title: Robust model-based 3d/3D fusion using sparse matching for minimally invasive surgery.
abstract: Classical surgery is being disrupted by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm CT and C-arm fluoroscopy are routinely used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel sparse matching approach for fusing high quality pre-operative CT and non-contrasted, non-gated intra-operative C-arm CT by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the pre-operative CT and mapped to the intra-operative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments demonstrate that our model-based fusion approach has an average execution time of 2.9 s, while the accuracy lies within expert user confidence intervals.
authors: Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany
authors: Dominik
authors: D
authors: Neumann
authors: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors: Sasa
authors: S
authors: Grbic
authors: Siemens AG, Healthcare Sector, Forchheim,Germany.
authors: Matthias
authors: M
authors: John
authors: Computer Aided Medical Procedures, Technical University Munich, Germany.
authors: Nassir
authors: N
authors: Navab
authors: Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany
authors: Joachim
authors: J
authors: Hornegger
authors: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors: Razvan
authors: R
authors: Ionasec
mesh_terms: D000465:Algorithms; D013504:Cardiovascular Surgical Procedures / Q000379:methods*; D003198:Computer Simulation; D017023:Coronary Angiography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019060:Minimally Invasive Surgical Procedures / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*