The aim of the Super-resolution of Multi-dimensional Diffusion MRI (Super MUDI) Challenge is to super-resolve combined diffusion-relaxometry MRI data.
The challenge consists of two tasks. Each task explores a different low resolution MRI acquisition strategy, leading to two different types of images to super-resolve. Only submissions that attempt both of the tasks will be considered for evaluation.
The aim in Task 1 is to super-resolve data that has high in-plane resolution but thick (axial) slices, whereas Task 2 consists of super-resolving data with isotropically lower resolution. The images below show an example of the downsampling procedure in each task.
The winner of the challenge will be the submission with the overall best score (lower value) across the two tasks, computed as the sum of the scores in each task (see individual task description for further details about the metrics used for evaluation).
In this way, we will be able to draw conclusions on two aspects of super-resolution: 1) how reliable and stable is a super-resolution method; 2) which combination of sub-sampling strategy and super-resolution method is the best alternative to apply in a clinical scenario. The richness of contrasts in the MUDI data will allow us to generalize the derived conclusions to many MR imaging modalities.
The challenge results and discussion will be presented within the CDMRI'20 full-day workshop.