Abstract #3981

HANDI: Hessian Accelerated Nonlinear Dipole Inversion for rapid QSM

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

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

We propose an extension of nonlinear dipole inversion (NDI), a first order method for solving the nonlinear formulation of the dipole inversion in quantitative susceptibility mapping (QSM), by using a second order method to increase the convergence rate of the minimization. The proposed method, Hessian Accelerated Nonlinear Dipole Inversion (HANDI), is shown to require fewer iterations than NDI, resulting in reconstruction times of a few seconds, more than 10x faster than NDI, without sacrificing accuracy. We further propose a learned proximal Newton method (HANDINet) and show that it outperforms learned variational networks based on NDI and standard dipole deconvolution minimizations.

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