Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and results
Conference item
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., Karamuyak, Suheyla Cetin, Rathi, Yogesh, Koppers, Simon, Weninger, Leon, Ebert, Julia, Merhof, Dorit, Moyer, Daniel, ... Tax, Chantal W. M.. (2019). Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and results. In E. Bonet-Carne, F. Grussu and L. Ning, F. Sepehrband & C. Tax (Ed.). International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI 2019. Switzerland: Springer Nature Switzerland AG. pp. 217 - 224 https://doi.org/10.1007/978-3-030-05831-9_18
Authors | 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., Karamuyak, 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 D., Rudrapatna, Umesh, Evans, John, Charron, Cyril, Jones, Derek Kenton and Tax, Chantal W. M. |
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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. |
Keywords | Diffusion MRI; Harmonisation; Spherical harmonics; Deep learning; Parametric model |
Year | 2019 |
Journal | International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI 2019 |
Publisher | Springer Nature Switzerland AG |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-05831-9_18 |
Scopus EID | 2-s2.0-85066914373 |
Publisher's version | File Access Level Controlled |
Page range | 217 - 224 |
Research Group | Mary MacKillop Institute for Health Research |
Place of publication | Switzerland |
Editors | E. Bonet-Carne, F. Grussu and L. Ning, F. Sepehrband & C. Tax |
https://acuresearchbank.acu.edu.au/item/87vw9/muti-shell-diffusion-mri-harmonisation-and-enhancement-challenge-mushac-progress-and-results
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