SandwichNM Denoising with Deep Learning-Based Approach
Motivation: SandwichNM is an advanced neuromelanin sensitive MRI method, but it use the same sequence twice and averages them to increase signal-to-noise ratio (SNR) requiring long scan time. Goal(s): The objective is to preserve the SNR while reducing two scans into a single scan. Approach: We proposed deep learning-based denoising method for sandwichNM image to reduce the number of scans. Results: The proposed model achieved an increased PSNR and SSIM with utilizing single scan, which has reduced the scan time to half of the previous one. Impact: SandwichNM is an advanced neuromelanin sensitive MRI technique but requires averaging two scans due to SNR issue. The proposed method enabled higher SNR from single scan which can be useful for scanning patients with Parkinson's disease with involuntary movements.
Authors
- Jae-Woo Choi (ORCID: https://orcid.org/0000-0003-3666-9198)
- Sungbum Park
- Yoo‐Mi Kim (ORCID: https://orcid.org/0000-0002-8440-5069)
- Sungho Jeong (ORCID: https://orcid.org/0000-0001-9374-118X)
- Chankyu Park (ORCID: https://orcid.org/0000-0003-4855-2210)
- Beomseok Sohn (ORCID: https://orcid.org/0000-0002-6765-8056)
- Soo-Yeon Ji (ORCID: https://orcid.org/0000-0003-2677-2773)
- Soohwa Song (ORCID: https://orcid.org/0000-0002-4723-5603)
- Chul‐Ho Sohn (ORCID: https://orcid.org/0000-0003-0039-5746)
- Jongho Lee (ORCID: https://orcid.org/0000-0003-0398-4220)
- P Lee
- Jongsam Baik
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/10.58530/2024/4346
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00