Cardiac unfold: A novel technique for image-guided cardiac catheterization procedures

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Abstract

X-ray fluoroscopically-guided cardiac catheterization procedures are commonly carried out for the treatment of cardiac arrhythmias, such as atrial fibrillation (AF) and cardiac resynchronization therapy (CRT). X-ray images have poor soft tissue contrast and, for this reason, overlay of a 3D roadmap derived from pre-procedure volumetric image data can be used to add anatomical information. However, current overlay technologies have the limitation that 3D information is displayed on a 2D screen. Therefore, it is not possible for the cardiologist to appreciate the true positional relationship between anatomical/functional data and the position of the interventional devices. We prose a navigation methodology, called cardiac unfold, where an entire cardiac chamber is unfolded from 3D to 2D along with all relevant anatomical and functional information and coupled to real-time device tracking. This would allow more intuitive navigation since the entire 3D scene is displayed simultaneously on a 2D plot. A real-time unfold guidance platform for CRT was developed, where navigation is performed using the standard AHA 16-segment bull's-eye plot for the left ventricle (LV). The accuracy of the unfold navigation was assessed in 13 patient data sets by computing the registration errors of the LV pacing lead electrodes and was found to be 2.2 ± 0.9 mm. An unfold method was also developed for the left atrium (LA) using trimmed B-spline surfaces. The method was applied to 5 patient data sets and its utility was demonstrated for displaying information from delayed enhancement MRI of patients that had undergone radio-frequency ablation. © 2012 Springer-Verlag.

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Ma, Y. L., Karim, R., Housden, R. J., Gijsbers, G., Bullens, R., Rinaldi, C. A., … Rhode, K. S. (2012). Cardiac unfold: A novel technique for image-guided cardiac catheterization procedures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7330 LNCS, pp. 104–114). https://doi.org/10.1007/978-3-642-30618-1_11

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