Keywords: Image Reconstruction, Image Reconstruction, Neural Implicit Representations, Low-rank, Non-Cartesian Sampling
Motivation: Existing cardiac cine MRI methods achieve limited temporal resolution due to retrospective gating, which limits accurate capture of continuous cardiac dynamics.
Goal(s): To develop a reconstruction framework for real-time cardiac cine MRI using subspace implicit neural representations, aiming to improve both spatial and temporal resolution.
Approach: We use two MLPs to learn the spatial and temporal subspace bases, leveraging the low-rank properties of cardiac cine MRI. Networks are initialized with low-resolution GRASP reconstruction and fine-tuned using spoke-specific losses to recover details.
Results: Our real-time approach achieves superior quality compared to NUFFT and GRASP reconstruction, which were evaluated across several temporal resolutions.
Impact: This method provides a novel reconstruction approach for real-time cardiac MRI with continuous radial acquisition, and will potentially reducing scan times and improving diagnostic capabilities, especially for imaging arrhythmias and characterizing beat-to-beat dynamics compared to conventional approaches.
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