title: Pericardium based model fusion of CT and non-contrasted C-arm CT for visual guidance in cardiac interventions.
abstract: 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.
authors.forename: Yefeng
authors.initials: Y
authors.lastname: Zheng
mesh_terms: D001021:Aortic Valve / Q000000981:diagnostic imaging / Q000601:surgery; D000082882:Bicuspid Aortic Valve Disease; D003198:Computer Simulation; D005471:Fluoroscopy / Q000379:methods*; D006330:Heart Defects, Congenital / Q000000981:diagnostic imaging* / Q000601:surgery*; D006349:Heart Valve Diseases / Q000000981:diagnostic imaging* / Q000601:surgery*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods; D010496:Pericardium / Q000000981:diagnostic imaging* / Q000601:surgery; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015641:Radiography, Interventional / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D025321:Surgery, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods*; D065467:Transcatheter Aortic Valve Replacement / Q000379:methods*