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 parameters were 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, κ 0.45). CONCLUSION: There is a moderate visual but not statistically significant correlation between DECT iodine maps and perfusion parameter maps of DCE-MRI.
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 parameters were 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, κ 0.45). CONCLUSION: There is a moderate visual but not statistically significant correlation between DECT iodine maps and perfusion parameter maps of DCE-MRI.
Authors: Thorsten R C Johnson; Bernhard Krauss; Martin Sedlmair; Michael Grasruck; Herbert Bruder; Dominik Morhard; Christian Fink; Sabine Weckbach; Miriam Lenhard; Bernhard Schmidt; Thomas Flohr; Maximilian F Reiser; Christoph R Becker Journal: Eur Radiol Date: 2006-12-07 Impact factor: 5.315
Authors: Tristan A Kuder; Frank Risse; Monika Eichinger; Sebastian Ley; Michael Puderbach; Hans-Ulrich Kauczor; Christian Fink Journal: Eur Radiol Date: 2007-08-18 Impact factor: 5.315
Authors: Thomas G Flohr; Cynthia H McCollough; Herbert Bruder; Martin Petersilka; Klaus Gruber; Christoph Süss; Michael Grasruck; Karl Stierstorfer; Bernhard Krauss; Rainer Raupach; Andrew N Primak; Axel Küttner; Stefan Achenbach; Christoph Becker; Andreas Kopp; Bernd M Ohnesorge Journal: Eur Radiol Date: 2005-12-10 Impact factor: 5.315
Authors: Sven F Thieme; Christoph R Becker; Marcus Hacker; Konstantin Nikolaou; Maximilian F Reiser; Thorsten R C Johnson Journal: Eur J Radiol Date: 2008-09-05 Impact factor: 3.528
Authors: Kambiz Nael; Michael Fenchel; Mayil Krishnam; J Paul Finn; Gerhard Laub; Stefan G Ruehm Journal: Invest Radiol Date: 2007-06 Impact factor: 6.016