AIM:To correlate dual-energy computed tomography(DECT) pulmonary angiography derived iodine maps with parameter maps of quantitative pulmonary perfusion magnetic resonance imaging(MRI).METHODS:Eighteen patients with p...AIM:To correlate dual-energy computed tomography(DECT) pulmonary angiography derived iodine maps with parameter maps of quantitative pulmonary perfusion magnetic resonance imaging(MRI).METHODS:Eighteen patients with pulmonary perfusion defects detected on DECT derived iodine maps were included in this prospective study and additionally underwent time-resolved contrast-enhanced pulmonary MRI [dynamic contrast enhanced(DCE)-MRI].DCE-MRI data were quantitatively analyzed using a pixel-by-pixel deconvolution analysis calculating regional pulmonary blood flow(PBF),pulmonary blood volume(PBV) and mean transit time(MTT) in visually normal lung parenchyma and perfusion defects.Perfusion parameterswere correlated to mean attenuation values of normal lung and perfusion defects on DECT iodine maps.Two readers rated the concordance of perfusion defects in a visual analysis using a 5-point Likert-scale(1 = no correlation,5 = excellent correlation).RESULTS:In visually normal pulmonary tissue mean DECT and MRI values were:22.6 ± 8.3 Hounsfield units(HU);PBF:58.8 ± 36.0 mL/100 mL per minute;PBV:16.6 ± 8.5 mL;MTT:17.1 ± 10.3 s.In areas with restricted perfusion mean DECT and MRI values were:4.0 ± 3.9 HU;PBF:10.3 ± 5.5 mL/100 mL per minute,PBV:5 ± 4 mL,MTT:21.6 ± 14.0 s.The differences between visually normal parenchyma and areas of restricted perfusion were statistically significant for PBF,PBV and DECT(P < 0.0001).No linear correlation was found between MRI perfusion parameters and attenuation values of DECT iodine maps(PBF:r = 0.35,P = 0.15;PBV:r = 0.34,P = 0.16;MTT:r = 0.41,P = 0.08).Visual analysis revealed a moderate correlation between perfusion defects on DECT iodine maps and the parameter maps of DCE-MRI(mean score 3.6,k 0.45).CONCLUSION:There is a moderate visual but not statistically significant correlation between DECT iodine maps and perfusion parameter maps of DCE-MRI.展开更多
Purpose: Dual-energy CT (DECT) can be used for quantification of lung perfusion blood volume (PBV), allowing objective evaluation. However, no reports have investigated pulmonary perfusion correlating with pulmonary a...Purpose: Dual-energy CT (DECT) can be used for quantification of lung perfusion blood volume (PBV), allowing objective evaluation. However, no reports have investigated pulmonary perfusion correlating with pulmonary artery pressure (PAP) in patients with chronic pulmonary diseases. The purpose was to evaluate automated quantification of the lung PBV using dual-energy CT, and its correlation with PAP. Methods: 274 patients who underwent echocardiography within two weeks also underwent CT. The population was divided into high (≥40 mmHg) and low (<40 mmHg) estimated systolic PAP (sPAP) groups (n = 63 and n = 211, respectively). We retrospectively eva-luated the lung PBV using Syngo software, and correlations between the lung PBV and estimated sPAP. Results: Lung PBV values were 25.0 ± 9.6 and 29.0 ± 9.3 Hounsfield units (HU) in high and low sPAP groups, respectively, with a significant difference between them (p = 0.003). In the high sPAP group with underlying lung diseases (n = 15), chronic thromboembolism (n = 25), pulmonary artery stenosis (n = 12), and left heart failure (n = 11), using the Dana Point classification system, lung PBV values were 18.6 ± 1.6, 25.1 ± 4.5, 25.8 ± 4.5, and 32.7 ± 9.4 HU, respectively. There were significant differences in quantification of the lung PBV among them. The mean sPAP of subjects with left heart failure was significantly higher than in the others. In subjects with left heart failure, a positive correlation between the lung PBV value and sPAP was noted (R = 0.721, p < 0.0001). Conclusions: Automated quantification of the lung PBV may estimate the high sPAP. The lung PBV may contribute to clarifying the etiology of a high PAP due to left heart failure.展开更多
