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.

翻译：传统的心脏电影磁共振成像方法依赖于回顾性门控，这限制了时间分辨率以及捕捉连续心脏动态的能力，特别是在存在心律失常和搏动间变异性的患者中。为了应对这些挑战，我们提出了一种基于子空间隐式神经表示的重建框架，用于对连续采样的径向数据进行实时心脏电影磁共振成像。该方法利用心脏电影磁共振成像的低秩特性，采用两个多层感知器来学习空间和时间子空间基。网络以低分辨率重建结果进行初始化，并通过使用特定于（k空间）辐条（spoke）的损失函数进行微调，以恢复空间细节和时间保真度。我们的方法在训练过程中直接利用连续采样的径向k空间辐条，从而无需进行数据分箱和非均匀快速傅里叶变换。与传统的分箱方法相比，在10倍和20倍加速率下，该方法实现了更优的空间和时间图像质量，展现了其对动态心脏事件进行高分辨率成像并增强诊断能力的潜力。