title: Quantification of the myocardial area at risk using coronary CT angiography and Voronoi algorithm-based myocardial segmentation.
abstract: OBJECTIVE
  The purpose of this study was to estimate the myocardial area at risk (MAAR) using coronary computed tomography angiography (CTA) and Voronoi algorithm-based myocardial segmentation in comparison with single-photon emission computed tomography (SPECT).
  
  
  METHODS
  Thirty-four patients with coronary artery disease underwent 128-slice coronary CTA, stress/rest thallium-201 SPECT, and coronary angiography (CAG). CTA-based MAAR was defined as the sum of all CAG stenosis (>50%) related territories (the ratio of the left ventricular volume). Using automated quantification software (17-segment model, 5-point scale), SPECT-based MAAR was defined as the number of segments with a score above zero as compared to the total 17 segments by summed stress score (SSS), difference (SDS) score map, and comprehensive SPECT interpretation with either SSS or SDS best correlating CAG findings (SSS/SDS). Results were compared using Pearson's correlation coefficient.
  
  
  RESULTS
  Forty-nine stenoses were observed in 102 major coronary territories. Mean value of CTA-based MAAR was 28.3 ± 14.0%. SSS-based, SDS-based, and SSS/SDS-based MAAR was 30.1 ± 6.1%, 20.1 ± 15.8%, and 26.8 ± 15.7%, respectively. CTA-based MAAR was significantly related to SPECT-based MAAR (r = 0.531 for SSS; r = 0.494 for SDS; r = 0.814 for SSS/SDS; P < 0.05 in each).
  
  
  CONCLUSIONS
  CTA-based Voronoi algorithm myocardial segmentation reliably quantifies SPECT-based MAAR.
  
  
  CONCLUSIONS
  • Voronoi algorithm allows for three-dimensional myocardial segmentation of coronary CT angiography • Stenosis-related CT myocardial territories correlate to SPECT based area at risk • CT angiography myocardial segmentation may assist in clinical decision-making.
authors.affiliation: Department of Radiology, Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam, 3015CE, The Netherlands, akuratasan2000@gmail.com.
authors.forename: Akira
authors.initials: A
authors.lastname: Kurata
authors.forename: Atsushi
authors.initials: A
authors.lastname: Kono
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Sakamoto
authors.forename: Teruhito
authors.initials: T
authors.lastname: Kido
authors.forename: Teruhito
authors.initials: T
authors.lastname: Mochizuki
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Higashino
authors.forename: Mitsunori
authors.initials: M
authors.lastname: Abe
authors.forename: Adriaan
authors.initials: A
authors.lastname: Coenen
authors.forename: Raluca G
authors.initials: RG
authors.lastname: Saru-Chelu
authors.forename: Pim J
authors.initials: PJ
authors.lastname: de Feyter
authors.forename: Gabriel P
authors.initials: GP
authors.lastname: Krestin
authors.forename: Koen
authors.initials: K
authors.lastname: Nieman
mesh_terms: D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008297:Male; D061330:Multidetector Computed Tomography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012984:Software; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods