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.; et al. (Mathematics and Visualization, 2019)
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.
CRIS-Team Scopus Importer:2023-03-23
Keywords: Deep learning; Diffusion MRI; Harmonisation; Parametric model; Spherical harmonics