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 (ASiR-V). Materials and Methods Abdominal arterial phase images of 33 patients [body mass index (BMI) BMI≤20 kg/m2] scanned by GE Revolution CT were retrospectively analyzed (group A) using 70 kVp tube voltage and 50% ASiR-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.%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(ASiR-V)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/m2]的腹部动脉期图像(A组),采用70 kVp管电压、50% ASiR-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.
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