Abstract #4346

SandwichNM Denoising with Deep Learning-Based Approach

Jaewoo Choi¹, Sooyeon Ji¹, Soohwa Song², Sungbum Park², Yoomi Kim², Philhyu Lee³, Chaejung Park⁴, Beomseok Sohn⁵, Chulho Sohn⁶, Jongsam Baik⁷, SeongHo Jeong⁷, and Jongho Lee¹

¹Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, ²Heuron Co.Ltd., Seoul, Korea, Republic of, ³Department of Neurology, Severance Hospital,, Yonsei University College of Medicine, Seoul, Korea, Republic of, ⁴Yongin Severance Hospital, Yonsei University College of Medicine, seoul, Korea, Republic of, ⁵Samsung Medical Center, Seoul, Korea, Republic of, ⁶Seoul National University Hospital, Seoul, Korea, Republic of, ⁷Department of Neurology, Inje University Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Parkinson's Disease, Parkinson's Disease, Denoising

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.

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