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.
@article{arxiv.2412.12742,
title = {Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging},
author = {Wenqi Huang and Veronika Spieker and Siying Xu and Gastao Cruz and Claudia Prieto and Julia Schnabel and Kerstin Hammernik and Thomas Kuestner and Daniel Rueckert},
journal= {arXiv preprint arXiv:2412.12742},
year = {2024}
}