Software
Multi-Component T2 Relaxometry Methods for Myelin Water Quantification: The Toolbox
Authors: Erick Jorge Canales-Rodríguez, Marco Pizzolato, Gian Franco Piredda, Tom Hilbert, Nicolas Kunz, Caroline Pot, Thomas Yu, Raymond Salvador, Edith Pomarol-Clotet, Tobias Kober, Jean-Philippe Thiran, Alessandro Daducci, 2020.
Link to multicomponent-T2-toolbox
Model-Informed Machine Learning for Multi-component T2 Relaxometry
Authors: Thomas Yu, Erick Jorge Canales-Rodrı́guez, Marco Pizzolato, Gian Franco Piredda , Tom Hilbert, Elda Fischi-Gomez, Matthias Weigel, Muhamed Barakovic, Meritxell Bach Cuadra, Cristina Granziera, Tobias Kober, Jean-Philippe Thiran, 2020.
Model-Informed Machine Learning (MIML) algorithm to estimate T2 spectra and myelin water fraction maps from Multi-echo T2 relaxation data.
Robust Monte-Carlo Diffusion-MRI Simulator
Authors: Jonathan Rafael-Patino, David Romascano, Alonso Ramirez-Manzanares, Erick J Canales-Rodrı́guez, Gabriel Girard, Jean-Philippe Thiran. Front. Neuroinform, 2020
The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for diffusion MRI.
Sparse Wars: A Survey and Comparative Study of Spherical Deconvolution Algorithms for Diffusion MRI.
Authors: Erick J Canales-Rodríguez, Jon Haitz Legarreta, Marco Pizzolato, Gaëtan Rensonnet, Gabriel Girard, Jonathan Rafael Patiño, Muhamed Barakovic, David Romascano, Yasser Alemán-Gomez, Joaquim Radua, Edith Pomarol-Clotet, Raymond Salvador, Jean-Philippe Thiran, Alessandro Daducci. Neuroimage, 2019
MRIPredict
Authors: Salvador R, Radua J, Canales-Rodríguez EJ, Solanes A, Sarró S, Goikolea JM, Valiente A, Monté GC, Natividad MDC, Guerrero-Pedraza A, Moro N, Fernández-Corcuera P, Amann BL, Maristany T, Vieta E, McKenna PJ, Pomarol-Clotet E., 2017
A free tool for SPM, FSL and R, to easily carry out voxelwise predictions based on VBM images
Parallel HARDI on the GPU
Authors: Javier Garcia Blas, Manuel F Dolz, J Daniel Garcia, Jesus Carretero, Alessandro Daducci, Yasser Aleman and Erick Jorge Canales-Rodríguez, 2017
This is a collection of routines for the fast intravoxel reconstruction of HARDI data on the GPU.
Non redundant connectivity (NRC) maps
Authors: R. Salvador, R. Landín-Romero, M. Anguera, E. J. Canales-Rodríguez, J. Radua, A. Guerrero-Pedraza, S. Sarró, T. Maristany, P.J. McKenna, E. Pomarol-Clotet, 2016
Non-Redundant Connectivity (NRC) maps are summary maps of functional connectivity levels in the brain, estimated from functional Magnetic Resonance (fMRI) series. They are similar to weighted Global Brain Connectivity maps, which are based on averages of correlations with each voxel. However, NRC is based on estimates of multiple correlations instead of bivariate correlations. To avoid dimensionality problems it applies the method of Supervised Principal Components.
Accelerated Microstructure Imaging via Convex Optimization (AMICO)
Authors: Alessandro Daducci, Erick J. Canales-Rodríguez, Hui Zhang, Tim B. Dyrby, Daniel C. Alexander, Jean-Philippe Thiran, 2014
Implementation of the linear framework for Accelerated Microstructure Imaging (i.e., ActiveAx and NODDI models) via Convex Optimization. The code contains a series of demos to show how to use the AMICO framework.
High Angular Resolution Diffusion Imaging (HARDI) Tools
Authors: Erick J. Canales-Rodríguez, Lester Melie-García, Yasser Iturria-Medina, Yasser Alemán-Gómez, 2013
This is a collection of MATLAB routines for the analysis of High Angular Resolution Diffusion Imaging (HARDI) data. It is a subset of the code internally used in our laboratory.
Wavelet-based morphometry (WBM)
Authors: Erick J. Canales-Rodríguez, Joaquim Radua, Edith Pomarol-Clotet, Salvador Sarró, Yasser Alemán-Gómez , Yasser Iturria-Medina and Raymond Salvador, 2012
Statistical analyses in voxel-based morphometry are usually based on either Gaussian random fields or in cluster-based statistics. Wavelet-based morphometry (WBM) is a different methodology consisting in conducting the statistical analysis (i.e., univariate tests) in the wavelet domain.
A well-known property of wavelets is that they are effective representing piecewise smooth images with few non-zero coefficients. Because of in the wavelet domain a small fraction of coefficients characterize the signal components, these coefficients can be identified and thus selected for the subsequent statistical analysis. This strategy allows reducing the number of hypothesis to be tested, thereby alleviating the multiple comparisons problem.