title: Image-based computational models for TAVI planning: from CT images to implant deployment.
abstract: Transcatheter aortic valve implantation (TAVI) is becoming the standard choice of care for non-operable patients suffering from severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, accurate preoperative planning is crucial for a successful outcome. The most important decision during planning is selecting the proper implant type and size. Due to the wide variety in device sizes and types and non-circular annulus shapes, there is often no obvious choice for the specific patient. Most clinicians base their final decision on their previous experience. As a first step towards a more predictive planning, we propose an integrated method to estimate the aortic apparatus from CT images and compute implant deployment. Aortic anatomy, which includes aortic root, leaflets and calcifications, is automatically extracted using robust modeling and machine learning algorithms. Then, the finite element method is employed to calculate the deployment of a TAVI implant inside the patient-specific aortic anatomy. The anatomical model was evaluated on 198 CT images, yielding an accuracy of 1.30 +/- 0.23 mm. In eleven subjects, pre- and post-TAVI CT images were available. Errors in predicted implant deployment were of 1.74 +/- 0.40 mm in average and 1.32 mm in the aortic valve annulus region, which is almost three times lower than the average gap of 3 mm between consecutive implant sizes. Our framework may thus constitute a surrogate tool for TAVI planning.
authors.affiliation: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors.forename: Sasa
authors.initials: S
authors.lastname: Grbic
authors.affiliation: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors.forename: Tommaso
authors.initials: T
authors.lastname: Mansi
authors.affiliation: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors.forename: Razvan
authors.initials: R
authors.lastname: Ionasec
authors.affiliation: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors.forename: Ingmar
authors.initials: I
authors.lastname: Voigt
authors.affiliation: Siemens, Healthcare Sector, Mountain View, USA.
authors.forename: Helene
authors.initials: H
authors.lastname: Houle
authors.affiliation: Siemens AG, Healthcare Sector, Forchheim, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: John
authors.affiliation: Siemens AG, Healthcare Sector, Forchheim, Germany.
authors.forename: Max
authors.initials: M
authors.lastname: Schoebinger
authors.affiliation: Computer Aided Medical Procedures, Technical University Munich, Germany.
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
authors.affiliation: Imaging and Computer Vision, Siemens Corporate Research, Princeton, USA.
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D001024:Aortic Valve Stenosis / Q000000981:diagnostic imaging* / Q000601:surgery*; D003198:Computer Simulation; D019918:Heart Valve Prosthesis Implantation / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D011300:Preoperative Care / Q000379:methods*; D019919:Prosthesis Implantation / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*