title: Automated quantification of myocardial perfusion SPECT using simplified normal limits.
abstract: BACKGROUND
  To simplify development of normal limits for myocardial perfusion SPECT (MPS), we implemented a quantification scheme in which normal limits are derived without visual scoring of abnormal scans or optimization of regional thresholds.
  
  
  RESULTS
  Normal limits were derived from same-day TI-201 rest/Tc-99m-sestamibi stress scans of male (n = 40) and female (n = 40) low-likelihood patients. Defect extent, total perfusion deficit (TPD), and regional perfusion extents were derived by comparison to normal limits in polar-map coordinates. MPS scans from 256 consecutive patients without known coronary artery disease, who underwent coronary angiography, were analyzed. The new method of quantification (TPD) was compared with our previously developed quantification system and visual scoring. The receiver operator characteristic area under the curve for detection of 50% or greater stenoses by TPD (0.88 +/- 0.02) was higher than by visual scoring (0.83 +/- 0.03) ( P = .039) or standard quantification (0.82 +/- 0.03) ( P = .004). For detection of 70% or greater stenoses, it was higher for TPD (0.89 +/- 0.02) than for standard quantification (0.85 +/- 0.02) ( P = .014). Sensitivity and specificity were 93% and 79%, respectively, for TPD; 81% and 85%, respectively, for visual scoring; and 80% and 73%, respectively, for standard quantification. The use of stress mode-specific normal limits did not improve performance.
  
  
  CONCLUSIONS
  Simplified quantification achieves performance better than or equivalent to visual scoring or quantification based on per-segment visual optimization of abnormality thresholds.
authors.affiliation: Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. Piotr.Slomka@cshs.org
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.forename: Hidetaka
authors.initials: H
authors.lastname: Nishina
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.forename: Cigdem
authors.initials: C
authors.lastname: Akincioglu
authors.forename: Aiden
authors.initials: A
authors.lastname: Abidov
authors.forename: John D
authors.initials: JD
authors.lastname: Friedman
authors.forename: Sean W
authors.initials: SW
authors.lastname: Hayes
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D002986:Clinical Trials as Topic; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008297:Male; D008875:Middle Aged; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*