摘要
目的探讨基于深度学习的去噪声算法——像素闪耀(PixelShine)算法提升70 kVp结合自适应统计迭代重建算法(ASiRV)重建的腹部动脉期CT图像质量的可行性。资料与方法回顾性分析经GE Revolution CT扫描的33例患者[体重指数(BMI)≤20 kg/m^2]的腹部动脉期图像(A组),采用70 kVp管电压、50%ASi R-V技术。应用PixelShine算法B2模式对A组图像进行后处理,获得PixelShine图像(B组)。2名观察者分别对A、B组图像质量进行5分制评分,分析2名观察者评分结果的一致性,比较两组图像的评分差异、噪声以及肝脏与胰腺实质的信噪比(SNR)及对比噪声比(CNR)的差异。结果 A、B两组图像质量评分分别为(3.12±0.33)分、(3.97±0.53)分,噪声值分别为(14.50±1.42)HU、(10.05±1.80)HU,肝脏实质SNR分别为4.51±0.53、6.78±1.27,肝脏实质CNR分别为0.89±0.55、1.42±0.81,胰腺实质SNR分别为9.51±1.69、13.87±3.26,胰腺实质CNR分别为5.83±1.66、8.48±2.46,B组的图像质量评分、肝脏及胰腺实质SNR、CNR均大于A组,B组图像噪声较A组降低约31%,差异均有统计学意义(P<0.05)。结论应用PixelShine算法进行后处理可提高70 kVp腹部动脉期图像质量,显著降低图像噪声,并提升图像SNR及CNR。
Purpose To explore the feasibility of denoising algorithm-PixelShine algorithm based on deep learning to enhance the quality of abdominal arterial phase CT images rebuilt by 70 kVp combined with adaptive statistical iterative reconstruction-Veo(ASiRV). Materials and Methods Abdominal arterial phase images of 33 patients [body mass index(BMI) BMI≤20 kg/m^2] scanned by GE Revolution CT were retrospectively analyzed(group A) using 70 kVp tube voltage and 50% ASi R-V technique. PixelShine algorithm B2 mode was applied to post-process group A images to obtain PixelShine image(group B). Two observers rated the image quality of the two groups via a 5-point rating system. The consistency of the rating was analyzed. The difference in ratings, noise, virtual signal-to-noise ratio(SNR) of liver and pancreas and contrast noise ratio(CNR) were compared between the two groups of images. Results The image quality rating of group A and B were(3.12±0.33) scores and(3.97±0.53) scores respectively, noise value(14.50±1.42) HU vs(10.05±1.80) HU, liver virtual SNR 4.51±0.53 vs 6.78.±1.27, liver virtual CNR 0.89±0.55 vs 1.42±0.81, pancreatic virtual SNR 9.51±1.69 vs 13.87±3.26, and pancreatic virtual CNR 5.83±1.66 vs 8.48±2.46. The quality rating of images, liver and pancreas virtual SNR, CNR in group B were all higher than those in group A, and the image noise of group B decreased about 31% compared with that of group A, the difference was statistically significant(P〈0.05). Conclusion Post-processing with PixelShine algorithm can improve the image quality of 70 kVp abdominal arterial phase, significantly reduce image noise, and increase image SNR and CNR.
作者
田士峰
刘爱连
潘聚东
刘静红
刘义军
方鑫
袁刚
TIAN Shifeng;LIU Ailian;PAN Judong;LIU Jinghong;LIU Yijun;FANG Xin;YUAN Gang(Department of Radiology,the First Affiliated Hospital of Dalian Medical University,Dalian 116011,China)
出处
《中国医学影像学杂志》
CSCD
北大核心
2018年第3期205-208,共4页
Chinese Journal of Medical Imaging
关键词
胃肠疾病
腹部
体层摄影术
X线计算机
图像处理
计算机辅助
算法
质量控制
辐射剂量
Gastrointestinal diseases
Abdomen
Tomography, X-ray computed
Image processing, computer-assisted
Algorithms
Quality control
Radiation dosage