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

Canales-Rodríguez EJ; Legarreta JH; Pizzolato M; Rensonnet G; Girard G; Patino JR; Barakovic M; Romascano D; Alemán-Gómez Y; Radua J; Pomarol-Clotet E; Salvador R; Thiran JP; Daducci A

doi:10.1016/j.neuroimage.2018.08.071

Back

Journal article

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

Canales-Rodríguez EJ Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain. Electronic address: erick.canalesrodriguez@epfl.ch.
Legarreta JH Departamento de Física Matemática y Fluidos, Facultad de Ciencias, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
Pizzolato M Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Rensonnet G Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
Girard G Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Patino JR Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Barakovic M Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Romascano D Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Alemán-Gómez Y Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland.
Radua J FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain; Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Pomarol-Clotet E FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain.
Salvador R FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain; Mental Health Research Networking Center (CIBERSAM), Madrid, Spain.
Thiran JP Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Daducci A Computer Science Department, University of Verona, Verona, Italy.

2018-09-09

Published in:

NeuroImage. - 2019

Diffusion Magnetic Resonance Imaging

Image Processing, Computer-Assisted

Signal Processing, Computer-Assisted

English 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.

Language