title: Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI.
abstract: Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Daducci
authors.forename: Erick Jorge
authors.initials: EJ
authors.lastname: Canales-Rodríguez
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
authors.forename: Eleftherios
authors.initials: E
authors.lastname: Garyfallidis
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Gur
authors.forename: Ying-Chia
authors.initials: YC
authors.lastname: Lin
authors.forename: Merry
authors.initials: M
authors.lastname: Mani
authors.forename: Sylvain
authors.initials: S
authors.lastname: Merlet
authors.forename: Michael
authors.initials: M
authors.lastname: Paquette
authors.forename: Alonso
authors.initials: A
authors.lastname: Ramirez-Manzanares
authors.forename: Marco
authors.initials: M
authors.lastname: Reisert
authors.forename: Paulo
authors.initials: P
authors.lastname: Reis Rodrigues
authors.forename: Farshid
authors.initials: F
authors.lastname: Sepehrband
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Caruyer
authors.forename: Jeiran
authors.initials: J
authors.lastname: Choupan
authors.forename: Rachid
authors.initials: R
authors.lastname: Deriche
authors.forename: Mathews
authors.initials: M
authors.lastname: Jacob
authors.forename: Gloria
authors.initials: G
authors.lastname: Menegaz
authors.forename: Vesna
authors.initials: V
authors.lastname: Prčkovska
authors.forename: Mariano
authors.initials: M
authors.lastname: Rivera
authors.forename: Yves
authors.initials: Y
authors.lastname: Wiaux
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Thiran
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*