Deep learning-based MRI denoising enhances the reliability of whole-brain volumetric analysis
Won Beom Jung, Chuluunbaatar Otgonbaatar, Jaebin Lee, Jae-Kyun Ryu, Junhyung Kim, Seongkyu Jeon, Ju-ho Kim, Jin Woo Kim, Hackjoon Shim · 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: This study investigates the impact of deep learning-based image reconstruction (DLR) in structural brain MRI volumetric analysis. Goal(s): To demonstrate that DLR effectively reduces noise and enhances image quality with short acquisition time, Approach: Ten healthy subjects were scanned with a 3T MRI system with and without DLR reconstruction. Results: Voxel-based morphometry analysis revealed significant improvements in brain volumetric measurements with DLR compared to conventional methods. These advancements are particularly relevant in regions associated with neurodegenerative diseases. Impact: DLR offers the potential to facilitate earlier detection and monitoring of such conditions, providing clinical value with comparable scan duration.