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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
Pizzolato, Marco
Palombo, Marco
Bonet-Carne, Elisenda
Tax, Chantal M. W.
Grussu, Francesco
Ianus, Andrada
Bogusz, Fabian
Pieciak, Tomasz
Ning, Lipeng
Larochelle, Hugo
Author
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
Hutter, Jana
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
Hutter, Jana
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
Date
2020
Type
Book chapter
Journal
Book
Computational diffusion MRI : MICCAI Workshop, Shenzhen, China, October 2019
Volume
Issue
Page Range
195-208
Article Number
ACU Department
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as green open access
License
File Access
Open
Controlled
Controlled