Deep learning-based MRI denoising enhances the reliability of whole-brain volumetric analysis
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.
Authors
- Jae‐Kyun Ryu
- Chuluunbaatar Otgonbaatar (ORCID: https://orcid.org/0000-0002-8533-6165)
- Jaebin Lee (ORCID: https://orcid.org/0000-0002-0060-5689)
- Hackjoon Shim (ORCID: https://orcid.org/0000-0002-6731-0084)
- Won Beom Jung (ORCID: https://orcid.org/0000-0001-5427-5964)
- Jin Woo Kim (ORCID: https://orcid.org/0000-0003-3155-0569)
- Junhyung Kim (ORCID: https://orcid.org/0000-0001-7123-9535)
- Sanghoon Jeon (ORCID: https://orcid.org/0000-0001-5636-7555)
- Ju-ho Kim (ORCID: https://orcid.org/0009-0001-4055-4564)
Publication Details
- Journal
- 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
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/1972
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00