@inproceedings{b9fca4848ae24ac6b23d6bb7a0834518,
title = "Challenges and Opportunities in dMRI Data Harmonization",
abstract = "Advances in diffusion MRI (dMRI) have led to discoveries of factors that affect brain microstructure and connectivity in health and disease. The small size of many neuroimaging studies led to concerns about poor reproducibility of research findings, and calls for the comparison and pooling of multi-cohort datasets to establish the consistency of reported effects. Across studies diffusion MRI protocols vary in spatial, angular and q-space resolution, b-value, as well as hardware used—all of which affect measured diffusion parameters. Efforts to compare and pool dMRI measures use meta- or mega- analytical techniques to compensate for these sources of variance. Meta-analytical methods gauge the consistency of effects, and mega-analytical methods involve mathematical or statistical transformations of the data. Here, we review some recent advances that allowed the diffusion community to create large scale population studies with greater rigor and generalizability than was previously attainable by individual studies.",
keywords = "DTI, DWI, Harmonization, Multi-site, diffusion MRI",
author = "Zhu, \{Alyssa H.\} and Moyer, \{Daniel C.\} and Nir, \{Talia M.\} and Thompson, \{Paul M.\} and Neda Jahanshad",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; International Workshop on Computational Diffusion MRI, CDMRI 2018 held with International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 ; Conference date: 20-09-2018 Through 20-09-2018",
year = "2019",
doi = "10.1007/978-3-030-05831-9\_13",
language = "English",
isbn = "9783030058302",
series = "Mathematics and Visualization",
publisher = "Springer Heidelberg",
pages = "157--172",
editor = "Elisenda Bonet-Carne and Francesco Grussu and Lipeng Ning and Farshid Sepehrband and Tax, \{Chantal M.W.\}",
booktitle = "Mathematics and Visualization",
address = "Germany",
}