Rotating-view super-resolution (ROVER)-MRI reconstruction using tailored Implicit Neural Network

Motivation: Direct acquisition of high resolution data is time-consuming and degrades SNR. Super-resolution reconstruction (SRR) is widely used to address these challenges. However, existing reconstruction tools use algorithms that are sensitive to noise and motion. Goal(s): Our study aims to develop a training-free deep learning-based SRR method that integrates multi-view thick-slice data to reconstruct images with enhanced spatial resolution and high SNR. Approach: We used an implicit neural representation (INR) network, leveraging data from scans at various views, to achieve high isotropic SRR. Results: Our technique exhibited 30% better SNR and significant motion-robustness compared to existing techniques. Impact: Implicit neural representations allow continuous functional representation of MRI images thereby being a natural candidate for performing SRR in low SNR regimes. Our study validates the feasibility of employing INRs to reduce scan time, motion artifacts, and achieve high-quality SRR.