Abstract #1984

PedQ-NET : Unsupervised Model-Based Joint-Loss Deep learning for Pediatric QSM

Siyun Jung¹, Soohyun Jeon¹, Yoonho Nam², Hyun Gi Kim³, and Dong-Hyun Kim¹

¹Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of, ³Department of Radiology, Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul, Korea, Republic of

Synopsis

In conventional QSM methods, it is difficult to analyze gray matter subcortical in pediatrics, because of small contrast difference between gray matter and white matter. In addition, there are no deep learning approaches that reflect the characteristics of the pediatric brain. In this study, we propose an unsupervised model-based joint-loss deep learning network for pediatric QSM, PedQ-NET. The proposed method achieved better edge preservation on the deep gray matter subcortical areas in pediatric QSM. According to the evaluation of in-vivo pediatric data, QSM generated by PedQ-NET shows enhanced edges in the subcortical areas and clear details with reduced artifacts.

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