Scanners with ultra-strong gradients promise unprecedented opportunities for diffusion imaging. However, their effective use requires correction of gradient-nonlinearity effects during data analysis. Although such techniques exist, they neglect the effects of motion which induces spatio-temporal variations in b-values and b-vectors. Here, we propose a technique that accounts for interaction of subject motion with such non-linearity and study its effectiveness by performing diffusion experiments with volunteers positioned in regions with incrementally increasing gradient-nonlinearities. Our experiments reveal the importance of accounting for motion-induced spatio-temporal variations in B-matrices and our proposed technique corrects most gradient-nonlinearity effects.