title: Automated multidetector computed tomography evaluation of subacutely infarcted myocardium.
abstract: BACKGROUND
  Delayed enhanced (DE) multidetector computed tomography (MDCT) can identify acute and chronic myocardial infarct. To our knowledge, automated techniques for infarct quantification on DE-MDCT have not been used.
  
  
  OBJECTIVE
  We evaluated an automated signal intensity (SI) threshold method for quantification of subacute myocardial infarct and identified and quantified microvascular obstruction (MO) in subacute infarct.
  
  
  METHODS
  DE-MDCT imaging was performed on 5 pigs 6-7 days after mid left anterior descending artery occlusion-reperfusion. DE-MDCT images were compared with triphenyl tetrazolium chloride (TTC) staining for infarct quantification and with hematoxylin and eosin (H&E) staining for MO quantification. Pixels with SI more than the mean SI of a remote normal myocardial region (SI(remote)) plus 2 times the standard deviation (SI(remote) + 2 SD) value were considered infarct pixels. The ratio of infarct to total area of a given slice, the percentage of infarct area per slice (PIS), was calculated. MO as a percentage of total infarct area was also calculated.
  
  
  RESULTS
  The average density values on DE-MDCT (5 minutes after contrast injection) were remote normal myocardium of 93 +/- 19 Hounsfield units (HU), infarct myocardium of 159 +/- 40 HU, blood of 140 +/- 26 HU, and MO of 85 +/- 30 HU. PIS(MDCT) showed substantial agreement with PIS(TTC) (y = 1.003x + 4.12; R = 0.90, P < 0.05). A relation was also shown between MO determined by MDCT compared with H&E staining (y = 0.74x + 3.4).
  
  
  CONCLUSIONS
  We show the feasibility of using a semiautomated SI threshold technique for quantification of subacute myocardial infarct. We also show the persistent MO in subacute myocardial infarct on DE-MDCT images.
authors.affiliation: Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
authors.forename: Balázs
authors.initials: B
authors.lastname: Ruzsics
authors.forename: Pál
authors.initials: P
authors.lastname: Surányi
authors.forename: Pál
authors.initials: P
authors.lastname: Kiss
authors.forename: Brigitta C
authors.initials: BC
authors.lastname: Brott
authors.forename: Satinder S
authors.initials: SS
authors.lastname: Singh
authors.forename: Silvio
authors.initials: S
authors.lastname: Litovsky
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Aban
authors.forename: Steven G
authors.initials: SG
authors.lastname: Lloyd
authors.forename: Tamas
authors.initials: T
authors.lastname: Simor
authors.forename: Gabriel A
authors.initials: GA
authors.lastname: Elgavish
authors.forename: Himanshu
authors.initials: H
authors.lastname: Gupta
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D023421:Models, Animal; D015428:Myocardial Reperfusion Injury / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D013552:Swine; D014057:Tomography, X-Ray Computed / Q000379:methods*