All clinical MRI scanners require bulky and enclosed RF shielding rooms to prevent external electromagnetic interference (EMI) signals during data acquisition, and quality electronics inside shielding room (i.e., with minimal EMI emission). A deep learning EMI cancellation strategy is presented to model, predict and remove EMI signals from acquired MRI signals, eliminating the need for RF shielding. We demonstrated that this method worked robustly for various EMI sources from both external environments and internal scanner electronics, producing final image SNRs highly comparable to those obtained using a fully enclosed RF shielding cage in 0.055T and 1.5T experiments.
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