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; Hutter, Jana
(2020) Computational Diffusion MRI, pp. 195 - 208
Mathematics and Visualization, pp. 195 - 208
(Part of book)
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
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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.
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Keywords: Diffusion, MUDI, Quantitative imaging, Relaxation, Modelling and Simulation, Geometry and Topology, Computer Graphics and Computer-Aided Design, Applied Mathematics
ISSN: 1612-3786
Publisher: Springer Science and Business Media Deutschland GmbH
Note: Funding Information: Acknowledgments MPiz acknowledges support from European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. EB-C is partially supported by the Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK). CMWT is supported by a Veni grant (17331) from the Dutch Research Council (NWO) and a Sir Henry Wellcome Fellowship (215944/Z/19/Z). FB and TP acknowledge AGH Science and Technology, Kraków, Poland (16.16.120.773). MA and DKJ were supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and a Wellcome Trust Strategic Award (104943/Z/14/Z). SHB is supported in part by the National Institutes of Health (NIH, R01-CA111996, R01-NS082436, R01-MH00380 and P41EB017183). MPal, FG, DCA, TM and SBB acknowledge support from the UK EPSRC (EP/M020533/1, EP/N018702/1, EP/R006032/1), EU Horizon 2020 (ID 634541), NIH (Placenta imaging Project); Grant number: 1U01HD087202-01. AI acknowledges support from the Champalimaud Centre for the Unknown. LN is supported in part by NIH grants R21MH116352, R21MH115280, K01MH11 7346. SA-F’s work was supported by Ministerio de Ciencia e Inno-vación of Spain with research grant RTI2018-094569-B-I00; FZ is supported by the following NIH grants: P41EB015898, R01MH108574, P41EB015902, R01MH 119222. JH was supported by the Wellcome Trust (Sir Henry Wellcome Fellowship, [201374/Z/16/Z] [201374/Z/16/B]), and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. Funding Information: MPiz acknowledges support from European Union?s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. EB-C is partially supported by the Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, UK). CMWT is supported by a Veni grant (17331) from the Dutch Research Council (NWO) and a Sir Henry Wellcome Fellowship (215944/Z/19/Z). FB and TP acknowledge AGH Science and Technology, Krak?w, Poland (16.16.120.773). MA and DKJ were supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and a Wellcome Trust Strategic Award (104943/Z/14/Z). SHB is supported in part by the National Institutes of Health (NIH, R01-CA111996, R01-NS082436, R01-MH00380 and P41EB017183). MPal, FG, DCA, TM and SBB acknowledge support from the UK EPSRC (EP/M020533/1, EP/N018702/1, EP/R006032/1), EU Horizon 2020 (ID 634541), NIH (Placenta imaging Project); Grant number: 1U01HD087202-01. AI acknowledges support from the Champalimaud Centre for the Unknown. LN is supported in part by NIH grants R21MH116352, R21MH115280, K01MH11 7346. SA-F?s work was supported by Ministerio de Ciencia e Innovaci?n of Spain with research grant RTI2018-094569-B-I00; FZ is supported by the following NIH grants: P41EB015898, R01MH108574, P41EB015902, R01MH 119222. JH was supported by the Wellcome Trust (Sir Henry Wellcome Fellowship, [201374/Z/16/Z] [201374/Z/16/B]), and the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. Publisher Copyright: © 2020, Springer Nature Switzerland AG.
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