Pericardium Based Model Fusion of CT and Non-contrasted C-arm CT for Visual Guidance in Cardiac Interventions

  • Yefeng Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


Minimally invasive transcatheter cardiac interventions are being adopted rapidly to treat a range of cardiovascular diseases. Pre-operative imaging, e.g., computed tomography (CT), plays an important role in surgical planning and simulation of cardiac interventions. Overlaying a 3D cardiac model extracted from pre-operative images onto real-time fluoroscopic images provides valuable visual guidance during the intervention. However, direct 3D to 2D fusion is difficult and may require quite amounts of user interaction. Intra-operative non-contrasted C-arm CT can be used as an intermedium for model fusion. The cardiac model is first warped to C-arm CT and later overlaid onto fluoroscopy. The C-arm CT to fluoroscopy overlay is straightforward since both images are captured on the same machine and the C-arm projection geometry can be directly used for overlay. Though various image registration methods may be used to fuse pre-operative images and C-arm CT, cross-modality image registration is not robust due to the significant difference in image characteristics (contrasted vs. non-contrasted). In this work we propose a model based fusion method using the pericardium to align pre-operative CT to intra-operative C-arm CT. After automatic segmentation of the pericardium in both CT and C-arm CT, the deformation field is estimated and then applied to warp the cardiac model extracted from CT to C-arm CT. The proposed method can be applied to fuse different cardiac models (e.g., chambers, aorta, coronary arteries, and cardiac valves). A feasibility study on aortic root model fusion shows that a reasonable accuracy can be achieved using a generic model (from a different patient), while more accurate results come from a patient-specific model. Intelligently weighted fusion can further improve the accuracy by using all available cardiac models in a pre-collected training set.


Aortic Root Image Registration Transcatheter Aortic Valve Implantation Model Fusion Cardiac Intervention 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yefeng Zheng
    • 1
  1. 1.Imaging and Computer Vision, Siemens Corporation, Corporate TechnologyPrincetonUSA

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