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Deconvolution algorithms for diffusion MRI

Implementation of the algorithms described here:

Sparse Wars: A Survey and Comparative Study of Spherical Deconvolution Algorithms for Diffusion MRI. 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 (https://www.sciencedirect.com/science/article/abs/pii/S1053811918307699?via%3Dihub)

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. Erick J Canales-Rodríguez, Alessandro Daducci, Stamatios N Sotiropoulos, Emmanuel Caruyer, Santiago Aja-Fernández, Joaquim Radua, Yasser Iturria-Medina, Lester Melie-García, Yasser Alemán-Gómez, Jean-Philippe Thiran, Salvador Sarró, Edith Pomarol-Clotet, Raymond Salvador. PLoS ONE, 2015, 10(10): e0138910. (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138910)

The current implementation is written in Matlab.

List of included methods:

  1. Best-subset selection based on the extended Bayesian information criterion (NNLS-BSS-EBIC)
  2. LASSO based on the EBIC (LASSO-EBIC)
  3. Non-negative iterative reweighted l1 minimization (IRL1)
  4. Sparse Bayesian Learning (SBL)
  5. Robust and unbiased model-based spherical deconvolution (RUMBA-TV)

Install dependencies: