@article{eprints2421,
           month = {February},
          volume = {33},
          number = {2},
           pages = {384--399},
           title = {Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI},
            year = {2014},
         journal = {IEEE Transactions on Medical Imaging},
       publisher = {IEEE},
          author = {Alessandro Daducci and Erick Jorge Canales-Rodriguez and Maxime Descoteaux and Eleftherios Garyfallidis and Yaniv Gur and Ying-Chia Lin and Merry Mani and Sylvain Merlet and Michael Paquette and Alonso Ramirez-Manzanares and Marco Reisert and Paulo Reis Rodrigues and Farshid Sepehrband and Emmanuel Caruyer and Jeiran Choupan and Rachid Deriche and Matthew Jacob and Gloria Menegaz and Vesna Prckovska and Mariano Rivera and Yves Wiaux and Jean-Philippe Thiran},
        keywords = {Diffusion magnetic resonance imaging (dMRI); local reconstruction; quantitative comparison; synthetic data; validation},
        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.},
             url = {http://eprints.imtlucca.it/2421/}
}