Translator Disclaimer
21 March 2016 Semi-automated registration of pre- and intra-operative liver CT for image-guided interventions
Author Affiliations +
Percutaneous radio frequency ablation is a method for liver tumor treatment when conventional surgery is not an option. It is a minimally invasive treatment and may be performed under CT image guidance if the tumor does not give sufficient contrast on ultrasound images. For optimal guidance, registration of the pre-operative contrast-enhanced CT image to the intra-operative CT image is hypothesized to improve guidance. This is a highly challenging registration task due to large differences in pose and image quality. In this study, we introduce a semi-automated registration algorithm to address this problem. The method is based on a conventional nonrigid intensity-based registration framework, extended with a novel point-to-surface constraint. The point-to-surface constraint serves to improve the alignment of the liver boundary, while requiring minimal user interaction during the operation. The method assumes that a liver segmentation of the pre-operative CT is available. After an initial nonrigid registration without the point-to-surface constraint, the operator clicks a few points on the liver surface at those regions where the nonrigid registration seems inaccurate. In a subsequent registration step, these points on the intra-operative image are driven towards the liver surface on the preoperative image, using a penalty term added to the registration cost function. The method is evaluated on five clinical datasets and it is shown to improve registration compared with conventional rigid and nonrigid registrations in all cases.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gokhan Gunay, Luu Manh Ha, Theo van Walsum, and Stefan Klein "Semi-automated registration of pre- and intra-operative liver CT for image-guided interventions", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841N (21 March 2016);

Back to Top