用隐式神经表示实现心肌动态实时成像，提升分辨率与连续捕捉能力。
Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
- 通过双网络学习时空低秩子空间基，融合连续径向采样数据
- 加速10倍和20倍下，时空质量优于传统分组方法
- 适合心律不齐患者，支持高分辨率动态心脏成像
传统心脏电影磁共振依赖回顾性门控，限制了时间分辨率，难以捕捉心律不齐患者的连续心脏动态。为此，我们提出一种基于子空间隐式神经表示的实时心脏电影MRI重建框架，适用于连续采样的径向k空间数据。该方法使用两个多层感知机学习空间与时间子空间基，利用心脏电影MRI的低秩特性。以低分辨率重建初始化网络，通过特定于数据条（spoke）的损失函数进行微调，恢复空间细节与时间保真度。训练直接使用连续采样的径向k空间数据，无需分组与非均匀FFT。在加速率10和20下，相比传统分组方法显著提升了时空图像质量，展现出对动态心脏事件高分辨率成像的潜力，可增强诊断能力。
原文摘要 · Abstract (English)
Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patients with arrhythmias and beat-to-beat variations. To address these challenges, we propose a reconstruction framework based on subspace implicit neural representations for real-time cardiac cine MRI of continuously sampled radial data. This approach employs two multilayer perceptrons to learn spatial and temporal subspace bases, leveraging the low-rank properties of cardiac cine MRI. Initialized with low-resolution reconstructions, the networks are fine-tuned using spoke-specific loss functions to recover spatial details and temporal fidelity. Our method directly utilizes the continuously sampled radial k-space spokes during training, thereby eliminating the need for binning and non-uniform FFT. This approach achieves superior spatial and temporal image quality compared to conventional binned methods at the acceleration rate of 10 and 20, demonstrating potential for high-resolution imaging of dynamic cardiac events and enhancing diagnostic capability.
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