Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results

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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.; Tax, Chantal W.M.
(2019)
Computational diffusion MRI, issue 226249, pp. 217 - 224
Mathematics and Visualization, issue 226249, pp. 217 - 224
International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, issue 226249, pp. 217 - 224
(Part of book)
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 ... read more
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Keywords: Deep learning, Diffusion MRI, Harmonisation, Parametric model, Spherical harmonics, Modelling and Simulation, Geometry and Topology, Computer Graphics and Computer-Aided Design, Applied Mathematics
DOI: https://doi.org/10.1007/978-3-030-05831-9_18
ISSN: 1612-3786
ISBN: 9783030058302
9783030058319
Publisher: Springer Heidelberg
(Peer reviewed)