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 Rathi; Steven 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

doi:10.1007/978-3-030-52893-5_17

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

Marco Pizzolato (Corresponderende auteur)
, 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 Rathi

Steven 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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

17 Citaten (Scopus)

Samenvatting

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.

Originele taal-2	Engels
Titel	Computational Diffusion MRI
Subtitel	MICCAI Workshop, Shenzhen, China, October 2019
Redacteuren	Elisenda Bonet-Carne
Uitgeverij	Springer
Pagina's	195-208
Aantal pagina's	14
ISBN van elektronische versie	978-3-030-52893-5
ISBN van geprinte versie	978-3-030-52892-8
DOI's	https://doi.org/10.1007/978-3-030-52893-5_17
Status	Gepubliceerd - 7 nov 2020
Extern gepubliceerd	Ja

Publicatie series

Naam	Mathematics and Visualization
ISSN van geprinte versie	1612-3786
ISSN van elektronische versie	2197-666X

Bibliografische nota

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Financiering

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]. 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].

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10.1007/978-3-030-52893-5_17

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Pizzolato, M., Palombo, M., Bonet-Carne, E., Tax, C. M. W., Grussu, F., Ianus, A., Bogusz, F., Pieciak, T., Ning, L., Larochelle, H., Descoteaux, M., Chamberland, M., Blumberg, S. B., Mertzanidou, T., Alexander, D. C., Afzali, M., Aja-Fernández, S., Jones, D. K., Westin, C.-F., ... Hutter, J. (2020). Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. In E. Bonet-Carne (editor), Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019 (blz. 195-208). (Mathematics and Visualization). Springer. https://doi.org/10.1007/978-3-030-52893-5_17

@inbook{c1d3cb6614c94176b078dbe40fecbdb6,

title = "Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge",

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.",

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

note = "Funding Information: Acknowledgments MPiz acknowledges support from European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sk{\l}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{\'o}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{\textquoteright}s work was supported by Ministerio de Ciencia e Inno-vaci{\'o}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]. 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: {\textcopyright} 2020, Springer Nature Switzerland AG.",

year = "2020",

month = nov,

day = "7",

doi = "10.1007/978-3-030-52893-5\_17",

language = "English",

isbn = "978-3-030-52892-8",

series = "Mathematics and Visualization",

publisher = "Springer",

pages = "195--208",

editor = "Elisenda Bonet-Carne",

booktitle = "Computational Diffusion MRI",

address = "Germany",

}

Pizzolato, M, Palombo, M, Bonet-Carne, E, Tax, CMW, Grussu, F, Ianus, A, Bogusz, F, Pieciak, T, Ning, L, Larochelle, H, Descoteaux, M, Chamberland, M, Blumberg, SB, Mertzanidou, T, Alexander, DC, Afzali, M, Aja-Fernández, S, Jones, DK, Westin, C-F, Rathi, Y, Baete, SH, Cordero-Grande, L, Ladner, T, Slator, PJ, Hajnal, JV, Thiran, JP, Price, AN, Sepehrband, F, Zhang, F & Hutter, J 2020, Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. in E Bonet-Carne (reds.), Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019. Mathematics and Visualization, Springer, blz. 195-208. https://doi.org/10.1007/978-3-030-52893-5_17

Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge. / Pizzolato, Marco (Corresponderende auteur); Palombo, Marco; Bonet-Carne, Elisenda et al.
Computational Diffusion MRI: MICCAI Workshop, Shenzhen, China, October 2019. reds. / Elisenda Bonet-Carne. Springer, 2020. blz. 195-208 (Mathematics and Visualization).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

TY - CHAP

T1 - Acquiring and Predicting Multidimensional Diffusion (MUDI) Data

T2 - An Open Challenge

AU - Pizzolato, Marco

AU - Palombo, Marco

AU - Bonet-Carne, Elisenda

AU - Tax, Chantal M.W.

AU - Grussu, Francesco

AU - Ianus, Andrada

AU - Bogusz, Fabian

AU - Pieciak, Tomasz

AU - Ning, Lipeng

AU - Larochelle, Hugo

AU - Descoteaux, Maxime

AU - Chamberland, Maxime

AU - Blumberg, Stefano B.

AU - Mertzanidou, Thomy

AU - Alexander, Daniel C.

AU - Afzali, Maryam

AU - Aja-Fernández, Santiago

AU - Jones, Derek K.

AU - Westin, Carl-Fredrik

AU - Rathi, Yogesh

AU - Baete, Steven H.

AU - Cordero-Grande, Lucilio

AU - Ladner, Thilo

AU - Slator, Paddy J.

AU - Hajnal, Joseph V.

AU - Thiran, Jean Philippe

AU - Price, Anthony N.

AU - Sepehrband, Farshid

AU - Zhang, Fan

AU - Hutter, Jana

N1 - 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.

PY - 2020/11/7

Y1 - 2020/11/7

N2 - 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.

AB - 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.

KW - Diffusion

KW - MUDI

KW - Quantitative imaging

KW - Relaxation

UR - https://www.scopus.com/pages/publications/85095863984

U2 - 10.1007/978-3-030-52893-5_17

DO - 10.1007/978-3-030-52893-5_17

M3 - Chapter

AN - SCOPUS:85095863984

SN - 978-3-030-52892-8

T3 - Mathematics and Visualization

SP - 195

EP - 208

BT - Computational Diffusion MRI

A2 - Bonet-Carne, Elisenda

PB - Springer

ER -

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

Samenvatting

Publicatie series

Bibliografische nota

Financiering

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