B-tensor data

Diffusion-weighted images (from two healthy participants) were acquired with 10 b=0 and 8 non-zero shells (b=1, 2, 3, 4.5, 6, 7.5, 9, 10.5  ms/um^2) in (10, 31, 31, 31, 31, 61, 61, 61, 61) directions for linear tensor encoding (LTE) and 5 shells (b=1, 2, 3, 4.5, 6 ms/um^2) in (31, 31, 31, 31, 61) directions for planar tensor encoding (PTE) and 5 shells for spherical tensor encoding (STE) (b=0.2, 1, 2, 3, 4.5 ms/um^2$) in (6, 9, 9, 12, 15) using a 3T Connectom MR imaging system with 300 mT/m gradients (Siemens Healthineers, Erlangen, Germany). Forty-two axial slices with 3 mm isotropic voxel size and a 78x78 matrix size, TE = 88 ms, TR = 3000 ms, partial Fourier factor = 6/8, were obtained for each individual. 

To take full advantage of q-space trajectory imaging, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. Sjolund et al. 2015 provided a tool for achieving this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating, and positioning of radiofrequency pulses. The gradient waveform is optimized and Maxwell-compensated based on a framework that maximizes the b-value for a given measurement b-tensor shape and echo time. Substantial gains in terms of reduced echo times and increased signal-to-noise ratio can be achieved, in particular as compared with naive planar and spherical tensor encoding. 

Data are available in nifti format.

Research results based upon these data are published at https://doi.org/10.1016/j.neuroimage.2021.118183