Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients
Qiyuan Tian, Qiuyun Fan, Thomas Witzel, Maya N. Polackal, Ned A. Ohringer, Chanon Ngamsombat, Andrew W. Russo, Natalya Machado, Kristina Brewer, Fuyixue Wang, Kawin Setsompop, Jon̈athan R. Polimeni, Boris Keil, Lawrence L. Wald, Bruce R. Rosen, Eric C. Klawiter, Aapo Nummenmaa, Susie Y. Huang
Abstract
Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.