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Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results

Ning, Lipeng, Bonet-Carne, Elisenda, Grussu, Francesco, Sepehrband, Farshid, Kaden, Enrico, Veraart, Jelle, Blumberg, Stefano B., Khoo, Can Son, Palombo, Marco, Coll-Font, Jaume, Scherrer, Benoit, Warfield, Simon K., Karayumak, Suheyla Cetin, Rathi, Yogesh, Koppers, Simon, Weninger, Leon, Ebert, Julia, Merhof, Dorit, Moyer, Daniel, Pietsch, Maximilian, Christiaens, Daan, Teixeira, Rui, Tournier, Jacques-Donald, Zhylka, Andrey, Pluim, Josien, Parker, Greg, Rudrapatna, Umesh, Evans, John, Charron, Cyril, Jones, Derek K. and Tax, Chantal W. M. 2019. Muti-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC): Progress and results. Presented at: MICCAI 2018, Granada, Spain, 16-20 September 2018. Published in: Bonet-Carne, E, Grussu, F, Ning, L, Sepehrband, F and Tax, C eds. Computational Diffusion MRI. Mathematics and Visualization Cham: Springer, pp. 217-224. 10.1007/978-3-030-05831-9_18

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Abstract

We present a summary of competition results in the multi-shell diffusion MRI harmonisation and enhancement challenge (MUSHAC). MUSHAC is an open competition intended to stimulate the development of computational methods that reduce scanner- and protocol-related variabilities in multi-shell diffusion MRI data across multi-site studies. Twelve different methods from seven research groups have been tested in this challenge. The results show that cross-vendor harmonization and enhancement can be performed by using suitable computational algorithms such as deep convolutional neural networks. Moreover, parametric models for multi-shell diffusion MRI signals also provide reliable performances.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Psychology
Publisher: Springer
ISBN: 978-3-030-05831-9
ISSN: 2197-666X
Last Modified: 28 Aug 2019 12:14
URI: http://orca.cf.ac.uk/id/eprint/123692

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