An intra-volume movement model was added to an existing framework for correcting off-resonance distortions, movement-induced signal dropout and subject movement in diffusion data. It was validated on highly realistic simulated data with "normal" and "high" levels of subject movement. The results show that slice-wise movement parameters can be estimated with an accuracy of ~0.2mm and ~0.2degrees for translations and rotations respectively. The simulations also show that the method substantially decreases the difference in fidelity of FA between subjects who move a lot and subjects who move a little. We finally demonstrate how the method corrects telltale signs of intra-volume movement in real data.