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.
Original language | English |
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Title of host publication | Computational Diffusion MRI |
Subtitle of host publication | International MICCAI Workshop, Granada, Spain, September 2018 |
Editors | Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M.W. Tax |
Place of Publication | Cham |
Publisher | Springer |
Chapter | 18 |
Pages | 217-224 |
Number of pages | 8 |
ISBN (Electronic) | 978-3-030-05831-9 |
ISBN (Print) | 978-3-030-05830-2 |
DOIs | |
Publication status | Published - 2019 |
Event | 9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) : Atrial Segmentation and LV Quantification Challenges - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018 Conference number: 9 |
Publication series
Name | Mathematics and Visualization |
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ISSN (Print) | 1612-3786 |
ISSN (Electronic) | 2197-666X |
Conference
Conference | 9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) |
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Abbreviated title | STACOM 2018 |
Country/Territory | Spain |
City | Granada |
Period | 16/09/18 → 16/09/18 |
Other | held in conjunction with the 21st Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 |
Keywords
- Deep learning
- Diffusion MRI
- Harmonisation
- Parametric model
- Spherical harmonics