Help Privacy Policy Disclaimer
  Advanced SearchBrowse


  Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

Ning, L., Laun, F., Gur, Y., DiBella, E. V., Deslauriers-Gauthier, S., Megherbi, T., et al. (2015). Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use? Medical Image Analysis, 26(1), 316-331. doi:10.1016/j.media.2015.10.012.

Item is


show Files




Ning, Lipeng1, Author
Laun, Frederik1, Author
Gur, Yaniv1, Author
DiBella, Edward V.R.1, Author
Deslauriers-Gauthier, Samuel1, Author
Megherbi, Thinhinane1, Author
Ghosh, Aurobrata1, Author
Zucchelli, Mauro1, Author
Menegaz, Gloria1, Author
Fick, Rutger1, Author
St-Jean, Samuel1, Author
Paquette, Michael1, Author           
Aranda, Ramon1, Author
Descoteaux, Maxime1, Author
Deriche, Rachid1, Author
O’Donnell, Lauren1, Author
Rathi, Yogesh1, Author
1External Organizations, ou_persistent22              


Free keywords: Diffusion MRI; Physical phantom; Normalized mean square error; Angular error
 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.


Language(s): eng - English
 Dates: 2015-10-232015-03-262015-10-272015-11-102015-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -



Legal Case


Project information


Source 1

Title: Medical Image Analysis
  Other : Med. Image Anal.
Source Genre: Journal
Publ. Info: London : Elsevier
Pages: - Volume / Issue: 26 (1) Sequence Number: - Start / End Page: 316 - 331 Identifier: ISSN: 1361-8415
CoNE: https://pure.mpg.de/cone/journals/resource/954927741859