Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).

ABSTRACT: High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descen

SUBMITTER: Polak D 

PROVIDER: S-EPMC7528217 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature