Deep learning for emulating bio-electromagnetic interactions in MRI - Lim, Shannon Shi Mei (Nanyang Technological University, 2021)
Magnetic resonance imaging (MRI) equipment requires properly designed coils to obtain high-resolution images, which should also obey safety regulations to avoid thermal burn injuries of the human body during MRI scanning. However, generated heat and induced fields in MRI vary from patient to patient due to the difference in tissue conductivities. Therefore, to ensure designed coils are safe for everybody, this project aims to quantify the uncertainty from human head tissue conductivities during MRI. During uncertainty quantification (UQ), emulation of MRI is indispensable and will be often required enormous times. As emulators for bio-electromagnetic interactions in MRI, two deep neural networks are implemented in this final year project achieving mean absolute percentage error (MAPE) of 4.46% and 2.90%, respectively. Compared with one currently available MRI simulator, MARIE, the time consumed for one simulation reduces from 10 minutes to less than 1 second, which will significantly improve the efficiency of UQ. DNNs developed guarantee quicker analysis of electric fields based on tissue conductivities while including uncertainties in EM analysis allowing designed coils to be safe for everybody.