title: Multislice computed tomography for prediction of optimal angiographic deployment projections during transcatheter aortic valve implantation.
abstract: OBJECTIVE
  This study assessed whether multislice computed tomography (MSCT) could predict optimal angiographic projections for visualizing the plane of the native valve and facilitate accurate positioning during transcatheter aortic valve implantation (TAVI).
  
  
  BACKGROUND
  Accurate device positioning during TAVI depends on valve deployment in angiographic projections perpendicular to the native valve plane, but these may be difficult to determine.
  
  
  METHODS
  Twenty patients underwent MSCT before TAVI. Using a novel technique, multiple angiographic projections accurately representing the native valve plane in multiple axes were determined. The accuracy of all predicted projections was determined post-procedure using angiography according to new criteria, based on valve perpendicularity and the degree of strut overlap (defined as excellent, satisfactory, or poor). The accuracy of valve deployment using MSCT was compared with the results of 20 consecutive patients undergoing TAVI without such MSCT angle prediction.
  
  
  RESULTS
  Correct final deployment projections were more frequent in the MSCT-guided compared with non-MSCT-guided group: excellent or satisfactory projections (90% vs. 65%, p = 0.06). The MSCT angle prediction was accurate but dependent on optimal images (optimal images: 93% of predicted angles were excellent or satisfactory, suboptimal images: 73% of predicted angles were poor). A "line of perpendicularity" could be generated with optimal projections across the right-to-left anterior oblique plane by adding the correct cranial or caudal angulation.
  
  
  CONCLUSIONS
  Pre-procedural MSCT can predict optimal angiographic deployment projections for implantation of transcatheter valves. An ideal deployment angle curve or "line of perpendicularity" can be generated. Understanding and applying these principles improves the accuracy of valve deployment and may improve outcomes.
authors.affiliation: St Paul’s Hospital, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Ronen
authors.initials: R
authors.lastname: Gurvitch
authors.forename: David A
authors.initials: DA
authors.lastname: Wood
authors.forename: Jonathon
authors.initials: J
authors.lastname: Leipsic
authors.forename: Edgar
authors.initials: E
authors.lastname: Tay
authors.forename: Mark
authors.initials: M
authors.lastname: Johnson
authors.forename: Jian
authors.initials: J
authors.lastname: Ye
authors.forename: Fabian
authors.initials: F
authors.lastname: Nietlispach
authors.forename: Namal
authors.initials: N
authors.lastname: Wijesinghe
authors.forename: Anson
authors.initials: A
authors.lastname: Cheung
authors.forename: John G
authors.initials: JG
authors.lastname: Webb
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001024:Aortic Valve Stenosis / Q000000981:diagnostic imaging* / Q000628:therapy*; D001955:British Columbia; D006328:Cardiac Catheterization / Q000295:instrumentation; D016009:Chi-Square Distribution; D005260:Female; D006350:Heart Valve Prosthesis; D019918:Heart Valve Prosthesis Implantation / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome