Julian L Wichmann1, Felix G Meinel2, U Joseph Schoepf3, Akos Varga-Szemes4, Giuseppe Muscogiuri5, Paola M Cannaò6, Andrew D McQuiston4, Yeon Hyeon Choe7, Yining Wang8, Carlo N De Cecco5. 1. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425; Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. 2. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425; Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany. 3. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425; Division of Cardiology, Department of Medicine, Medical University of South Carolina, Ashley River Tower, MSC 592, 25 Courtenay Drive, Charleston, SC 29425. Electronic address: schoepf@musc.edu. 4. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425. 5. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Drive, Charleston, SC 29425; Department of Radiological Sciences, Oncology and Pathology, University of Rome "Sapienza"-Polo Pontino, Latina, Italy. 6. Department of Radiological Sciences, Oncology and Pathology, University of Rome "Sapienza"-Polo Pontino, Latina, Italy; Scuola di Specializzazione in Radiodiagnostica, University of Milan, Milano, Italy. 7. Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 8. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Abstract
RATIONALE AND OBJECTIVES: To evaluate the diagnostic accuracy of semiautomated global quantification of left ventricular myocardial perfusion derived from stress dynamic computed tomography myocardial perfusion imaging (CTMPI) for detection of territorial perfusion deficits (PD). MATERIALS AND METHODS: Dynamic CTMPI datasets of 71 patients were analyzed using semiautomated volume-based software to calculate global myocardial blood flow (MBF), myocardial blood volume, and volume transfer constant. Optimal cutoff values to assess the diagnostic accuracy of these parameters for detection of one- to three-vessel territories with PD in comparison to visual analysis were calculated. RESULTS: Nonsignificant differences (P = 0.694) were found for average global MBF in patients without PD and single-territorial PD. Significant differences were found for mean global MBF in patients with PD in two (P < 0.0058) and three territories (P < 0.0003). Calculated optimal thresholds for global MBF and myocardial blood volume resulted in a sensitivity, specificity, and negative predictive value of 100% for detection of three-vessel territory PD. For detection of ≥2 territories with PD, global MBF was superior to other parameters (sensitivity 81.3%, specificity 90.9%, and negative predictive value 94.3%). CONCLUSIONS: Semiautomated global quantification of left ventricular MBF during stress dynamic CTMPI shows high diagnostic accuracy for detection of ≥2 vessel territories with PD, facilitating identification of patients with multi-territorial myocardial PD.
RATIONALE AND OBJECTIVES: To evaluate the diagnostic accuracy of semiautomated global quantification of left ventricular myocardial perfusion derived from stress dynamic computed tomography myocardial perfusion imaging (CTMPI) for detection of territorial perfusion deficits (PD). MATERIALS AND METHODS: Dynamic CTMPI datasets of 71 patients were analyzed using semiautomated volume-based software to calculate global myocardial blood flow (MBF), myocardial blood volume, and volume transfer constant. Optimal cutoff values to assess the diagnostic accuracy of these parameters for detection of one- to three-vessel territories with PD in comparison to visual analysis were calculated. RESULTS: Nonsignificant differences (P = 0.694) were found for average global MBF in patients without PD and single-territorial PD. Significant differences were found for mean global MBF in patients with PD in two (P < 0.0058) and three territories (P < 0.0003). Calculated optimal thresholds for global MBF and myocardial blood volume resulted in a sensitivity, specificity, and negative predictive value of 100% for detection of three-vessel territory PD. For detection of ≥2 territories with PD, global MBF was superior to other parameters (sensitivity 81.3%, specificity 90.9%, and negative predictive value 94.3%). CONCLUSIONS: Semiautomated global quantification of left ventricular MBF during stress dynamic CTMPI shows high diagnostic accuracy for detection of ≥2 vessel territories with PD, facilitating identification of patients with multi-territorial myocardial PD.