Diffusion tensor imaging has accelerated the study of brain connectivity, but single-tensor diffusion models are too simplistic to model fiber crossing and mixing. Hybrid diffusion imaging (HYDI) samples the radial and angular structure of local diffusion on multiple spherical shells in q-space, combining the high SNR and CNR achievable at low and high b-values, respectively. We acquired and analyzed human multi-shell HARDI at ultra-high field-strength (7 Tesla; b=1000, 2000, 3000 s/mm2). In experiments with the tensor distribution function (TDF), the b-value affected the intrinsic uncertainty for estimating component fiber orientations and their diffusion eigenvalues. We computed orientation density functions by least-squares fitting in multiple HARDI shells simultaneously. Within the range examined, higher b-values gave improved orientation estimates but poorer eigenvalue estimates; lower b-values showed opposite strengths and weaknesses. Combining these strengths, multiple-shell HARDI, especially with staggered angular sampling, outperformed single-shell scanning protocols, even when overall scanning time was held constant.