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Semiautomated registration of pre‐ and intraoperative CT for image‐guided percutaneous liver tumor ablation interventions
Author(s) -
Gunay Gokhan,
Luu Manh Ha,
Moelker Adriaan,
Walsum Theo,
Klein Stefan
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12332
Subject(s) - image registration , artificial intelligence , computer science , ablation , computer vision , image guided surgery , feature (linguistics) , medicine , image (mathematics) , linguistics , philosophy
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.