S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Electrical and Computer Engineering (전기·정보공학부) Theses (Master's Degree_전기·정보공학부)
An Improved SMWI Processing of Substantia Nigra Using Accurate Phase Combination and Deep Neural Network Based QSM : 정확한 위상 합성과 심층 신경망 기반 QSM을 이용한 흑질의 SMWI 영상 개선
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- 공과대학 전기·정보공학부
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2018. 8. 이종호.
- Visibility of nigrosome 1, a subregion of substantia nigra is used as an MR imaging biomarker of Parkinsons disease. In this work, we introduced two algorithms for SMWI imaging of substantia nigra. First, we suggested Multi-Channel Phase Combination using Multi-Echo (MCPC-ME), a strategy to calculate and correct phase offsets in multi-echo GRE data. MCPC-ME provided a more accurate estimation of voxel-wise phase offsets particularly in low SNR regions by utilizing phase information from all echoes. Second, we applied QSMnet, a deep neural network for QSM reconstruction, to produce QSM image used in SMWI processing. QSM of nigrosome 1 was reconstructed to have comparable SMWI contrast with 5.4 times faster reconstruction speed compared to the conventional QSM reconstruction algorithm.
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