Journal Article

Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI

Rensonnet, Gaëtan, Salvador, Raymond, Pomarol-Clotet, Edith, Girard, Gabriel, Canales-Rodríguez, Erick Jorge, Patino, Jonathan Rafael, Legarreta, Jon Haitz, Daducci, Alessandro, Thiran, Jean-Philippe, Alemán-Gómez, Yasser, Radua, Joaquim, Barakovic, Muhamed, Pizzolato, Marco, Romascano, David

2019

Overview

Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection techniques based on the extended Bayesian information criterion (i.e., best subset selection and the least absolute shrinkage and selection operator), iteratively reweighted l2- and l1-norm approaches to approximate the l0-norm, sparse Bayesian learning, Cauchy deconvolution, and two accelerated Richardson-Lucy algorithms. Results from our exhaustive evaluation show that there is no single optimal method for all different fiber configurations, suggesting that further studies should be conducted to find the optimal way of combining solutions from different methods. We found l0-norm regularization algorithms to resolve more accurately fiber crossings with small inter-fiber angles. However, in voxels with very dominant fibers, algorithms promoting more sparsity are less accurate in detecting smaller fibers. In most cases, the best algorithm to reconstruct fiber crossings with two fibers did not perform optimally in voxels with one or three fibers. Therefore, simplified validation systems as employed in a number of previous studies, where only two fibers with similar volume fractions were tested, should be avoided as they provide incomplete information. Future studies proposing new reconstruction methods based on high angular resolution diffusion imaging data should validate their results by considering, at least, voxels with one, two, and three fibers, as well as voxels with dominant fibers and different diffusion anisotropies. •There is no single optimal SD method for all the different fiber configurations.•Sparse algorithms to resolve fiber crossings with small inter-fiber angles were found.•Algorithms promoting more sparsity are less accurate in detecting smaller fibers.•Future studies should validate their results by considering many fiber configurations.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Algorithms / Bayes Theorem / Bayesian analysis / Brain - anatomy & histology / Comparative studies / Diffusion Magnetic Resonance Imaging - methods / Diffusion MRI / Diffusion Tensor Imaging - methods / Fibers / Humans / Image Processing, Computer-Assisted - methods / LASSO / Magnetic resonance imaging / Methods / Neuroimaging / NMR / Noise / Non-negative least squares / Nuclear magnetic resonance / Optimization algorithms / Population / Reproducibility of Results / Signal Processing, Computer-Assisted / Sparse regression / Sparsity / Spherical deconvolution / Substantia alba / Surveys and Questionnaires / White Matter - anatomy & histology