Diffusion MRI microstructure models with in vivo human brain Connectom data: results from a multi-group comparison
Résumé
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, 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 model in their ability to explain measured in vivo DW human brain data. We focus specifically on the challenge of explaining a large range of measurable data. We used the Connectome scanner at the Massachusetts General Hospital, using gradients 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 opportunity for quantitative comparison of a diverse set of methods from multiple groups worldwide. The comparison of the challenge entries reveals important trends and conclusions that influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact tissue models on average rank highest of those tested. The second is that assuming a non-Gaussian (rather than a purely Gaussian) noise model provides little benefit. The third is that preprocessing the training data (here, omitting signal outliers) and using signal predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remains available to build up a more complete comparison over future years.
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