MRF-SEG: Accelerated Brain MRI Acquisition and Segmentation
Ashwin Srinivasan Kumar, Zihan Zhou, Quan Chen, Xiaozhi Cao, Benjamin Billot, Bruce Fischl, Akshay Chaudhari, Kawin Setsompop · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Motivation: Despite the availability of rapid high-resolution MRF sequences that can be used to synthesize MPRAGE, acquiring MPRAGE scans remains necessary for accurate downstream segmentation. Goal(s): The aim is to perform accurate-segmentation directly on MRF time-resolved data, eliminating the need for a lengthy MPRAGE scan, resulting in significant time savings, and providing quantitative tissue parameter maps. Approach: We used deep learning to directly segment MRF time-resolved data and generate multi-tissue brain segmentation maps. Results: Our findings indicate that deep learning segmentation methods trained directly on MRF data, both quantitatively and qualitatively perform better than segmentation on synthesized MPRAGE. Impact: Applying deep learning directly on MRF data improves MRF segmentation compared to synthesizing MPRAGE and performing segmentation on it. This strengthens the validation of MRF and enhances its clinical potential by rapidly acquiring and segmenting brain images.