Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison

作者：Uran Ferizi, Benoît Scherrer, Torben Schneider, Mohammad Alipoor, Odin Eufracio, Rutger Fick, Rachid Deriche, Markus Nilsson, Ana K. Loya-Olivas, Mariano Rivera, Dirk H. J. Poot, Alonso Ramírez-Manzanares, José L. Marroquín, Ariel Rokem, Christian De Potter, Robert F. Dougherty, Ken Sakaie, Claudia A. M. Gandini Wheeler‐Kingshott, Simon K. Warfield, Thomas Witzel, Lawrence L. Wald, José G. Raya, Daniel C. Alexander · 发表于：NMR in Biomedicine · 年份：2017 · DOI：10.1002/nbm.3734 · 被引用次数：37 · 研究领域：Advanced Neuroimaging Techniques and Applications、MRI in cancer diagnosis、Bone and Joint Diseases

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique ...