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2014 article

Dedicated versus hybrid myocardial perfusion PET: Comparative diagnostic accuracy in a propensity-matched population undergoing coronary angiography

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1764 Objectives Hybrid PET/CT has potential advantages (newer technology, higher resolution, CT for attenuation correction and coronary calcium identification) and disadvantages (expense, challenging registration, higher radiation, need for metal-artifact correction) compared to dedicated PET for Rb-82 myocardial perfusion imaging (MPI). The current study was designed to determine if PET/CT is superior to dedicated PET for diagnosing coronary artery disease (CAD). Methods We identified 557 consecutive patients (excluding those who had paced cardiac rhythms, or were post-CABG or post-MI) who underwent Rb-82 PET MPI on dedicated PET (ACCEL, CTI, Knoxville TN) or hybrid PET/CT (Biograph 16 and 64’s, Siemens Med Systems) scanners followed by coronary angiography (CA) within 60 days. After propensity matching for BMI, diabetes, and prior CAD history, there were 208 study patients (56% male, mean age 70 years, mean BMI 29.3, 23% diabetic and 36% with known CAD, with no significant differences between dedicated (n=104) and hybrid (n=104) populations. Significant CAD was defined as ≥50% stenosis in any major coronary artery by CA. PET MPI ischemia was defined as a summed stress score >3 and a final interpretation of definite ischemia in any major epicardial coronary artery territory. Results Significant CAD was present in 78% patients (dedicated =76%, hybrid=80%, p=0.51), and 44% had multivessel disease (MVD; dedicated =43%, hybrid =44%, p=0.89). Comparative accuracies are shown in Table. In addition, there was no difference in correct recognition of presence of MVD by either modalities. Conclusions Our study examining real-world experience of diagnostic performance of Rb-82 PET for detection of CAD and identification of MVD shows no differences between dedicated PET and hybrid PET/CT.

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Les sujets associés

Medical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical ImagingCardiac Imaging and Diagnostics

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