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

• URL: http://arxiv.org/abs/2412.12742v1
• Date: Tue, 17 Dec 2024 10:06:37 GMT
• Title: Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging
• Authors: Wenqi Huang, Veronika Spieker, Siying Xu, Gastao Cruz, Claudia Prieto, Julia Schnabel, Kerstin Hammernik, Thomas Kuestner, Daniel Rueckert,
• Abstract summary: We propose a reconstruction framework based on subspace implicit neural representations for real-time cardiac cine MRI of continuously sampled radial data. Our method directly utilizes the continuously sampled radial k-space spokes during training, thereby eliminating the need for binning and non-uniform FFT.
• Score: 9.373081514803303
• License:
• 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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