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. Author manuscript; available in PMC: 2022 Oct 15.
Published in final edited form as: Neuroimage. 2022 Jul 12;260:119464. doi: 10.1016/j.neuroimage.2022.119464

Fig. 1.

Fig. 1.

A data-driven approach for automatic identification of sPVS. CSF (A), Vessel probability (B), and ADCsys-dia (C) masks were used to determine the initial sPVS mask (D). An iterative process was followed to constrain the sPVS mask to only include voxels that showed pulsatile pattern (E). A temporal correlation coefficient >0.6 was applied to constrain and update the sPVS mask for the next iteration. The iteration continued until the volume of sPVS converged (G), and a final sPVS mask was generated (H).