C
Carlos Milovic
Researcher at Pontifical Catholic University of Chile
Publications - 25
Citations - 523
Carlos Milovic is an academic researcher from Pontifical Catholic University of Chile. The author has contributed to research in topics: Quantitative susceptibility mapping & Imaging phantom. The author has an hindex of 9, co-authored 21 publications receiving 328 citations. Previous affiliations of Carlos Milovic include University College London.
Papers
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Journal ArticleDOI
Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.
Christian Langkammer,Ferdinand Schweser,Karin Shmueli,Christian Kames,Xu Li,Li Guo,Carlos Milovic,Jinsuh Kim,Hongjiang Wei,Kristian Bredies,Sagar Buch,Yihao Guo,Zhe Liu,Jakob Meineke,Alexander Rauscher,José P. Marques,Berkin Bilgic +16 more
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.
Journal ArticleDOI
Fast nonlinear susceptibility inversion with variational regularization.
TL;DR: A novel algorithm is presented that solves the nonlinear functional while achieving computation speeds comparable to those for a linear formulation.
Journal ArticleDOI
A robust multi-scale approach to quantitative susceptibility mapping.
Julio Acosta-Cabronero,Julio Acosta-Cabronero,Carlos Milovic,Hendrik Mattern,Cristian Tejos,Oliver Speck,Martina F. Callaghan +6 more
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.
Journal ArticleDOI
Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM)
Daniel Polak,Daniel Polak,Daniel Polak,Itthi Chatnuntawech,Jaeyeon Yoon,Siddharth Iyer,Siddharth Iyer,Carlos Milovic,Jongho Lee,Peter Bachert,Peter Bachert,Elfar Adalsteinsson,Kawin Setsompop,Kawin Setsompop,Berkin Bilgic,Berkin Bilgic +15 more
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.
Journal ArticleDOI
QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures.
José P. Marques,Jakob Meineke,Carlos Milovic,Carlos Milovic,Berkin Bilgic,Berkin Bilgic,Kwok-Shing Chan,Renaud Hedouin,Renaud Hedouin,Wietske van der Zwaag,Christian Langkammer,Ferdinand Schweser +11 more
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.