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Abstract #1972

Deep learning-based MRI denoising enhances the reliability of whole-brain volumetric analysis

Won Beom Jung1, Chuluunbaatar Otgonbaatar2, Jaebin Lee3, Jae-Kyun Ryu1, Junhyung Kim3, Seongkyu Jeon3, Juho Kim1,3, Jin Woo Kim4, and Hackjoon Shim1,3
1Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Korea, Republic of, 2College of Medicine, Seoul National University, Seoul, Korea, Republic of, 3Magnetic Resonance Business Unit, Canon Medical Systems Korea, Seoul, Korea, Republic of, 4Department of Radiology, Yonsei University Wonju College of Medicine, Wonju, Korea, Republic of

Synopsis

Keywords: Data Processing, Brain

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.

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Keywords