Head movement during cerebral CT perfusion imaging of acute ischaemic stroke: Characterisation and correlation with patient baseline features. Issue 144 (November 2021) - Dashtbani Moghari, Mahdieh; Young, Noel; Moore, Krystal; Fulton, Roger R.; Evans, Andrew; Kyme, Andre Z.
Highlights: Quantitative characterisation of the head motion during acute stroke CTP imaging. Evidence of mild to extreme head motion for approximately 40% of stroke patients. Increased NIHSS and patient age are patient-based risk factors for motion. A multi-component feature combining NIHSS and age is highly predictive of motion. Potential use of the predictive model to anticipate cases at-risk of motion. Abstract: Purpose: To quantitatively characterise head motion prevalence and severity and to identify patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute ischaemic stroke. Methods: The head motion of 80 stroke patients undergoing CTP imaging was classified retrospectively into four categories of severity. Each motion category was then characterised quantitatively based on the average head movement with respect to the first frame for all studies. Statistical testing and principal component analysis (PCA) were then used to identify and analyse the relationship between motion severity and patient baseline features. Results: 46/80 (58%) of patients showed negligible motion, 19/80 (24%) mild-to-moderate motion, and 15/80 (19%) considerable-to-extreme motion sufficient to affect diagnostic/therapeutic accuracy even with correction. The most prevalent movement was "nodding" with maximal translation/rotation in the sagittal/axial planes. There was a tendency for motion to worsen as scan proceeded and for faster motion to occur in the first 15 s. Statistical analyses showed that greater stroke severity (National Institutes of Health Stroke Scale (NIHSS)), older patient age and shorter time from stroke onset were predictive of increased head movement (p < 0.05 Kruskal-Wallis). Using PCA, the combination of NIHSS and patient age was found to be highly predictive of head movement (p < 0.001). Conclusions: Quantitative methods were developed to characterise CTP studies impacted by motion and to anticipate patients at-risk of motion. NIHSS, age, and time from stroke onset function as good predictors of motion likelihood and could potentially be used pre-emptively in CTP scanning of acute stroke.