Department of Diagnostic Radiology, Goethe University Hospital, and Department of Biophysics, Goethe University, Max von Laue-Str.1, 60438 Frankfurt main, Germany.
Eur J Radiol. 2011 Nov;80(2):612-9. doi: 10.1016/j.ejrad.2011.02.023. Epub 2011 Mar 3.
The purpose of this study was to evaluate image fusion in dual energy computed tomography for detecting various anatomic structures based on the effect on contrast enhancement, contrast-to-noise ratio, signal-to-noise ratio and image quality.
Forty patients underwent a CT neck with dual energy mode (DECT under a Somatom Definition flash Dual Source CT scanner (Siemens, Forchheim, Germany)). Tube voltage: 80-kV and Sn140-kV; tube current: 110 and 290 mAs; collimation-2×32×0.6 mm. Raw data were reconstructed using a soft convolution kernel (D30f). Fused images were calculated using a spectrum of weighting factors (0.0, 0.3, 0.6 0.8 and 1.0) generating different ratios between the 80- and Sn140-kV images (e.g. factor 0.6 corresponds to 60% of their information from the 80-kV image, and 40% from the Sn140-kV image). CT values and SNRs measured in the ascending aorta, thyroid gland, fat, muscle, CSF, spinal cord, bone marrow and brain. In addition, CNR values calculated for aorta, thyroid, muscle and brain. Subjective image quality evaluated using a 5-point grading scale. Results compared using paired t-tests and nonparametric-paired Wilcoxon-Wilcox-test.
Statistically significant increases in mean CT values noted in anatomic structures when increasing weighting factors used (all P≤0.001). For example, mean CT values derived from the contrast enhanced aorta were 149.2±12.8 Hounsfield Units (HU), 204.8±14.4 HU, 267.5±18.6 HU, 311.9±22.3 HU, 347.3±24.7 HU, when the weighting factors 0.0, 0.3, 0.6, 0.8 and 1.0 were used. The highest SNR and CNR values were found in materials when the weighting factor 0.6 used. The difference CNR between the weighting factors 0.6 and 0.3 was statistically significant in the contrast enhanced aorta and thyroid gland (P=0.012 and P=0.016, respectively). Visual image assessment for image quality showed the highest score for the data reconstructed using the weighting factor 0.6.
Different fusion factors used to create images in DECT cause statistically significant differences in CT value, SNR, CNR and image quality. Best results obtained using the weighting factor 0.6 for all anatomic structures used in this study.
本研究旨在评估基于增强效果、对比噪声比、信噪比和图像质量的双能 CT 中的图像融合在检测各种解剖结构中的应用。
40 名患者在西门子 Somatom Definition flash 双源 CT 扫描仪（德国 Forchheim）上进行了颈部 CT 双能模式检查（DECT）。管电压：80-kV 和 Sn140-kV；管电流：110 和 290 mAs；准直器：2×32×0.6mm。使用软卷积核（D30f）对原始数据进行重建。使用加权因子谱（0.0、0.3、0.6、0.8 和 1.0）计算融合图像，生成 80-kV 和 Sn140-kV 图像之间不同的比值（例如，因子 0.6 对应于 80-kV 图像信息的 60%，以及 Sn140-kV 图像信息的 40%）。测量升主动脉、甲状腺、脂肪、肌肉、CSF、脊髓、骨髓和脑的 CT 值和 SNR。此外，计算了主动脉、甲状腺、肌肉和脑的 CNR 值。使用 5 分制评分量表评估主观图像质量。使用配对 t 检验和非参数配对 Wilcoxon-Wilcox 检验比较结果。
随着加权因子的增加，观察到解剖结构的平均 CT 值有统计学显著增加（均 P≤0.001）。例如，增强后的主动脉的平均 CT 值分别为 149.2±12.8 HU、204.8±14.4 HU、267.5±18.6 HU、311.9±22.3 HU、347.3±24.7 HU，当加权因子为 0.0、0.3、0.6、0.8 和 1.0 时。在使用加权因子 0.6 时，SNR 和 CNR 值最高。加权因子 0.6 和 0.3 之间的 CNR 差值在增强后的主动脉和甲状腺中具有统计学意义（P=0.012 和 P=0.016）。视觉图像质量评估显示，使用加权因子 0.6 重建的数据获得了最高评分。
在 DECT 中使用不同的融合因子会导致 CT 值、SNR、CNR 和图像质量的统计学显著差异。在本研究中使用的所有解剖结构中，使用加权因子 0.6 可获得最佳结果。
