Rozemarijn Vliegenthart1, Carlo N De Cecco2, Julian L Wichmann3, Felix G Meinel4, Gert Jan Pelgrim5, Christian Tesche6, Ullrich Ebersberger7, Francesca Pugliese8, Fabian Bamberg9, Yeon Hyeon Choe10, Yining Wang11, U Joseph Schoepf12. 1. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA; University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. Electronic address: r.vliegenthart@umcg.nl. 2. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA. Electronic address: carlodececco@gmail.com. 3. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA; University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany. Electronic address: docwichmann@gmail.com. 4. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA; Ludwig-Maximilians-University Hospital, Institute for Clinical Radiology, Marchioninistr. 15, 81377, Munich, Germany. Electronic address: fmeinel@med.lmu.de. 5. University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Department of Radiology, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. Electronic address: g.j.pelgrim@umcg.nl. 6. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA; Heart Center Munich-Bogenhausen, Department of Cardiology and Intensive Care Medicine, Munich Municipal Hospital Group, Englschalkinger Str. 77, 81925, Munich, Germany. Electronic address: tesche@musc.edu. 7. Heart Center Munich-Bogenhausen, Department of Cardiology and Intensive Care Medicine, Munich Municipal Hospital Group, Englschalkinger Str. 77, 81925, Munich, Germany. Electronic address: ebersberger@gmx.net. 8. Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Centre for Advanced Cardiovascular Imaging, NIHR Cardiovascular Biomedical Research Unit at Barts, UK William Harvey Research Institute, Barts Heart Centre, 2nd Floor Cardiac Imaging, West Smithfield, London, EC1A 7BE, UK. Electronic address: f.pugliese@qmul.ac.uk. 9. University of Tuebingen, Department of Radiology, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany. Electronic address: fabian.bamberg@uni-tuebingen.de. 10. Samsung Medical Center, Sungkyunkwan University School of Medicine, Department of Radiology, 81 Ilwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. Electronic address: ychoe11@gmail.com. 11. Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Department of Radiology, No.1 Shuaifuyuan, Wangfujing, DongCheng District, Beijing, 100730, China. Electronic address: wangyining@pumch.cn. 12. Medical University of South Carolina, Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, 25 Courtenay Drive, Charleston, SC, 29425, USA; Medical University of South Carolina, Division of Cardiology, Department of Medicine, 25 Courtenay Drive, Charleston, SC, 29425, USA. Electronic address: schoepf@musc.edu.
Abstract
BACKGROUND: To identify patients with early signs of myocardial perfusion reduction, a reference base for perfusion measures is needed. OBJECTIVE: To analyze perfusion parameters derived from dynamic computed tomography perfusion imaging (CTPI) in patients with suspected coronary artery disease (CAD), and relationship with risk factors. METHODS: In this multicenter study, coronary CT angiography (cCTA) and dynamic CTPI were performed by second-generation dual-source CT in patients suspected of CAD. Risk factors were collected from hospital records. Patients with visual perfusion defects on CTPI, previous coronary intervention, or missing risk factor details were excluded. This analysis included 98 patients (mean age ± standard deviation [SD], 59.0 ± 8.6yrs; 73 male). Global measures of left ventricular myocardial blood flow (MBF), myocardial blood volume (MBV) and volume transfer constant (K(trans)) were calculated. RESULTS: Mean MBF was 139.3 ± 31.4 mL/100 mL/min, MBV 19.1 ± 2.7 mL/100 mL, and Ktrans 85.0 ± 17.5 mL/100 mL/min. No significant differences in perfusion parameters were found by gender or age category. Hypertension and diabetes mellitus resulted in lower perfusion parameters (hypertension vs normotension: MBV 18.5 ± 3.0 vs 19.7 ± 2.3 mL/100 mL and K(trans) 82.0 ± 18.0 vs 89.0 ± 16.0, p < 0.05; diabetes vs no diabetes: MBF 128.5 ± 31.5 vs 144.0 ± 30.5 mL/100 mL/min and MBV 17.9 ± 2.4 vs 19.4 ± 2.8 mL/100 mL, p < 0.05). In patients with hyperlipidemia, MBF was higher (146.8 ± 34.4 vs 130.7 ± 24.3 mL/100 mL/min, p < 0.05). Smoking and family history did not show perfusion parameter differences. CONCLUSIONS: Dynamic CTPI identifies early perfusion disturbances in conditions like diabetes and hypertension. With further standardization, absolute perfusion measures may improve CAD risk stratification in patients without visual perfusion defects.
BACKGROUND: To identify patients with early signs of myocardial perfusion reduction, a reference base for perfusion measures is needed. OBJECTIVE: To analyze perfusion parameters derived from dynamic computed tomography perfusion imaging (CTPI) in patients with suspected coronary artery disease (CAD), and relationship with risk factors. METHODS: In this multicenter study, coronary CT angiography (cCTA) and dynamic CTPI were performed by second-generation dual-source CT in patients suspected of CAD. Risk factors were collected from hospital records. Patients with visual perfusion defects on CTPI, previous coronary intervention, or missing risk factor details were excluded. This analysis included 98 patients (mean age ± standard deviation [SD], 59.0 ± 8.6yrs; 73 male). Global measures of left ventricular myocardial blood flow (MBF), myocardial blood volume (MBV) and volume transfer constant (K(trans)) were calculated. RESULTS: Mean MBF was 139.3 ± 31.4 mL/100 mL/min, MBV 19.1 ± 2.7 mL/100 mL, and Ktrans 85.0 ± 17.5 mL/100 mL/min. No significant differences in perfusion parameters were found by gender or age category. Hypertension and diabetes mellitus resulted in lower perfusion parameters (hypertension vs normotension: MBV 18.5 ± 3.0 vs 19.7 ± 2.3 mL/100 mL and K(trans) 82.0 ± 18.0 vs 89.0 ± 16.0, p < 0.05; diabetes vs no diabetes: MBF 128.5 ± 31.5 vs 144.0 ± 30.5 mL/100 mL/min and MBV 17.9 ± 2.4 vs 19.4 ± 2.8 mL/100 mL, p < 0.05). In patients with hyperlipidemia, MBF was higher (146.8 ± 34.4 vs 130.7 ± 24.3 mL/100 mL/min, p < 0.05). Smoking and family history did not show perfusion parameter differences. CONCLUSIONS: Dynamic CTPI identifies early perfusion disturbances in conditions like diabetes and hypertension. With further standardization, absolute perfusion measures may improve CAD risk stratification in patients without visual perfusion defects.
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