Abstract #3198

ProxVNET: A proximal gradient descent-based deep learning model for dipole inversion in susceptibility mapping

Christian Kames^1,2, Jonathan Doucette^1,2, and Alexander Rauscher^1,2,3

¹Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada, ²UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada, ³Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada

A deep learning model, ProxVNET, is proposed to solve the ill-posed dipole inversion in susceptibily mapping. ProxVNET is derived from unrolled proximal gradient descent iterations wherein the proximal operator is implemented as a V-Net and is itself learned. ProxVNET is shown to outperform the U-Net-based dipole inversion deep learning model QSMnet when compared to COSMOS reconstructed susceptibility maps.

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