文摘AIM:To correlate dual-energy computed tomography(DECT) pulmonary angiography derived iodine maps with parameter maps of quantitative pulmonary perfusion magnetic resonance imaging(MRI).METHODS:Eighteen patients with pulmonary perfusion defects detected on DECT derived iodine maps were included in this prospective study and additionally underwent time-resolved contrast-enhanced pulmonary MRI [dynamic contrast enhanced(DCE)-MRI].DCE-MRI data were quantitatively analyzed using a pixel-by-pixel deconvolution analysis calculating regional pulmonary blood flow(PBF),pulmonary blood volume(PBV) and mean transit time(MTT) in visually normal lung parenchyma and perfusion defects.Perfusion parameterswere correlated to mean attenuation values of normal lung and perfusion defects on DECT iodine maps.Two readers rated the concordance of perfusion defects in a visual analysis using a 5-point Likert-scale(1 = no correlation,5 = excellent correlation).RESULTS:In visually normal pulmonary tissue mean DECT and MRI values were:22.6 ± 8.3 Hounsfield units(HU);PBF:58.8 ± 36.0 mL/100 mL per minute;PBV:16.6 ± 8.5 mL;MTT:17.1 ± 10.3 s.In areas with restricted perfusion mean DECT and MRI values were:4.0 ± 3.9 HU;PBF:10.3 ± 5.5 mL/100 mL per minute,PBV:5 ± 4 mL,MTT:21.6 ± 14.0 s.The differences between visually normal parenchyma and areas of restricted perfusion were statistically significant for PBF,PBV and DECT(P < 0.0001).No linear correlation was found between MRI perfusion parameters and attenuation values of DECT iodine maps(PBF:r = 0.35,P = 0.15;PBV:r = 0.34,P = 0.16;MTT:r = 0.41,P = 0.08).Visual analysis revealed a moderate correlation between perfusion defects on DECT iodine maps and the parameter maps of DCE-MRI(mean score 3.6,k 0.45).CONCLUSION:There is a moderate visual but not statistically significant correlation between DECT iodine maps and perfusion parameter maps of DCE-MRI.
文摘Purpose: Dual-energy CT (DECT) can be used for quantification of lung perfusion blood volume (PBV), allowing objective evaluation. However, no reports have investigated pulmonary perfusion correlating with pulmonary artery pressure (PAP) in patients with chronic pulmonary diseases. The purpose was to evaluate automated quantification of the lung PBV using dual-energy CT, and its correlation with PAP. Methods: 274 patients who underwent echocardiography within two weeks also underwent CT. The population was divided into high (≥40 mmHg) and low (<40 mmHg) estimated systolic PAP (sPAP) groups (n = 63 and n = 211, respectively). We retrospectively eva-luated the lung PBV using Syngo software, and correlations between the lung PBV and estimated sPAP. Results: Lung PBV values were 25.0 ± 9.6 and 29.0 ± 9.3 Hounsfield units (HU) in high and low sPAP groups, respectively, with a significant difference between them (p = 0.003). In the high sPAP group with underlying lung diseases (n = 15), chronic thromboembolism (n = 25), pulmonary artery stenosis (n = 12), and left heart failure (n = 11), using the Dana Point classification system, lung PBV values were 18.6 ± 1.6, 25.1 ± 4.5, 25.8 ± 4.5, and 32.7 ± 9.4 HU, respectively. There were significant differences in quantification of the lung PBV among them. The mean sPAP of subjects with left heart failure was significantly higher than in the others. In subjects with left heart failure, a positive correlation between the lung PBV value and sPAP was noted (R = 0.721, p < 0.0001). Conclusions: Automated quantification of the lung PBV may estimate the high sPAP. The lung PBV may contribute to clarifying the etiology of a high PAP due to left heart failure.
