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Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion {MRI}

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2014
Author Daducci Alessandro, Canales-Rodríguez Erick Jorge, Descoteaux Maxime, Garyfallidis Eleftherios, Gur Yaniv, Lin Ying-Chia, Mani Merry, Merlet Sylvain, Paquette Michael, Ramirez-Manzanares Alonso, Reisert Marco, Rodrigues Paulo Reis, Sepehrband Farshid, Jacob Mathews, Caruyer Emmanuel, Choupan Jeiran, Deriche Rachid, Menegaz Gloria, Prckovska Vesna, Rivera Mariano, Wiaux Yves, Thiran Jean-Philippe,
Project Advanced signal processing on the sphere for high angular resolution diffusion magnetic resonance imaging
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Original article (peer-reviewed)

Journal {IEEE} Transactions on Medical Imaging
Volume (Issue) 33(2)
Page(s) 384 - 399
Title of proceedings {IEEE} Transactions on Medical Imaging
DOI 10.1109/Tmi.2013.2285500

Open Access

URL https://infoscience.epfl.ch/record/183667?ln=en
Type of Open Access Repository (Green Open Access)

Abstract

Validation is arguably the bottleneck in the diffusion 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.
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