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Abstract #0330

Subspace Implicit Neural Representations for Real-Time Cardiac MR Imaging

Wenqi Huang1, Veronika Spieker1,2, Siying Xu3, Gastao Cruz4, Claudia Prieto5,6, Kerstin Hammernik1, Thomas Küstner3, and Daniel Rueckert1,7
1Technical University of Munich, Munich, Germany, 2Helmholtz Center Munich, Munich, Germany, 3University of Tübingen, Tübingen, Germany, 4University of Michigan, Michigan, MI, United States, 5Pontificia Universidad Católica de Chile, Santiago, Chile, 6King’s College London, London, United Kingdom, 7Imperial College London, London, United Kingdom

Synopsis

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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Keywords