Automatic 2D registration of renal perfusion image sequences by mutual information and adaptive prediction

Abstract

The objective of this study was to develop an automatic image registration technique capable of compensating for kidney motion in renal perfusion MRI, to assess the effect of renal artery stenosis on the kidney parenchyma.

Materials and methods: Images from 20 patients scheduled for a renal perfusion study were acquired using a 1.5 T scanner. A free-breathing 3D-FSPGR sequence was used to acquire coronal views encompassing both kidneys following the infusion of Gd-BOPTA. A two-step registration algorithm was developed, including a preliminary registration minimising the quadratic difference and a fine registration maximising the mutual information (MI) between consecutive image frames. The starting point for the MI-based registration procedure was provided by an adaptive predictor that was able to predict kidney motion using a respiratory movement model. The algorithm was validated against manual registration performed by an expert user.

Results: The mean distance between the automatically and manually defined contours was 2.95 ± 0.81 mm, which was not significantly different from the interobserver variability of the manual registration procedure (2.86 ± 0.80 mm, P = 0.80). The perfusion indices evaluated on the manually and automatically extracted perfusion curves were not significantly different.

Conclusions: The developed method is able to automatically compensate for kidney motion in perfusion studies, which prevents the need for time-consuming manual image registration.