Semiautomated registration of pre- and intraoperative CT for image-guided percutaneous liver tumor ablation interventions:

Purpose: In CT-guided liver tumor ablation interventions, registration of a preoperative contrast-enhanced CT image to the intraoperative CT image is hypothesized to improve guidance. This is a highly challenging registration task due to differences in patient poses and large deformations, and therefore high registration errors are expected. In this study, our objective is to develop a method that enables users to locally improve the registration where the registration fails, with minimal user interaction. Methods: The method is based on a conventional nonrigid intensity-based registration framework, extended with a novel point-to-surface penalty. The point-to-surface penalty serves to improve the alignment of the liver boundary, while requiring minimal user interaction during the intervention: annotating some points on the liver surface at those regions where the conventional registration seems inaccurate. Results: The method is evaluated on 18 clinical datasets. It improves registration accuracy compared with the conventional nonrigid registration in terms of average surface distance (from 2.75 to 2.05 mm) and target registration error (from 6.92 to 5.8 mm). Conclusions: In this study, we introduce a semiautomated registration algorithm that improves the accuracy of image registration.

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