Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging.

PURPOSE: To assess the intermodel agreement of different tracer kinetic models to determine myocardial blood flow (MBF) and their diagnostic accuracy in coronary artery disease (CAD) at dynamic CT myocardial perfusion imaging (CTMPI). METHODS: Three porcine hearts perfused in Langendorff mode and 15 patients with suspected CAD and perfusion single photon emission CT (SPECT) were included. Dynamic CTMPI was performed in shuttle-mode (70 kVp, 350mAs/rot) on 3rd generation dual-source CT. In porcine hearts and patients, myocardial segments (AHA-16-segment model) were drawn. Tissue attenuation curves were constructed per segment and arterial input functions were derived from the aorta. True MBF was calculated with input flow and weight of the porcine hearts. In patients, ischemic segments were based on SPECT results. MBF quantification was performed using the VPCT-software, Upslope, Extended Toft (ET), Two-compartment (TC) and Fermi models. RESULTS: In porcine hearts, true MBF was 1.88 (interquartile range [IQR]:1.80-2.80)mL/g/min. Diagnostic accuracy was similar for all models: 0.96, 0.99, 0.92, 0.93 and 0.96 for VPCT software, Upslope method, Fermi, ET and TC model. The VPCT software and Upslope method resulted in lower MBF (median 1.44 [1.29-1.58] and 1.39 [1.25-1.59]mL/g/min) compared to the Fermi, ET, and TC model (median values of 1.76 mL/g/min [1.36-2.44], 2.55 mL/g/min [2.20-2.92], and 1.98 mL/g/min [1.60-2.60], respectively [p < 0.001]). In patients, all models showed a significant difference in MBF between the 34 ischemic and 206 non-ischemic segments (p-value<0.001). CONCLUSION: Absolute MBF values are significantly different between the models and a uniform threshold could not be determined; however, diagnostic accuracy for detecting ischemia is similar.

Analysis of myocardial perfusion parameters in an ex-vivo porcine heart model using third generation dual-source CT.

PURPOSE: To evaluate the relationship between fractional flow reserve (FFR)-determined coronary artery stenosis severity and myocardial perfusion parameters derived from dynamic myocardial CT perfusion imaging (CTP) in an ex-vivo porcine heart model. METHODS: Six porcine hearts were perfused according to Langendorff. Circulatory parameters such as arterial blood flow (ABF) (L/min), mean arterial pressure (MAP) (mmHg) and heart rate (bpm) were monitored. Using an inflatable cuff and monitored via a pressure wire, coronary artery stenoses of different FFR grades were created (no stenosis, FFR = 0.80, FFR = 0.70, FFR = 0.60, and FFR = 0.50). Third generation dual-source CT was used to perform dynamic CTP in shuttle mode at 70 kV. Using the AHA-16-segment model, myocardial blood flow (MBF) (mL/100 mL/min) and volume (MBV) (mL/100 mL) were analyzed using dedicated software for all ischaemic and non-ischaemic segments. RESULTS: During five successful experiments, ABF ranged from 0.8 to 1.2 L/min, MAP from 73 to 90 mmHg and heart rate from 83 to 115 bpm. Non-ischaemic and ischaemic segments showed significant differences in MBF for stenosis grades of FFR ≤ 0.70. At this degree of obstruction, median MBF was 79 (interquartile range [IQR]: 66-90) for non-ischaemic segments versus 56 mL/100 mL/min (IQR: 46-73) for ischaemic segments (p < 0.05). For MBV, a significant difference was found at FFR ≤ 0.80 with median MBV values of 7.6 (IQR: 7.0-8.3) and 7.1 mL/100 mL (IQR: 6.0-8.2) for non-ischaemic and ischaemic myocardial segments, respectively (p < 0.05). CONCLUSION: Artificial flow alterations in a Langendorff porcine heart model could be detected and measured by CTP-derived myocardial perfusion parameters and showed significant systematic correlation with stepwise flow reduction that permitted early detection of ischaemic myocardium. Additional research in clinical setting is required to develop absolute quantitative CTP.