Harmonizing diffusion MRI data across magnetic field strengths

Abstract

Diffusion MRI (dMRI) data is increasingly being acquired on multiple scanners as part of large multi-center neuroimaging studies. However, diffusion imaging is particularly sensitive to scanner-specific differences in coil sensitivity, reconstruction algorithms, acquisition parameters as well as the scanner magnetic field strength, which precludes joint analysis of such multi-site data. Earlier works on dMRI data harmonization were limited to data acquired on different scanners but with the same magnetic field strength (3T). In this work, we explore the possibility of harmonizing dMRI data acquired on scanners with different magnetic field strengths, i.e., 3T and 7T. We propose a linear and several machine learning based non-linear mapping algorithms that use rotation invariant spherical harmonic (RISH) features to map the dMRI data (the raw signal) between scanners without changing the fiber orientations. We extensively validate our algorithms on in-vivo data from the Human Connectome Project (HCP) where we used data from 40 subjects with scans done on both 7T and 3T scanners (10 training + 30 test). Using several quantitative metrics such as the root mean squared error (RMSE) in the harmonized dMRI signal and diffusion measures as well as a fiber bundle overlap measure, our preliminary results on 30 test subjects shows that the convolutional neural network (CNN) based algorithm can reliably harmonize the raw dMRI signal across magnetic field strengths. The algorithms proposed are general and can be used for dMRI data harmonization in multi-site studies.