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A large number of mathematical models have been proposed to describe themeasured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) andinfer properties about the white matter microstructure. However, a head-to-headcomparison of DW-MRI models is critically missing in the field. To address thisdeficiency, we organized the "White Matter Modeling Challenge" during theInternational Symposium on Biomedical Imaging (ISBI) 2015 conference. Thiscompetition aimed at identifying the DW-MRI models that best predict unseen DWdata. in vivo DW-MRI data was acquired on the Connectom scanner at theA.A.Martinos Center (Massachusetts General Hospital) using gradients strengthof up to 300 mT/m and a broad set of diffusion times. We focused on assessingthe DW signal prediction in two regions: the genu in the corpus callosum, wherethe fibres are relatively straight and parallel, and the fornix, where theconfiguration of fibres is more complex. The challenge participants had accessto three-quarters of the whole dataset, and their models were ranked on theirability to predict the remaining unseen quarter of data. In this paper weprovide both an overview and a more in-depth description of each evaluatedmodel, report the challenge results, and infer trends about the modelcharacteristics that were associated with high model ranking. This workprovides a much needed benchmark for DW-MRI models. The acquired data and modeldetails for signal prediction evaluation are provided online to encourage alarger scale assessment of diffusion models in the future.

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