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Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge

Pizzolato, Marco, Palombo, Marco, Bonet-Carne, Elisenda, Tax, Chantal M. W., Grussu, Francesco, Ianus, Andrada, Bogusz, Fabian, Pieciak, Tomasz, Ning, Lipeng, Larochelle, Hugo, Descoteaux, Maxime, Chamberland, Maxime, Blumberg, Stefano B., Mertzanidou, Thomy, Alexander, Daniel C., Afzali, Maryam, Aja-Fernández, Santiago, Jones, Derek K., Westin, Carl-Fredrik, Rathi, Yogesh, Baete, Steven H., Cordero-Grande, Lucilio, Ladner, Thilo, Slator, Paddy J., Hajnal, Joseph V, Thiran, Jean-Philippe, Price, Anthony N., Sepehrband, Farshid, Zhang, Fan and Hutter, Jana 2020. Acquiring and predicting multidimensional diffusion (MUDI) data: an open challenge. Presented at: MICCAI Workshop, Shenzhen, China, Oct 2019. Published in: Bonet-Carne, Elisenda, Hutter, Jana, Palombo, Marco, Pizzolato, Marco, Sepehrband, Farshid and Zhang, Fan eds. Computational Diffusion MRI. Mathematics and Visualization Springer, pp. 195-208. 10.1007/978-3-030-52893-5_17

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Psychology
Publisher: Springer
ISBN: 9783030528935
ISSN: 1612-3786
Last Modified: 15 Sep 2021 11:30
URI: http://orca.cardiff.ac.uk/id/eprint/143627

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