He Yong-Lan, Zhang Da-Ming, Xue Hua-Dan, Jin Zheng-Yu
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
Chin Med Sci J. 2013 Jan;27(4):207-12. doi: 10.1016/s1001-9294(13)60003-6.
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.
目的 定量比较并确定不同双能量衍生数据集中胰腺肿瘤的最佳对比噪声比（CNR）。方法 在这项回顾性单中心研究中，纳入了16例经病理诊断为胰腺癌的患者（9例男性，7例女性，平均年龄59.4±13.2岁）。所有患者在活检或手术前7至31天使用双源CT扫描仪进行腹部扫描。注射碘造影剂后，依次扫描动脉期和胰腺实质期，胰腺实质期采用双能量扫描模式（100 kVp/230 mAs和Sn 140 kVp/178 mAs）。评估了一系列衍生的双能量数据集，包括非线性融合（非线性融合宽度0 - 500 HU；融合中心 - 500至500 HU）、单能量（40 - 190 keV）、100 kVp和140 kVp。在每个数据集中，测量胰腺实质和肿瘤的平均CT值，以及皮下脂肪和腰大肌的标准差CT值。计算100 kVp和140 kVp图像的皮下脂肪和主要腰大肌的感兴趣区。使用最佳单能量keV设置和最佳CNR的最佳融合中心/宽度设置，计算非线性融合和单能量数据集中皮下脂肪的最佳CNR（CNRF）和主要腰大肌的CNR（CNRM）。采用单因素方差分析比较不同双能量衍生数据集中的最佳CNR。结果 在融合中心 - 16.6±103.9 HU和融合宽度12.3±10.6 HU的非线性融合数据集中获得最佳CNRF（4.48±1.29）。在73.3±4.3 keV的单能量数据集中获得最佳CNRF（3.28±0.97）。100 kVp和140 kVp中的CNRF分别为3.02±0.91和1.56±0.56。以脂肪作为噪声背景，除单能量图像集的最佳CNRF与100 kVp图像的CNRF比较（P = 0.460）外，所有这些图像系列均显示出显著差异（P < 0.01）。以肌肉作为噪声背景也发现了类似结果（单能量图像与100 kVp图像：P = 0.246；单能量图像与非线性融合图像：P = 0.044；其他：P < 0.01）。结论 与单能量数据集和低kVp数据集相比，自动选择融合宽度/窗口的非线性融合图像提供了更好的肿瘤与胰腺的CNR，这可能有利于更好地检测胰腺肿瘤。
