B-tensor data

<p>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. <br></p><p>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. </p><p>Data are available in nifti format.</p><p>Research results based upon these data are published at https://doi.org/10.1016/j.neuroimage.2021.118183<br></p><p><br></p>