Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain ‘truly quantitative measures’ and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with ‘standard’ and ‘state-of-the-art’ protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today’s data harmonisation techniques.
Chantal MW. Tax, Francesco Grussu, Enrico Kaden, Lipeng Ning, Umesh Rudrapatna, John Evans, Samuel St-Jean, Alexander Leemans, Simon Koppers, Dorit Merhof, Aurobrata Ghosh, Ryutaro Tanno, Daniel C. Alexander, Stefano Zappalà, Cyril Charron, Slawomir Kusmia, David EJ. Linden, Derek K. Jones, Jelle Veraart Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms NeuroImage, 2019.