PURPOSE: Previous studies using dynamic perfusion CT and volume perfusion CT (VPCT) software consistently underestimated the stress myocardial blood flow (MBF) in normal myocardium to be 1.1-1.4 ml/min/g, whilst the O 15-water PET studies demonstrated the normal stress MBF of 3-5 ml/min/g. We hypothesized that the MBF determined by VPCT (MBF-VPCT) is actually presenting the blood-to-myocardium transfer constant, K1. In this study, we determined K1 using Patlak plot (K1-Patlak) and compared the results with MBF-VPCT. MATERIAL AND METHODS: 17 patients (66 ± 9 years, 7 males) with suspected coronary artery disease (CAD) underwent stress dynamic perfusion CT, followed by rest coronary CT angiography (CTA). Arterial input and myocardial output curves were analyzed with Patlak plot to quantify myocardial K1. Significant CAD was defined as >50% stenosis on CTA. A simulation study was also performed to investigate the influence of limited temporal sampling in dynamic CT acquisition on K1 using the undersampling data generated from MRI. RESULTS: There were 3 patients with normal CTA, 7 patients with non-significant CAD, and 7 patients with significant CAD. K1-patlak was 0.98 ± 0.35 (range 0.22-1.67) ml/min/g, whereas MBF-VPCT was 0.83 ± 0.23 (range 0.34-1.40) ml/min/g. There was a linear relationship between them: (MBF-VPCT) = 0.58 x (K1-patlak) + 0.27 (r(2) = 0.65, p < 0.001). The simulation study done on MRI data demonstrated that Patlak plot substantially underestimated true K1 by 41% when true K1 was 2.0 ml/min/g with the temporal sampling of 2RR for arterial input and 4RR for myocardial output functions. CONCLUSIONS: The results of our study are generating hypothesis that MBF-VPCT is likely to be calculating K1-patlak equivalent, not MBF. In addition, these values may be substantially underestimated because of limited temporal sampling rate.
PURPOSE: Previous studies using dynamic perfusion CT and volume perfusion CT (VPCT) software consistently underestimated the stress myocardial blood flow (MBF) in normal myocardium to be 1.1-1.4 ml/min/g, whilst the O 15-water PET studies demonstrated the normal stress MBF of 3-5 ml/min/g. We hypothesized that the MBF determined by VPCT (MBF-VPCT) is actually presenting the blood-to-myocardium transfer constant, K1. In this study, we determined K1 using Patlak plot (K1-Patlak) and compared the results with MBF-VPCT. MATERIAL AND METHODS: 17 patients (66 ± 9 years, 7 males) with suspected coronary artery disease (CAD) underwent stress dynamic perfusion CT, followed by rest coronary CT angiography (CTA). Arterial input and myocardial output curves were analyzed with Patlak plot to quantify myocardial K1. Significant CAD was defined as >50% stenosis on CTA. A simulation study was also performed to investigate the influence of limited temporal sampling in dynamic CT acquisition on K1 using the undersampling data generated from MRI. RESULTS: There were 3 patients with normal CTA, 7 patients with non-significant CAD, and 7 patients with significant CAD. K1-patlak was 0.98 ± 0.35 (range 0.22-1.67) ml/min/g, whereas MBF-VPCT was 0.83 ± 0.23 (range 0.34-1.40) ml/min/g. There was a linear relationship between them: (MBF-VPCT) = 0.58 x (K1-patlak) + 0.27 (r(2) = 0.65, p < 0.001). The simulation study done on MRI data demonstrated that Patlak plot substantially underestimated true K1 by 41% when true K1 was 2.0 ml/min/g with the temporal sampling of 2RR for arterial input and 4RR for myocardial output functions. CONCLUSIONS: The results of our study are generating hypothesis that MBF-VPCT is likely to be calculating K1-patlak equivalent, not MBF. In addition, these values may be substantially underestimated because of limited temporal sampling rate.
Authors: Gert Jan Pelgrim; Marco Das; Sjoerd van Tuijl; Marly van Assen; Frits W Prinzen; Marco Stijnen; Matthijs Oudkerk; Joachim E Wildberger; Rozemarijn Vliegenthart Journal: Int J Cardiovasc Imaging Date: 2017-05-23 Impact factor: 2.357
Authors: Sara Seitun; Cecilia De Lorenzi; Filippo Cademartiri; Angelo Buscaglia; Nicole Travaglio; Manrico Balbi; Gian Paolo Bezante Journal: Biomed Res Int Date: 2018-10-14 Impact factor: 3.411