Brief Article
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World J Radiol. May 28, 2013; 5(5): 202-207
Published online May 28, 2013. doi: 10.4329/wjr.v5.i5.202
Correlation analysis of dual-energy CT iodine maps with quantitative pulmonary perfusion MRI
Jan Hansmann, Paul Apfaltrer, Frank G Zoellner, Thomas Henzler, Mathias Meyer, Gerald Weisser, Stefan O Schoenberg, Ulrike I Attenberger
Jan Hansmann, Paul Apfaltrer, Thomas Henzler, Mathias Meyer, Gerald Weisser, Stefan O Schoenberg, Ulrike I Attenberger, Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany
Frank G Zoellner, Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, D-68167 Mannheim, Germany
Author contributions: All authors made substantial contributions to the conception and design of the study, drafting or revising the article critically for important intellectual content; Hansmann J, Apfaltrer P, Zoellner FG and Attenberger UI contributed to the data analysis and interpretation.
Correspondence to: Jan Hansmann, MD, Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim, Germany. jan.hansmann@medma.uni-heidelberg.de
Telephone: +49-621-3832067 Fax: +49-621-3833817
Received: January 14, 2013
Revised: May 3, 2013
Accepted: May 16, 2013
Published online: May 28, 2013
Processing time: 144 Days and 7.6 Hours
Abstract

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.

Keywords: Dual-energy computed tomography; Time-resolved magnetic resonance imaging; Pulmonary perfusion; Iodine maps

Core tip: Dual-energy derived iodine maps and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) may allow evaluation of pulmonary perfusion. Hypothetical the decrease in pulmonary perfusion detected on DCE-derived iodine maps would correlate highly with perfusion parameters derived from DCE-MRI in patients with restricted pulmonary perfusion. However, against our hypothesis, we did not find a significant correlation between pulmonary perfusion defects detected on dual-energy computed tomography-derived iodine maps and perfusion parameters derived from time-resolved MRI. In addition, there was only a moderate level of visual correlation. This is in contrast with prior studies that investigated the role of pulmonary iodine maps to serve as an additional tool providing a functional evaluation of pulmonary perfusion.