{"url":"/dataset/supermudi","name":"SuperMUDI","full_name":null,"description_markdown":"The Super-resolution of Multi-Dimensional Diffusion MRI\r\n(Super MUDI) dataset  contains the data of four healthyhuman subjects with ages range between 19 and 46 years.\r\nFor each subject 1,344 MRI volumes are provided. Theimaging device was clinical 3T Philips Achieva Scanner\r\n(Best, Netherlands) with a 32-channel adult head coil.\r\nThe Super MUDI Challenge comprises two tasks:\r\nisotropic, and anisotropic super-resolution. The names of\r\nthese tasks were derived from the acquisition strategies of\r\nthe low-resolution MRI data. The objective of using two\r\ndown-sampling strategies is to compare the combinations of\r\nthe down-sampling methods and the super-resolution\r\napproaches that can best to be used in a clinical scheme to\r\nobtain simulated high-quality and high-fidelity MRI images\r\nwhile reducing the acquisition time. In the anisotropic\r\nsubsampling the volume has high in-plane resolution\r\n(2.5mm ×2.5mm), but thick axial slice (5mm), while in the\r\nisotropic subsampling the volume has low resolution (5mm)\r\nin all the directions.\r\nFor our experiments, we use one subject each for training\r\nand validation, and two for testing.\r\nReference: Marco Pizzolato, Marco Palombo, Jana Hutter, Vish-\r\nwesh Nash, Fan Zhang, and Noemi Gyori, “Super-\r\nresolution of Multi Dimensional Diffusion MRI data,”\r\nMar. 2020","description_withheld":null,"homepage":"http://cmic.cs.ucl.ac.uk/cdmri20/challenge.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SuperMUDI"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}