Validation of motion correction techniques for liver CT perfusion studies

Abstract

Motion in images potentially compromises the evaluation of temporally acquired CT perfusion (CTp) data; image registration should mitigate this, but first requires validation. Our objective was to compare the relative performance of manual, rigid and non-rigid registration techniques to correct anatomical misalignment in acquired liver CTp data sets. 17 data sets in patients with liver tumours who had undergone a CTp protocol were evaluated. Each data set consisted of a cine acquisition during a breath-hold (Phase 1), followed by six further sets of cine scans (each containing 11 images) acquired during free breathing (Phase 2). Phase 2 images were registered to a reference image from Phase 1 cine using two semi-automated intensity-based registration techniques (rigid and non-rigid) and a manual technique (the only option available in the relevant vendor CTp software). The performance of each technique to align liver anatomy was assessed by four observers, independently and blindly, on two separate occasions, using a semi-quantitative visual validation study (employing a six-point score). The registration techniques were statistically compared using an ordinal probit regression model. 306 registrations (2448 observer scores) were evaluated. The three registration techniques were significantly different from each other (p=0.03). On pairwise comparison, the semi-automated techniques were significantly superior to the manual technique, with non-rigid significantly superior to rigid (p<0.0001), which in turn was significantly superior to manual registration (p=0.04). Semi-automated registration techniques achieved superior alignment of liver anatomy compared with the manual technique. We hope this will translate into more reliable CTp analyses.