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

A 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.