title: Planimetry of aortic valve area in aortic stenosis by magnetic resonance imaging.
abstract: BACKGROUND
  The aim of the study was to determine whether noninvasive planimetry of aortic valve area (AVA) by magnetic resonance imaging (MRI) is feasible and reliable in patients with valvular aortic stenosis in comparison to transesophageal echocardiography (TEE) and catheterization.
  
  
  RESULTS
  Planimetry of AVA by MRI (MRI-AVA) was performed on a clinical magnetic resonance system (1.5-T Sonata, Siemens Medical Solutions) in 33 patients and compared with AVA calculated invasively by the Gorlin-formula at catheterization (CATH-AVA, n = 33) as well as to AVA planimetry by multiplane TEE (TEE-AVA, n = 27). Determination of MRI-AVA was possible with an adequate image quality in 82% (27/33), whereas image quality of TEE-AVA was adequate only in 56% (15/27) of patients because of calcification artifacts (P = 0.05). The correlation between MRI-AVA and CATH-AVA was 0.80 (P < 0.0001) and the correlation of MRI-AVA and TEE-AVA was 0.86 (P < 0.0001). MRI-AVA overestimated TEE-AVA by 15% (0.98 +/- 0.31 cm2 vs. 0.85 +/- 0.3 cm2, P < 0.001) and CATH-AVA by 27% (0.94 +/- 0.29 cm2 vs. 0.74 +/- 0.24 cm2, P < 0.0001). Nevertheless, a MRI-AVA below 1,3 cm2 indicated severe aortic stenosis (CATH-AVA < 1 cm2) with a sensitivity of 96% and a specificity of 100% (ROC area 0.98).
  
  
  CONCLUSIONS
  Planimetry of aortic valve area by MRI can be performed with better image quality as compared with TEE. In the clinical management of patients with aortic stenosis, it has to be considered that MRI slightly overestimates aortic valve area as compared with catheterization despite an excellent correlation.
authors.affiliation: Klinik und Poliklinik für Innere Medizin II, Klinikum der Universität, Regensburg, Germany. kurt.debl@klinik.uni-regensburg.de
authors.forename: Kurt
authors.initials: K
authors.lastname: Debl
authors.forename: Behrus
authors.initials: B
authors.lastname: Djavidani
authors.forename: Johannes
authors.initials: J
authors.lastname: Seitz
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Nitz
authors.forename: Franz-Xaver
authors.initials: FX
authors.lastname: Schmid
authors.forename: Frank
authors.initials: F
authors.lastname: Muders
authors.forename: Stefan
authors.initials: S
authors.lastname: Buchner
authors.forename: Stefan
authors.initials: S
authors.lastname: Feuerbach
authors.forename: Günter
authors.initials: G
authors.lastname: Riegger
authors.forename: Andreas
authors.initials: A
authors.lastname: Luchner
mesh_terms: D000368:Aged; D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D001021:Aortic Valve / Q000473:pathology*; D001024:Aortic Valve Stenosis / Q000473:pathology*; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity