title: CT-based diagnosis of diffuse coronary artery disease on the basis of scaling power laws.
abstract: OBJECTIVE
  To provide proof of concept for a diagnostic method to assess diffuse coronary artery disease (CAD) on the basis of coronary computed tomography (CT) angiography.
  
  
  METHODS
  The study was approved by the Cleveland Clinic Institutional Review Board, and all subjects gave informed consent. Morphometric data from the epicardial coronary artery tree, determined with CT angiography in 120 subjects (89 patients with metabolic syndrome and 31 age- and sex-matched control subjects) were analyzed on the basis of the scaling power law. Results obtained in patients with metabolic syndrome and control subjects were compared statistically.
  
  
  RESULTS
  The mean lumen cross-sectional area (ie, lumen cross-sectional area averaged over each vessel of an epicardial coronary artery tree) and sum of intravascular volume in patients with metabolic syndrome (0.039 cm(2) ± 0.015 [standard deviation] and 2.71 cm(3) ± 1.75, respectively) were significantly less than those in control subjects (0.054 cm(2)± 0.015 and 3.29 cm(3)± 1.77, respectively; P < .05). The length-volume power law showed coefficients of 27.0 cm(-4/3) ± 9.0 (R(2) = 0.91 ± 0.08) for patients with metabolic syndrome and 19.9 cm(-4/3) ± 4.3 (R(2) = 0.92 ± 0.07) for control subjects (P < .05). The probability frequency shows that more than 65% of patients with metabolic syndrome had a coefficient of 23 or more for the length-volume scaling power law, whereas approximately 90% of the control subjects had a coefficient of less than 23.
  
  
  CONCLUSIONS
  The retrospective scaling analysis provides a quantitative rationale for diagnosis of diffuse CAD.
authors.affiliation: Department of Biomedical Engineering, IUPUI, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Huo
authors.forename: Thomas
authors.initials: T
authors.lastname: Wischgoll
authors.forename: Jenny Susana
authors.initials: JS
authors.lastname: Choy
authors.forename: Srikanth
authors.initials: S
authors.lastname: Sola
authors.forename: Jose L
authors.initials: JL
authors.lastname: Navia
authors.forename: Shawn D
authors.initials: SD
authors.lastname: Teague
authors.forename: Deepak L
authors.initials: DL
authors.lastname: Bhatt
authors.forename: Ghassan S
authors.initials: GS
authors.lastname: Kassab
mesh_terms: D000465:Algorithms; D017023:Coronary Angiography / Q000706:statistics & numerical data*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009820:Ohio / Q000453:epidemiology; D015995:Prevalence; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*