| 刘静红,刘爱连,陶奉明,刘义军,方鑫,潘聚东.像素闪烁算法在肾脏低剂量CT灌注扫描中的应用[J].中华放射医学与防护杂志,2018,38(5):386-389 |
| 像素闪烁算法在肾脏低剂量CT灌注扫描中的应用 |
| Application of a deep machine learning technique for low dose renal CT perfusion |
| 投稿时间：2017-09-03 |
| DOI：10.3760/cma.j.issn.0254-5098.2018.05.012 |
| 中文关键词: 低剂量 像素闪烁算法 CT灌注成像 深度学习 肾脏 |
| 英文关键词:Low dose Deep learning technique CT perfusion |
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| 中文摘要: |
| 目的 探讨深度学习像素闪烁算法在低剂量一站式肾脏CT灌注扫描（CTP）检查中提高图像质量的应用价值。方法 回顾性收集2017年3月至8月本院行全肾脏CT增强及灌注扫描的患者21例。采用Revolution CT机，先行肾脏平扫，然后进行灌注和增强3期扫描，管电压120 kVp，CTP采集管电流为20 mA，增强3期管电流为100 mA，轴扫模式，ASiR-V 80%，X射线管旋转时间0.5 s，z轴覆盖范围160 mm，扫描层厚5 mm，层间隔5 mm。首次28 s采用屏气扫描，共获得15期图像，然后分别于第39、43、47、51、63、83、113、153、213、353、593 s各采集1次，其中第22、51及153 s分别采集增强的皮质期、髓质期及排泄期3期图像。所有CTP数据经深度学习的A7模式进行处理，处理前数据为A组，处理后数据为B组。比较皮质期A、B组肾皮质CT值、CT值标准差、竖脊肌CT值标准差、对比噪声比（CNR）、信噪比（SNR），比较A、B组肾皮质的血流量、血容量、达峰时间和表面通透性。结果 A、B组图像肾皮质标准差（SD）值（9.04±1.77和5.75±1.00）、竖脊肌SD值（8.52±2.28和5.67±0.98）、CNR（16.28±6.61和28.90±1.50）、SNR（21.41±6.67和30.65±7.67）差异均有统计学意义（t=1.562、6.286、5.925、-5.892、-17.274，P<0.05）；像素闪烁算法处理之后的图像其SD值明显减低，SNR明显升高。两组的肾皮质、肾髓质血流量（BF）值、血容量（BV）值、达峰时间（TP）值及肾髓质的表面通透性（PS）值差异均无统计学意义（P>0.05）。结论 深度学习像素闪烁算法可以减小低条件扫描图像的噪声，增加图像的对比噪声比，从而提高图像质量，并且不影响灌注参数值。 |
| 英文摘要: |
| Objective To assess the ability of a deep machine learning technique for improving the quality of one-stop renal low dose CTP images. Methods Twenty-one cases who underwent renal noncontrast CT, triple-phase contrast enhanced CT, and CT perfusion(CTP) were collected prospectively. Revolution CT scanner was used with the scan protocol as followed:120 kVp, 20 mA for CTP and 100 mA for triple-phase conctrast enhancement, axial scan, ASIR-V80%, rotation 0.5 s, coverage area for z-axial 160 mm, thickness 5 mm. A total of 15 phases were obtained for the first 28 s and then scanned once at 39, 43, 47, 51, 63, 83, 113, 213, 353, 593 s for CTP, which the phases at the 22, 51 and 153 s were the cortical phase, medullary phase and excretory phase, respectively. All CTP data was reconstructed with a deep machine learning technique pixel shine A7 model. The data before and after reconstruction was in group A and in group B, respectively. Compared the all data of cortex in the cortical phase and CTP parameters between the two groups. Results There were significant differences of CT values of SD of cortex (9.04±1.77 and 5.75±1.00, respectively), CT values of SD of elector spinae (8.52±2.28 and 5.67±0.98, respectively), CNR(16.28±6.61 and 28.90±1.50, respectively) and SNR (21.41±6.67 and 30.65±7.67, respectively) between the two groups(t=1.562, 6.286, 5.925, -5.892, -17.274, P<0.05). The SD of images after PS-B was lower than that before PS-B significantly and SNR was improved obviously. There were no differences of cortical blood flow (BF), blood volume (BV), time to peak (TP) and medullary permeability of surface (PS) between the two groups(P>0.05). Conclusions The reconstruction of deep machine learning PixelShine technique PS-A7 can reduce the noise of images obtained with low tube current, improve the SNR and can not effect the CTP parameters. |
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