Background: Recent advances in diffusion weighted (DW) Magnetic Resonance Imaging (MRI) hardware have led to dramatic improvements of the data quality and reductions in scan duration. Such improvements come at the expense of high costs, and most clinical sites will not have access to such state-of-the-art systems in the near future. Moreover, in the current era of "big data", there is strong interest in developing techniques that allow for reliable combination of the information provided by different MRI scanners. Combining data from several scanners would increase dramatically the statistical power and sensitivity of clinical studies, with obvious benefits in clinical trials and multi-centre research. This Challenge represents a first step to unlock this potential.

Aims: The aim of the challenge is the systematic evaluation of the performance of algorithms that enable the harmonisation of DW MRI data across different scanners. Harmonisation consists of making data sets acquired with different scanners/protocols as comparable as possible. Specifically, this challenge will consist of a twofold task, and addresses the two aspects mentioned in the Background section.  On the one hand, algorithms will be tested for their ability of harmonising the same DW MRI protocol across different MRI scanners. On the other hand, algorithms will be tested for their ability of enhancing the quality of routine DW MRI data sets to resemble the latest state-of-the-art acquisitions, via extrapolation of information that was not acquired given a set of training examples.

Relevance: The results of this challenge will be highly informative and clinically relevant, since the low quality of routine DW MRI limits its applicability in the clinical practice in spite of its strong potential. Moreover, results will also be informative for multi-centre studies such as clinical trials, since the tested algorithms may help improve significantly their statistical power and their ability to detect differences among subject groups. 

Tasks for participating teams: The challenge will focus on DW MRI of the brain. It will consist of predicting unseen DW MRI scans given a set of examples made of acquisitions from the same subjects performed with different MRI machines and different DW protocols. The predicted scans will be compared directly to the reference, in order to assess the quality of the prediction. 

Two tasks will be evaluated:

  1. the harmonisation of DW data acquired with the same protocol but using machines from different vendors;
  2. the prediction of state-of-the-art DW MRI scans from standard DW MRI data sets of lower quality.  

The data will consist of whole-brain, multi-vendor DW scans and are available from the 16th of June 2017. All the data are kindly provided by the Cardiff University Brain Research Imaging Centre (CUBRIC).

Applications to obtain the data are invited (please sign the data transfer agreement here). The deadline for submission is 28th of August 2017, 23:59 BST.

Date and Venue

The Challenge will be part of the 2017 Computational Diffusion MRI (CDMRI'17) MICCAI Workshop, which will be held in the Québec City Convention Centre (Canada) on the September 10, 2017.