Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging

Wenqi Huang; Veronika Spieker; Siying Xu; Gastao Cruz; Claudia Prieto; Julia Schnabel; Kerstin Hammernik; Thomas Kuestner; Daniel Rueckert

arXiv · 2412.12742

Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging

Wenqi Huang·Veronika Spieker·Siying Xu·Gastao Cruz·Claudia Prieto·Julia Schnabel·Kerstin Hammernik·Thomas Kuestner·Daniel Rueckert

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Abstract

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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eess.IV cs.AI cs.LG

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Wenqi Huang, Veronika Spieker, Siying Xu, Gastao Cruz, Claudia Prieto, Julia Schnabel, Kerstin Hammernik, Thomas Kuestner, Daniel Rueckert. 2024-12-17. Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging. https://arxiv.org/abs/2412.12742

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