Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge

Marco Pizzolato*, Marco Palombo, Elisenda Bonet-Carne, Chantal M.W. Tax, Francesco Grussu, Andrada Ianus, Fabian Bogusz, Tomasz Pieciak, Lipeng Ning, Hugo Larochelle, Maxime Descoteaux, Maxime Chamberland, Stefano B. Blumberg, Thomy Mertzanidou, Daniel C. Alexander, Maryam Afzali, Santiago Aja-Fernández, Derek K. Jones, Carl Fredrik Westin, Yogesh RathiSteven H. Baete, Lucilio Cordero-Grande, Thilo Ladner, Paddy J. Slator, Joseph V. Hajnal, Jean Philippe Thiran, Anthony N. Price, Farshid Sepehrband, Fan Zhang, Jana Hutter

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Citations (Scopus)


In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion-summarized by the b-value-constitutes yet another explorable “dimension” to modify the image contrast, which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it.

Original languageEnglish
Title of host publicationMathematics and Visualization
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
Publication statusPublished - 2020

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


  • Diffusion
  • MUDI
  • Quantitative imaging
  • Relaxation


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