Objective To quantitatively compare and determine the best pancreatic tumor contrast to noise ratio (CNR) in different dual-energy derived datasets. Methods In this retrospective, single center study, 16 patients (9 male, 7 female, average age 59.4±13.2 years) with pathologically diagnosed pancreatic cancer were enrolled. All patients received an abdominal scan using a dual source CT scanner 7 to 31 days before biopsy or surgery. After injection of iodine contrast agent, arterial and pancreatic parenchyma phase were scanned consequently, using a dual-energy scan mode (100 kVp/230 mAs and Sn 140 kVp/178 mAs) in the pancreatic parenchyma phase. A series of derived dual-energy datasets were evaluated including non-liner blending (non-linear blending width 0-500 HU; blending center -500 to 500 HU), mono-energetic (40-190 keV), 100 kVp and 140 kVp. On each datasets, mean CT values of the pancreatic parenchyma and tumor, as well as standard deviation CT values of subcutaneous fat and psoas muscle were measured. Regions of interest of cutaneous fat and major psoas muscle of 100 kVp and 140 kVp images were calculated. Best CNR of subcutaneous fat (CNRF) and CNR of the major psoas muscle (CNRM) of non-liner blending and mono-energetic datasets were calculated with the optimal mono-energetic keV setting and the optimal blending center/width setting for the best CNR. One Way ANOVA test was used for comparison of best CNR between different dual-energy derived datasets. Results The best CNRF (4.48±1.29) was obtained from the non-liner blending datasets at blending center -16.6±103.9 HU and blending width 12.3±10.6 HU. The best CNRF (3.28±0.97) was obtained from the mono-energetic datasets at 73.3±4.3 keV. CNRF in the 100 kVp and 140 kVp were 3.02±0.91 and 1.56±0.56 respectively. Using fat as the noise background, all of these images series showed significant differences (P<0.01) except best CNRF of mono-energetic image sets vs. CNRF of 100 kVp image (P=0.460). Similar results were found using muscle as the noise background (mono-energetic image vs. 100 kVp image: P=0.246; mono-energetic image vs. non-liner blending image: P=0.044; others: P<0.01). Conclusion Compared with mono-energetic datasets and low kVp datasets, non-linear blending image at automatically chosen blending width/window provides better tumor to the pancreas CNR, which might be beneficial for better detection of pancreatic tumors.

Key words: pancreatic adenocarcinoma, dual-energy, contrast to noise ratio, non-linear blending