Paper
8 March 2019 Multimodal image registration of pre- and intra-interventional data for surgical planning of transarterial chemoembolisation
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Abstract
Multimodal registration improves surgical planning and the performance of interventional procedures such as transarterial chemoembolizations (TACE), since it allows to combine complementary information provided by pre- and intrainterventional data about tumor localization and access. However, no registration methods specifically developed for the multimodal registration of abdominal scans exist and as a result only general-purpose methods are available for this application. In this paper, we evaluate and optimize the performance of three standard registration methods which rely on different similarity metrics, namely Advanced Mattes Mutual Information (AMMI), Advanced Normalized Correlation (ANC) and Normalized Mutual Information (NMI), for the registration of preinterventional T1- and T2-weighted MRI to preinterventional CT as well as intrainterventional Cone Beam CT (CBCT) to preinterventional CT of the liver. Moreover, different variants of the registration algorithms, based on the introduction of masks and different resolution levels in multistage registrations, are investigated. To evaluate the performance of each registration method, the capture range was estimated based on the calculation of the mean target registration error.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Barbara Waldkirch, Sandy Engelhardt, Frank G. Zöllner, Lothar R. Schad, and Ivo Wolf "Multimodal image registration of pre- and intra-interventional data for surgical planning of transarterial chemoembolisation", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512U (8 March 2019); https://doi.org/10.1117/12.2513664
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KEYWORDS
Image registration

Liver

Computed tomography

Magnetic resonance imaging

Rigid registration

Image-guided intervention

Multimodal imaging

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