the magnitude constraints. Experiments on retrospectively undersampled cine MRI and phase-contrast flow MRI datasets, as well as prospectively undersampled real-time cine MRI acquisitions, demonstrate improved artifact suppression, sharper anatomical recovery, and better preservation of phase information compared to conventional PD-DL methods, which is further supported through blinded expert reader evaluations.
| Comments: | To appear in IEEE TBME |
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Medical Physics (physics.med-ph) |
| Cite as: | arXiv:2608.18036 [eess.IV] |
| (or arXiv:2608.18036v2 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18036 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1109/TBME.2026.3743233
DOI(s) linking to related resources |
Submission history
From: Mahdi Saberi [view email]
[v1]
Tue, 18 Aug 2026 17:31:07 UTC (7,067 KB)
[v2]
Tue, 20 Oct 2026 04:24:00 UTC (7,433 KB)
来源：arXiv cs.AI · arxiv.org