利用仅幅度测量与复值测量结合学习先验实现改进的动态MRI重建

中文摘要 AI 辅助

欠采样k空间数据的MRI重建方法自然会利用复值测量。并行的稀疏相位恢复研究表明，仅幅度测量可为信号恢复提供互补信息，但由于缺乏无需额外扫描时间即可获取有效幅度测量的实际场景，其在MRI重建中的应用仍未得到充分探索。本研究探讨辅助k空间幅度信息在加速稳态动态MRI重建中的应用，并验证了k空间幅度在不同时间帧间具有强一致性。基于该观察，我们提出$\boldsymbol{\text{C}}+\text{Mag}$，一种幅度感知的物理驱动深度学习重建方法。该方法采用基于ADMM的展开框架，结合新颖的幅度感知数据保真度公式，引入二次平滑优化与基于动量的更新，以解决幅度约束的不可微性与非凸性问题。在回顾性欠采样电影MRI、相位对比流动MRI数据集及前瞻性欠采样实时电影MRI采集数据上开展的实验表明，与传统PD-DL方法相比，所提方法可实现更好的伪影抑制、更清晰的解剖结构恢复及更优的相位信息保留，这一结果还得到了盲法专家读者评估的支持。

英文摘要

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.

发表机构

• University of Minnesota（明尼苏达大学）
• Center for Magnetic Resonance Research（磁共振研究中心）

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