Automated alignment of perioperative MRI scans: A technical note and application in pediatric epilepsy surgery

Conventional image registration utilizing brain voxel information may be erroneous in a neurosurgical setting due to pathology and surgery-related anatomical distortions. We report a novel application of an automated image registration procedure based on skull segmentation for magnetic resonance imaging (MRI) scans acquired before, during and after surgery (i.e., perioperative). The procedure was implemented to assist analysis of intraoperative brain shift in 11 pediatric epilepsy surgery cases, each of whom had up to five consecutive perioperative MRI scans. The procedure consisted of the following steps: (1) Skull segmentation using tissue classification tools. (2) Estimation of rigid body transformation between image pairs using registration driven by the skull segmentation. (3) Composition of transformations to provide transformations between each scan and a common space. The procedure was validated using locations of three types of reference structural landmarks: the skull pin sites, the eye positions, and the scalp skin surface, detected using the peak intensity gradient. The mean target registration error (TRE) scores by skull pin sites and scalp skin rendering were around 1 mm and <1 mm, respectively. Validation by eye position demonstrated >1 mm TRE scores, suggesting it is not a reliable reference landmark in surgical scenarios. Comparable registration accuracy was achieved between opened and closed skull scan pairs and closed and closed skull scan pairs. Our procedure offers a reliable registration framework for processing intrasubject time series perioperative MRI data, with potential of improving intraoperative MRI-based image guidance in neurosurgical practice. Hum Brain Mapp 37:3530-3543, 2016. © 2016 Wiley Periodicals, Inc.