Abstract #1125

Deep Learning Based Mask Generation Tools for QSM

Gawon Lee¹, Ji Wan Son¹, Ken SaKaie², Woojin Jung³, and Se-hong Oh^1,2

¹Division of Biomedical engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Korea, Republic of, ²Imaging institute, Cleveland Clinic Foundation, Cleveland, OH, United States, ³AIRS Medical, Seoul, Korea, Republic of

Synopsis

Even subtle differences in masks can generate systematic but avoidable errors in QSM calculations. We believe these errors propagate through the calculation of the background phase. In this work, we assessed the effect of the mask on the QSM, selected optimal mask generation method and Deep Learning-based efficient mask generation method for in-vivo has been presented. This study represents the first step towards a fully-automated and optimal workflow for QSM calculation.

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