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Introduction: By using bolus tracking with an appropriate acquisition delay dual-energy computed tomography (DECT) iodine maps might serve as a replacement of CT perfusion maps at reduced radiation exposure. This study aimed to evaluate the optimal acquisition delays of DECT for the replacement of parameter maps calculated with the Patlak model in pancreatic adenocarcinoma by corresponding iodine maps. Materials and methods: Dual-source dynamic DECT acquisitions at 80 kVp/Sn140 kVp of 14 patients with pancreatic carcinoma were used to calculate CT perfusion maps of blood volume and permeability with the Patlak model. DECT iodine maps were generated from individual DECT acquisitions, matching acquisition times relative to prior bolus-triggered three-phase CT acquisitions for investigating different acquisition delays. Correlation between perfusion parameters and iodine concentrations was determined for acquisition delays between −6 s and 33 s. Results: Correlation between iodine concentrations and perfusion parameters ranged from −0.05 to 0.63 for blood volume and from −0.05 to 0.71 for permeability, depending on potential trigger delay. The correlation was significant for potential acquisition delays above 1.5 s for blood volume and above 9.0 s for permeability (both p

Objectives: To investigate the value of spectral-detector computed tomography (SDCT) parameters for the quantitative differentiation between atelectasis and pneumonia on contrast-enhanced chest CT. Material and methods: Sixty-three patients, 22 clinically diagnosed with pneumonia and 41 with atelectasis, underwent contrast-enhanced SDCT scans during the venous phase. CT numbers (Hounsfield Units [HU]) were measured on conventional reconstructions (CON120kVp) and the iodine concentration (Ciodine, [mg/ml]), and effective atomic number (Zeff) on spectral reconstructions, using region-of-interest (ROI) analysis. Receiver operating characteristics (ROC) and contrast-to-noise ratios (CNRs) were calculated to assess each reconstruction's potential to differentiate between atelectasis and pneumonia. Results: On contrast-enhanced SDCT, the difference between atelectasis and pneumonia was significant on CON120kVp, Ciodine, and Zeff images (p < 0.001). On CON120kVp images, a threshold of 81 HU achieved a sensitivity of 93 % and a specificity of 95 % for identifying pneumonia, while Ciodine and Zeff images reached the same sensitivity but lower specificities of 85 % and 83 %. CON120kVp images showed significantly higher CNRs between normal lung and atelectasis or pneumonia with 30.63 and 27.69 compared to Ciodine images with 3.54 and 1.27 and Zeff images with 4.22 and 7.63 (p < 0.001). None of the parameters could differentiate atelectasis and pneumonia without contrast media. Conclusions: Contrast-enhanced SDCT can differentiate atelectasis and pneumonia based on the spectral parameters Ciodine, and Zeff. However, they had no added value compared to CT number measurement on CON120kVp images. Furthermore, contrast media is still needed for a differentiation based on quantitative SDCT parameters.