A framework for motion correction of background suppressed arterial spin labeling perfusion images acquired with simultaneous multi-slice EPI.

Purpose: When using simultaneous multi-slice (SMS) EPI for background suppressed (BGS) arterial spin labeling (ASL), correction of through-plane motion could introduce artefacts, because the slices with most effective BGS are adjacent to slices with the least BGS. In this study, a new framework is presented to correct for such artefacts. Methods: The proposed framework consists of 3 steps: (1) homogenization of the static tissue signal over the different slices to eliminate most inter-slice differences because of different levels of BGS, (2) application of motion correction, and (3) extraction of a perfusion-weighted signal using a general linear model. The proposed framework was evaluated by simulations and a functional ASL study with intentional head motion. Results: Simulation studies demonstrated that the strong signal differences between slices with the most and least effective BGS caused sub-optimal estimation of motion parameters when through-plane motion was present. Although use of the M-0 image as the reference for registration allowed 82% improvement of motion estimation for through-plane motion, it still led to residual subtraction errors caused by different static tissue signal between control and label because of different BGS levels. By using our proposed framework, those problems were minimized, and the accuracy of CBF estimation was improved. Moreover, the functional ASL study showed improved detection of visual and motor activation when applying the framework as compared to conventional motion correction, as well as when motion correction was completely omitted. Conclusion: When combining BGS-ASL with SMS-EPI, particular attention is needed to avoid artefacts introduced by motion correction. With the proposed framework, these issues are minimized.