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Abstract #4182

Pseudo COSMOS: Efficient deep learning quantitative susceptibility mapping based on local field transformation

De-Rong Huang1, Jhih-Shan Cheng1, Hsiao-Wen Chung2, and Ming-Long Wu1,3
1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, 2Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 3Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

Synopsis

Keywords: Susceptibility, Machine Learning/Artificial IntelligenceA deep learning framework termed pCOSMOS for quantitative susceptibility mapping (QSM) was proposed, which employed a local-field-to-local-field transformation to generate multiple orientation local field maps from single orientation data, followed by COSMOS reconstruction using physical model. 3T multi-orientation data from 10 healthy subjects (7 for training, 3 for testing) were used to investigate performance. Quantitative results compared with MEDI, SFCR, QSMnet, and LPCNN demonstrated superior performance of pCOSMOS comparable to the amongst best algorithms using single orientation data, while showing drastically reduced training time from tens of hours to 3.2 hours.

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