What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Subspace Implicit Neural Representations for Real-Time Cardiac MR Imaging

Key Points

Real-time cardiac MRI achieves superior quality compared to conventional techniques like NUFFT and GRASP.
Spatial and temporal resolutions are significantly improved using subspace implicit neural representations in the approach.
The innovative method utilizes low-rank properties and fine-tuning techniques to enhance MRI capture.
This technique may reduce scan times, offering better characterization of cardiac dynamics and arrhythmias.

Abstract

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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