title: Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT.
abstract: BACKGROUND
  Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation.
  
  
  RESULTS
  A total of 492 patients with known or suspected coronary artery disease underwent both stress and rest MPS. The proposed perfusion analysis algorithm (Segment) was trained in 140 patients and validated in the remaining 352 patients using visual scoring in MPS by an expert reader as reference standard. Furthermore, validation was performed with simulated perfusion defects in an anthropomorphic computer model. Total perfusion deficit (TPD, range 0-100), including both extent and severity of the perfusion defect, was used as the global measurement of the perfusion defects. Mean bias ± SD between TPD by Segment and the simulated TPD was 3.6 ± 3.8 (R(2) = 0.92). Mean bias ± SD between TPD by Segment and the visual scoring in the patients was 1.2 ± 2.9 (R (2)= 0.64) for stress-induced ischemia and -0.3 ± 3.1 (R(2) = 0.86) for infarction.
  
  
  CONCLUSIONS
  The proposed algorithm can detect and quantify perfusion defects in MPS with good agreement to expert readers and to simulated values in a computer phantom.
authors: Department of Clinical Physiology and Nuclear Medicine, Lund University, Lund University Hospital, 221 85, Lund, Sweden, helen.fransson@med.lu.se.
authors: Helen
authors: H
authors: Fransson
authors: Michael
authors: M
authors: Ljungberg
authors: Marcus
authors: M
authors: Carlsson
authors: Henrik
authors: H
authors: Engblom
authors: Håkan
authors: H
authors: Arheden
authors: Einar
authors: E
authors: Heiberg
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003324:Coronary Artery Disease / Q000150:complications / Q000000981:diagnostic imaging*; D005080:Exercise Test / Q000379:methods; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000000981:diagnostic imaging* / Q000209:etiology; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016037:Single-Blind Method; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*