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Published on: February 10, 2012
Dynamic Computed Tomography Myocardial Perfusion Imaging: Comparison of Clinical Analysis Methods for the Detection
Alexia Rossi1, Andrew Wragg1, Ernst Klotz1
1From the Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom and Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (A.R., A.W., F. Pirro, F. Pugliese); Siemens Healthineers, Forchheim, Germany (E.K.); Institute of Cardiovascular Science, University College London, United Kingdom (J.C.M.); and Departments of Cardiology and Radiology, Erasmus MC University Medical Centre Rotterdam, The Netherlands (K.N.).
Insights
Semiautomatic analysis of myocardial perfusion imaging shows improved accuracy in diagnosing ischemia compared to fully automatic methods. Endocardial readings are particularly effective for identifying coronary artery disease in stable angina patients.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Medical Diagnostics
Background:
- Clinical analysis of myocardial dynamic computed tomography myocardial perfusion imaging lacks standardization.
- Accurate diagnosis of ischemia is crucial for patients with stable angina.
Purpose of the Study:
- To compare different analysis approaches for diagnosing ischemia using myocardial perfusion imaging.
- To evaluate the diagnostic performance of fully automatic versus semiautomatic analysis methods.
Main Methods:
- Prospective study involving 115 patients with stable angina undergoing adenosine-stress dynamic computed tomography myocardial perfusion imaging.
- Quantitative perfusion parameters calculated using parametric deconvolution.
- Comparison of fully automatic (17-segment model) and semiautomatic (manual sampling) analysis methods.
- Invasive coronary angiography used as the reference standard for ischemia.
Main Results:
- Semiautomatic analysis demonstrated superior performance in predicting ischemia compared to fully automatic analysis (AUC 0.87 vs. 0.69, P<0.001).
- Endocardial readings showed better diagnostic accuracy than epicardial readings (AUC 0.87 vs. 0.72, P<0.001).
- No significant difference in performance between relative and absolute blood flow measurements (AUC 0.90 vs. 0.87, P=ns).
Conclusions:
- Semiautomatic analysis of endocardial perfusion parameters in dynamic computed tomography myocardial perfusion imaging robustly discriminates between ischemic and non-ischemic coronary vessels.
- This approach offers improved diagnostic accuracy for stable angina patients.
Background:
The clinical analysis of myocardial dynamic computed tomography myocardial perfusion imaging lacks standardization. The objective of this prospective study was to compare different analysis approaches to diagnose ischemia in patients with stable angina referred for invasive coronary angiography.
Methods And Results:
Patients referred for evaluation of stable angina symptoms underwent adenosine-stress dynamic computed tomography myocardial perfusion imaging with a second-generation dual-source scanner. Quantitative perfusion parameters, such as blood flow, were calculated by parametric deconvolution for each myocardial voxel. Initially, perfusion parameters were extracted according to standard 17-segment model of the left ventricle (fully automatic analysis). These were then manually sampled by an operator (semiautomatic analysis). Areas under the receiver-operating characteristic curves of the 2 different approaches were compared. Invasive fractional flow reserve ≤0.80 or diameter stenosis ≥80% on quantitative coronary angiography was used as reference standard to define ischemia. We enrolled 115 patients (88 men; age 57±9 years). There were 72 of 286 (25%) vessels causing ischemia in 52 of 115 (45%) patients. The semiautomatic analysis method was better than the fully automatic method at predicting ischemia (areas under the receiver-operating characteristic curves, 0.87 versus 0.69; P<0.001) with readings obtained in the endocardial myocardium performing better than those in the epicardial myocardium (areas under the receiver-operating characteristic curves, 0.87 versus 0.72; P<0.001). The difference in performance between blood flow, expressed as relative to remote myocardium, and absolute blood flow was not statistically significant (areas under the receiver-operating characteristic curves, 0.90 versus 0.87; P=ns).
Conclusions:
Endocardial perfusion parameters obtained by semiautomatic analysis of dynamic computed tomography myocardial perfusion imaging may permit robust discrimination between coronary vessels causing ischemia versus not causing ischemia.
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