Abstract
Accurate catheter tracking is crucial during Minimally Invasive Endovascular Procedures (MIEP), and Electromagnetic (EM) tracking is a widely used technology that serves this purpose. However, registration between preoperative images and the EM tracking system is often challenging. Existing registration methods typically require manual interactions, which can be time-consuming, increase the risk of errors and change the procedural workflow. Although several registration methods are available for catheter tracking, such as marker-based and path-based approaches, their limitations can impact the accuracy of the resulting tracking solution, consequently, the outcome of the medical procedure.
This paper introduces a novel automated catheter registration method for EM-guided MIEP. The method utilizes 3D signal temporal analysis, such as Dynamic Time Warping (DTW) algorithms, to improve registration accuracy and reliability compared to existing methods. DTW can accurately warp and match EM-tracked paths to the vessel’s centerline, making it particularly suitable for registration. The introduced registration method is evaluated for accuracy in a vascular phantom using a marker-based registration as the ground truth. The results indicate that the DTW method yields accurate and reliable registration outcomes, with a mean error of 2.22 mm. The introduced registration method presents several advantages over state-of-the-art methods, such as high registration accuracy, no initialization required, and increased automation.
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Acknowledgment
The project was funded by the Bavarian State Ministry of Science and Arts within the framework of the “Digitaler Herz-OP” project under grant number 1530/891 02. We also thank ImFusion GmbH and BrainLab AG for their software support and valuable interactions.
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Ramadani, A., Ewert, P., Schunkert, H., Navab, N. (2023). WarpEM: Dynamic Time Warping for Accurate Catheter Registration in EM-Guided Procedures. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_75
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