Motion occurring during the acquisition of diffusion-weighted image volumes is inevitable. Deficient accuracy of volumetric realignment and within-volume movements cause the quality of diffusion model reconstruction to deteriorate, particularly for uncooperative subjects. Taking advantage of the strong inference ability of neural networks, we reconstructed diffusion parametric maps with remaining volumes after the motion-corrupted data removed. Compared to conventional model fitting, our method is minimally sensitive to motion effects and generates results comparable to the gold standard, with as few as eight volumes retained from the motion-contaminated data. This method shows great potential in exploiting some valuable but motion-corrupted DWI data.