title: Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?
abstract: Diffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications.
authors.affiliation: Brigham and Women's Hospital, Harvard Medical School, Boston, United States. Electronic address: lning@bwh.harvard.edu.
authors.forename: Lipeng
authors.initials: L
authors.lastname: Ning
authors.affiliation: German Cancer Research Institute, Germany.
authors.forename: Frederik
authors.initials: F
authors.lastname: Laun
authors.affiliation: IBM Almaden Research Center, San Jose, United States.
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Gur
authors.affiliation: Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology, University of Utah, United States.
authors.forename: Edward V R
authors.initials: EV
authors.lastname: DiBella
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
authors.forename: Samuel
authors.initials: S
authors.lastname: Deslauriers-Gauthier
authors.affiliation: ParIMed Team, LRPE, USTHB, Algiers, Algeria.
authors.forename: Thinhinane
authors.initials: T
authors.lastname: Megherbi
authors.affiliation: Athena Project-Team, Inria Sophia Antipolis-Méditerranée, France.
authors.forename: Aurobrata
authors.initials: A
authors.lastname: Ghosh
authors.affiliation: Department of Computer Science, University of Verona, Italy.
authors.forename: Mauro
authors.initials: M
authors.lastname: Zucchelli
authors.affiliation: Department of Computer Science, University of Verona, Italy.
authors.forename: Gloria
authors.initials: G
authors.lastname: Menegaz
authors.affiliation: Athena Project-Team, Inria Sophia Antipolis-Méditerranée, France.
authors.forename: Rutger
authors.initials: R
authors.lastname: Fick
authors.affiliation: Université de Sherbrooke, Canada.
authors.forename: Samuel
authors.initials: S
authors.lastname: St-Jean
authors.affiliation: Université de Sherbrooke, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Paquette
authors.affiliation: Centro de Investigation en Matematicas, Department of Computer Science, Mexico.
authors.forename: Ramon
authors.initials: R
authors.lastname: Aranda
authors.affiliation: Université de Sherbrooke, Canada.
authors.forename: Maxime
authors.initials: M
authors.lastname: Descoteaux
authors.affiliation: Athena Project-Team, Inria Sophia Antipolis-Méditerranée, France.
authors.forename: Rachid
authors.initials: R
authors.lastname: Deriche
authors.affiliation: Brigham and Women's Hospital, Harvard Medical School, Boston, United States.
authors.forename: Lauren
authors.initials: L
authors.lastname: O'Donnell
authors.affiliation: Brigham and Women's Hospital, Harvard Medical School, Boston, United States.
authors.forename: Yogesh
authors.initials: Y
authors.lastname: Rathi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066127:White Matter / Q000033:anatomy & histology*
keywords: Angular error; Diffusion MRI; Normalized mean square error; Physical phantom