LAUSR

At present, MRI scans are typically performed inside fully enclosed RF shielding rooms, posing stringent installation requirement and patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no or incomplete RF shielding. In this study, a method of active sensing and deep learning EMI prediction is presented to model, predict and remove EMI signal components from acquired MRI signals. Specifically, during each MRI scan, separate EMI sensing coils placed in various locations are utilized to simultaneously sample external and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). A CNN model is trained using the EMI characterization data to relate EMI signals detected by EMI sensing coils to EMI signals in MRI receive coil. This model is then used to retrospectively predict and remove EMI signal components detected by MRI receive coil during the MRI signal acquisition window. This strategy was implemented on a low-cost ultra-low-field 0.055 T permanent magnet MRI scanner without RF shielding. It produced final image signal-to-noise ratios that were comparable to those obtained using a fully enclosed RF shielding cage, and outperformed existing analytical EMI elimination methods (i.e., transfer function and EDITER methods). A preliminary experiment also demonstrated its applicability on a 1.5 T superconducting magnet MRI scanner with incomplete RF shielding. Altogether, the results demonstrated that the proposed method was highly effective in predicting and removing various EMI signals from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (63.9 MHz). The proposed strategy enables shielding-free MRI. The concept is relatively simple and potentially applicable to other RF signal detection scenarios in presence of external or/and internal EMI.