title: Integrated spatiotemporal analysis for automatic contrast agent inflow detection on angiography and fluoroscopy during transcatheter aortic valve implantation.
abstract: OBJECTIVE
  In this paper, the authors propose an integrated spatial and temporal analysis for automatic detection of contrast agent inflow at the aortic root on fluoroscopic and angiographic sequences during transcatheter aortic valve implantation procedures as a means to automatically trigger registration of 3D aortic models.
  
  
  METHODS
  The proposed contrast agent inflow detection method is based on a contrast feature curve, calculated using histogram analysis and a likelihood ratio test. Several image preprocessing steps are performed to enhance the properties of the contrast feature curve. For sequences with a dominant peak on its contrast feature curve, a cascaded classifier is then applied to differentiate the contrast-enhanced aorta from contrast-enhanced balloons. Finally, a multilayer classifier based on sparse Gabor features is used to recognize sequences containing a faint contrast-enhanced aorta.
  
  
  RESULTS
  The algorithm was evaluated using 105 sequences consisting of more than 12,000 frames, and achieved a detection accuracy of 99.1% (100% sensitivity and 98.5% specificity). The computation time for a typical sequence of 150 frames was ≈ 1 s on a single-core Dell PC with a 1 GHz Intel Xeon processor and 2 GB of RAM.
  
  
  CONCLUSIONS
  The authors developed a novel, automatic method for contrast agent inflow detection on x-ray sequences. With the achieved efficiency and accuracy, the proposed method is potentially feasible for clinical use as it facilitates a seamless workflow in utilizing patient-specific 3D models to provide anatomical details during transcatheter aortic valve implantation procedures.
authors.affiliation: Imaging and Computer Vision, Siemens Corporation, Corporate Technology, Princeton, New Jersey 08540, USA. rui.liao@siemens.com
authors.forename: Rui
authors.initials: R
authors.lastname: Liao
authors.forename: Wei
authors.initials: W
authors.lastname: You
authors.forename: Yinxiao
authors.initials: Y
authors.lastname: Liu
authors.forename: Michelle
authors.initials: M
authors.lastname: Yan
authors.forename: Matthias
authors.initials: M
authors.lastname: John
authors.forename: Steven
authors.initials: S
authors.lastname: Shea
mesh_terms: D000792:Angiography / Q000379:methods*; D001021:Aortic Valve / Q000000981:diagnostic imaging / Q000378:metabolism / Q000601:surgery; D000082882:Bicuspid Aortic Valve Disease; D006328:Cardiac Catheterization / Q000379:methods*; D003287:Contrast Media / Q000493:pharmacokinetics*; D006330:Heart Defects, Congenital / Q000000981:diagnostic imaging / Q000378:metabolism* / Q000601:surgery*; D006349:Heart Valve Diseases / Q000000981:diagnostic imaging / Q000378:metabolism* / Q000601:surgery*; D019918:Heart Valve Prosthesis Implantation / Q000379:methods*; D006801:Humans; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D018511:Systems Integration
chemical_list: D003287:Contrast Media