title: Adenosine stress 64- and 256-row detector computed tomography angiography and perfusion imaging: a pilot study evaluating the transmural extent of perfusion abnormalities to predict atherosclerosis causing myocardial ischemia.
abstract: BACKGROUND
  Multidetector computed tomography coronary angiography (CTA) is a robust method for the noninvasive diagnosis of coronary artery disease. However, in its current form, CTA is limited in its prediction of myocardial ischemia. The purpose of this study was to test whether adenosine stress computed tomography myocardial perfusion imaging (CTP), when added to CTA, can predict perfusion abnormalities caused by obstructive atherosclerosis.
  
  
  RESULTS
  Forty patients with a history of abnormal single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) underwent adenosine stress 64-row (n=24) or 256-row (n=16) detector CTP and CTA. A subset of 27 patients had invasive angiography available for quantitative coronary angiography. CTA and quantitative coronary angiography were evaluated for stenoses > or =50%, and SPECT-MPI was evaluated for fixed and reversible perfusion deficits using a 17-segment model. CTP images were analyzed for the transmural differences in perfusion using the transmural perfusion ratio (subendocardial attenuation density/subepicardial attenuation density). The sensitivity, specificity, positive predictive value, and negative predictive value for the combination of CTA and CTP to detect obstructive atherosclerosis causing perfusion abnormalities using the combination of quantitative coronary angiography and SPECT as the gold standard was 86%, 92%, 92%, and 85% in the per-patient analysis and 79%, 91%, 75%, and 92% in the per vessel/territory analysis, respectively.
  
  
  CONCLUSIONS
  The combination of CTA and CTP can detect atherosclerosis causing perfusion abnormalities when compared with the combination of quantitative coronary angiography and SPECT.
authors.affiliation: Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
authors.forename: Richard T
authors.initials: RT
authors.lastname: George
authors.forename: Armin
authors.initials: A
authors.lastname: Arbab-Zadeh
authors.forename: Julie M
authors.initials: JM
authors.lastname: Miller
authors.forename: Kakuya
authors.initials: K
authors.lastname: Kitagawa
authors.forename: Hyuk-Jae
authors.initials: HJ
authors.lastname: Chang
authors.forename: David A
authors.initials: DA
authors.lastname: Bluemke
authors.forename: Lewis
authors.initials: L
authors.lastname: Becker
authors.forename: Omair
authors.initials: O
authors.lastname: Yousuf
authors.forename: John
authors.initials: J
authors.lastname: Texter
authors.forename: Albert C
authors.initials: AC
authors.lastname: Lardo
authors.forename: João A C
authors.initials: JA
authors.lastname: Lima
mesh_terms: D000241:Adenosine / Q000008:administration & dosage; D000368:Aged; D017023:Coronary Angiography / Q000379:methods*; D003326:Coronary Circulation; D023921:Coronary Stenosis / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007262:Infusions, Intravenous; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000209:etiology* / Q000503:physiopathology; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D015588:Observer Variation; D010865:Pilot Projects; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D015899:Tomography, Emission-Computed, Single-Photon; D014057:Tomography, X-Ray Computed
chemical_list: D000241:Adenosine