Carlos Milovic

TL;DR: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.

TL;DR: A novel algorithm is presented that solves the nonlinear functional while achieving computation speeds comparable to those for a linear formulation.

TL;DR: A new Bayesian QSM algorithm, named Multi‐Scale Dipole Inversion (MSDI), is presented, which builds on the nonlinear Morphology‐Enabled Dipole inversion (nMEDI) framework, incorporating three additional features: improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, improved error control through dynamic phase‐reliability compensation across spatial scales, and scalewise use of the morphological prior.

TL;DR: This work synergistically combine this physics‐model with a Variational Network to leverage the power of deep learning in the VaNDI algorithm and adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning.

TL;DR: In this paper, a digital whole-head tissue property phantom was created by segmenting and post-processing high-resolution (0.64 mm isotropic) multiparametric MRI data acquired at 7 T from a healthy volunteer.