Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
This work addresses the challenge of real-time cardiac imaging for patients with arrhythmias, offering a potential improvement in diagnostic capability, though it appears incremental as it builds on existing neural representation techniques.
The paper tackled the problem of limited temporal resolution and inability to capture continuous cardiac dynamics in conventional cardiac cine MRI, particularly for patients with arrhythmias, by proposing a reconstruction framework using subspace implicit neural representations for real-time imaging. The result was superior spatial and temporal image quality compared to conventional methods at acceleration rates of 10 and 20.
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.