Zhu Shumeng, Zhang Baoping, Tian Qian, Li Ao, Liu Zhe, Hou Wei, Zhao Wenzhe, Huang Xin, Xiao Yao, Wang Yiming, Wang Rui, Li Yuhang, Yang Jian, Jin Chao
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an, 710061, P. R. China.
BMC Med Inform Decis Mak. 2024 Dec 18;24(1):389. doi: 10.1186/s12911-024-02811-w.
The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current.
Sixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1-5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion.
A significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P < 0.003). The SNR and CNR in Group B with Deep IR at levels 1-5 were greater than those in Group A and significantly differed when HIR and Deep IR were applied at levels 1-3 of HIR and Deep IR (P < 0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4-5 in Group B were significantly higher than those in group A with HIR (P < 0.003).
Deep IR algorithm can meet the clinical requirements and reduce the radiation dose by 69.94% in portal venous phase abdominal CT with a low tube voltage of 80 kV and a low tube current. Deep IR at levels 4-5 can significantly improve the image quality of the abdominal parenchymal organs and the clarity of the portal vein.
低管电压技术（如80 kV）可有效降低腹部CT的辐射剂量，并增强血管和实质结构的对比增强效果。然而，在这种情况下总是需要高管电流，这限制了剂量降低的潜力。本研究探讨了深度学习迭代重建算法（Deep IR）在降低辐射剂量的同时提高低管电压和电流的腹部计算机断层扫描（CT）图像质量的可行性。
60例接受腹部门静脉期CT检查的患者（男/女，36/24；年龄，57.72±10.19岁）被随机分为A组（100 kV，自动曝光控制[AEC]，参考管电流为213 mAs）和B组（80 kV，AEC，参考值为130 mAs）。图像通过混合迭代重建（HIR）和Deep IR（1-5级）进行重建。测量每个图像序列中肝门水平的四个感兴趣区域（ROI），即肝脏、脾脏、门静脉主干和竖脊肌的平均CT值和标准差（SD），并计算信噪比（SNR）和对比噪声比（CNR）。两位放射科医生使用5分标准对图像质量进行主观评分。
与A组相比，B组的辐射剂量显著降低了69.94%（5.09±0.91 mSv对1.53±0.37 mSv）。应用Deep IR后，CT值无显著变化，但SD逐渐增加。B组的CT值高于A组，两组间门静脉CT值有显著差异（P<0.003）。B组在Deep IR 1-5级时的SNR和CNR大于A组，在HIR和Deep IR的1-3级应用时SNR和CNR有显著差异（P<0.003）。B组在Deep IR 4-5级时的主观评分（变形、门静脉清晰度、小结构可见性和整体图像质量）显著高于A组的HIR评分（P<0.003）。
Deep IR算法能够满足临床需求，在80 kV低管电压和低管电流的腹部门静脉期CT中可将辐射剂量降低69.94%。Deep IR 4-5级可显著提高腹部实质器官的图像质量和门静脉的清晰度。
