Acquiring and predicting multidimensional diffusion (MUDI) data : An open challenge
Book chapter
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, ... Hutter, Jana. (2020). Acquiring and predicting multidimensional diffusion (MUDI) data : An open challenge. In In Bonet-Carne, Elisenda, Hutter, Jana, Palombo, Marco, Pizzolato, Marco, Sepehrband, Farshid and Zhang, Fan (Ed.). Computational diffusion MRI : MICCAI Workshop, Shenzhen, China, October 2019 pp. 195-208 Springer. https://doi.org/10.1007/978-3-030-52893-5_17
Authors | 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 |
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Editors | Bonet-Carne, Elisenda, Hutter, Jana, Palombo, Marco, Pizzolato, Marco, Sepehrband, Farshid and Zhang, Fan |
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. |
Keywords | MUDI; diffusion; relaxation; quantitative imaging |
Page range | 195-208 |
Year | 2020 |
Book title | Computational diffusion MRI : MICCAI Workshop, Shenzhen, China, October 2019 |
Publisher | Springer |
Place of publication | Cham, Switzerland |
Series | Mathematics and Visualization |
ISBN | 9783030528928 |
9783030528935 | |
ISSN | 1612-3786 |
2197-666X | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-52893-5_17 |
Scopus EID | 2-s2.0-85095863984 |
Open access | Published as green open access |
Author's accepted manuscript | License All rights reserved File Access Level Open |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 14 May 2021 |
Publication process dates | |
Accepted | 10 May 2021 |
Deposited | 18 May 2021 |
https://acuresearchbank.acu.edu.au/item/8w147/acquiring-and-predicting-multidimensional-diffusion-mudi-data-an-open-challenge
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AM_Pizzolato_2020_Acquiring_and_predicting_multidimensional_diusion_MUDI.pdf | |
License: All rights reserved | |
File access level: Open |
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