Fig. 16.

Stack associated with the upper-left corner in Fig. 15 showing substantial in-plane artifacts with relatively moderate slice motion for the non-rejected slices. Red crosses mark the slices that were automatically rejected by the proposed outlier-robust SRR (S) algorithm (only the slices covering the brain are shown; additional six, automatically segmented slices outside the brain were successfully rejected too). The NCC slice similarities $\text{Sim}\left({\mathbf{y}}_k^i,{\mathbf{A}}_k^i{\mathbf{x}}^{i-1}\right)<{\beta }_i$, at the time of rejection at iteration $i\in \left\{1,2,3\right\}$ with $\left({\beta }_1,{\beta }_2,{\beta }_3\right)=\left(0.5,0.65,0.8\right)$ are shown in addition. Thus, the outlier-rejection method is able to successfully detect and reject artifact-corrupted slices while keeping slices with good in-plane quality for the final volumetric reconstruction step. It is worth noting that this stack served as the target stack for the SRR algorithm. Successful reconstructions in subject and template spaces for that case are shown in Fig. 14b and Inline Supplementary Fig. S6, respectively.