Low-field strength MRI (0.55T) for stereotactic and functional neurosurgery using deep learning-based reconstruction

Thomas Kinfe1, Miriam Ratliff2, Andreas Stadlbauer2

  • 1From the Mannheim Center for Neuromodulation and Neuroprosthetics (MCNN), Department of Neurosurgery (T.K., M.R., S.B.), Mannheim Center for Translational Neuroscience (MCTN)(T.K.), Mannheim Comprehensive Medical Systems Technology Campus (MCSC)(T.K., S.S.), Department of Radiology (S.S.), Medical Faculty Mannheim, Heidelberg University, Germany; Institute of Medical Radiology (A.S.), University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, St. Pölten, Austria; Department of Neurosurgery (J.M.), SUNY Upstate Medical University, Syracuse, New York; Department of Neurosurgery (S.R.), Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA; Siemens Healthineers (H.-P.F., T.V.), Erlangen, Germany and Department of Radiology (M.U.), University Hospital Erlangen, Erlangen, Germany. thomas.kinfe@medma.uni-heidelberg.de.

Summary

Low-field 0.55 T MRI, enhanced by deep learning, shows feasibility for visualizing critical anatomical landmarks in stereotactic neurosurgery. This approach offers potential advantages over high-field MRI, warranting further clinical investigation.