Zobrazeno 1 - 10
of 875
pro vyhledávání: '"Ciuciu A"'
We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of
Externí odkaz:
http://arxiv.org/abs/2404.08282
The brain criticality framework has largely considered brain dynamics to be monofractal even though experimental evidence suggests that the brain exhibits significant multifractality. To understand how multifractality may emerge in critical-like syst
Externí odkaz:
http://arxiv.org/abs/2312.03219
Autor:
Ciuciu, Alexandra1 (AUTHOR), Mulholland, Christopher1 (AUTHOR), Bozzi, Michael A.1 (AUTHOR), Frymoyer, Chris C.1 (AUTHOR), Cavinatto, Leonardo2 (AUTHOR), Yaron, David3 (AUTHOR), Harwood, Marc I.1,3 (AUTHOR), Close, Jeremy D.1 (AUTHOR), Mehallo, Christopher J.3 (AUTHOR), Tomlinson, Ryan E.1 (AUTHOR) ryan.tomlinson@jefferson.edu, Schwingel, Paulo (AUTHOR)
Publikováno v:
Advances in Orthopedics. 10/12/2024, Vol. 2024, p1-9. 9p.
Autor:
R, Chaithya G, Ciuciu, Philippe
We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We presen
Externí odkaz:
http://arxiv.org/abs/2201.11356
Autor:
Amor, Zaineb1 (AUTHOR), Ciuciu, Philippe1,2 (AUTHOR), G. R., Chaithya1,2 (AUTHOR), Daval-Frérot, Guillaume1,2,3 (AUTHOR), Mauconduit, Franck1 (AUTHOR), Thirion, Bertrand1,2 (AUTHOR), Vignaud, Alexandre1 (AUTHOR)
Publikováno v:
PLoS ONE. 5/13/2024, Vol. 19 Issue 5, p1-30. 30p.
Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image reconstruction from the undersampled k-space data. Recently, deep learn
Externí odkaz:
http://arxiv.org/abs/2110.12691
Autor:
R, Chaithya G, Weiss, Pierre, Daval-Frérot, Guillaume, Massire, Aurélien, Vignaud, Alexandre, Ciuciu, Philippe
The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using compressed sensing. It has then been extended to address 3D imaging usi
Externí odkaz:
http://arxiv.org/abs/2108.02991
We perform a qualitative analysis of performance of XPDNet, a state-of-the-art deep learning approach for MRI reconstruction, compared to GRAPPA, a classical approach. We do this in multiple settings, in particular testing the robustness of the XPDNe
Externí odkaz:
http://arxiv.org/abs/2106.00753
Autor:
Ramzi, Zaccharie, Mannel, Florian, Bai, Shaojie, Starck, Jean-Luc, Ciuciu, Philippe, Moreau, Thomas
In recent years, implicit deep learning has emerged as a method to increase the effective depth of deep neural networks. While their training is memory-efficient, they are still significantly slower to train than their explicit counterparts. In Deep
Externí odkaz:
http://arxiv.org/abs/2106.00553
The SPARKLING algorithm was originally developed for accelerated 2D magnetic resonance imaging (MRI) in the compressed sensing (CS) context. It yields non-Cartesian sampling trajectories that jointly fulfill a target sampling density while each indiv
Externí odkaz:
http://arxiv.org/abs/2103.03559