High Angular Resolution Diffusion Imaging (HARDI) is currently considered one of the most suitable approach for study white matter connectivity and is routinely adopted in several clinical research studies. However, with HARDI, a big amount of raw data is also collected, making difficult to manually inspect all of Diffusion Weighted (DW) volumes for artefacts that may negatively impact further analyses. These artifacts, due to head movements, physiological noise and scanner-related issue, are often also subtle and arduous to detect visually, even by expert users. Tools for artifacts identification, based on the evaluation of outliers when computing the residual of the signal fitted by different models[1,2], have been already developed. However, these methods detect outliers at the individual voxel level while most, if not all, artifacts affecting HARDI data are usually impacting more voxels within the same DW volume and brain slice. Moreover, most of this approaches simply reject outliers and fit the specific diffusion model with the remaining data points making potentially this estimation more computational demanding (i.e. applying a customized b-matrix or fiber response model per each voxel). In this work we adopted a fast slice-wise approach to detect corrupted slices within different DW volumes and regenerate the missing data using spherical harmonics (SH) decomposition of the HARDI signal. SH decomposition has also per se an intrinsic beneficial effect of denoising and removal of any not antipodal symmetric features of the HARDI signal. The final output is a fully regenerated dataset that can be then processed with any existing diffusion pipeline.HARDI data from 15 healthy subjects, mean age 32 ± 5 years, were acquired using a 3T GE HDx system (General Electric, Milwaukee, WI, USA) with the following parameters: voxel size 2.4x2.4x2.4 mm, slices 60, b-value 3000 s/mm2, 60 diffusion-weighted directions and 7 non-diffusion weighted volumes. After correcting for motion and eddy current distortions using FSL (fsl.fmrib.ox.ac.uk/fsl), for each subject the following steps were performed using a custom written Matlab code (R2013a, www.mathworks.com): 1) HARDI data was fitted using a SH decomposition. 2) Residuals values were computed as the difference between the actual HARDI signal and the SH modelled signal. For each brain slice the mean value of this residual was calculated along each DW direction. 3) A binary outlier mask was created by identifying as outliers all DW directions with a mean residual value above an automatic threshold obtained for each slice across all DW directions and defined as: $$$threshold=Q3+IQR\times3$$$, where Q3 is the 75th percentile and IQR is the interquartile range computed on the mean residuals[3]. 4) Corrupted signals were regenerated using new SH coefficients obtained by SH decomposition performed this time without outlier directions. SH at order 6 was used when regenerating slices of corrupted directions while SH at order 8 was applied to the rest of the data.To validate our method, a simulated artifact was created in a subject who did not present any artifact. In figure 1, panel a) shows the corrupted dataset in the sagittal view while panel b) displays the actual (left) and the regenerated (right) data. On real data, visual inspection of the diffusion dataset identified 2 subject with obvious and significant artefacts of along different DW directions. All artifacts were detected and corrected by the proposed approach. In figures 2 and 3, two examples of artifact correction are shown. Figure 2a illustrates the outlier mask in which one of the outlier value is identified and its location displayed in sagittal and axial views before (Fig. 2b) and after (Fig. 2c) correction. Similar results are show in figure 3. Few seconds were required to correct each subject on normal laptop. This work presents an effective and fast tool for the automatic outlier detection and correction. Our study confirms the efficacy of using SH for artifacts identification and proposes a completely model independent approach and simple solution for their correction. This approach can be useful in large studies when visual quality check may be not practical or when data artefacts are not obvious at visual inspection. To conclude, the present work introduces a fully automatic method for both artifact identification and correction of HARDI data which can potentially increase the precision in DW-derived measures.