ISSN 1004-4140
CN 11-3017/P
叶雄鑫, 刘元芬, 汤博荣, 等. 深度学习图像重建和能谱成像在低对比剂流速胸主动脉CTA中的价值[J]. CT理论与应用研究（中英文）, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.118.
引用本文: 叶雄鑫, 刘元芬, 汤博荣, 等. 深度学习图像重建和能谱成像在低对比剂流速胸主动脉CTA中的价值[J]. CT理论与应用研究（中英文）, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.118.
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YE X X, LIU Y F, TANG B R, et al. Energy Spectral Single-energy Technique Based on Deep Learning Image Reconstruction: Study on Image Quality of Thoracic Aorta under Low Contrast Agent Flow Rate[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.118. (in Chinese).
Citation: YE X X, LIU Y F, TANG B R, et al. Energy Spectral Single-energy Technique Based on Deep Learning Image Reconstruction: Study on Image Quality of Thoracic Aorta under Low Contrast Agent Flow Rate[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.118. (in Chinese).
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深度学习图像重建和能谱成像在低对比剂流速胸主动脉CTA中的价值

Energy Spectral Single-energy Technique Based on Deep Learning Image Reconstruction: Study on Image Quality of Thoracic Aorta under Low Contrast Agent Flow Rate

    摘要
  • 摘要: 目的：探究结合深度学习图像重建算法和能谱单能量技术对提高低造影剂流速下胸主动脉图像质量的价值。资料与方法：本研究回顾性分析福建医科大学附属协和医院2016年1月至2023年12月间以不大于1.5 mL/s对比剂流速接受胸主动脉能谱CTA扫描，且120 kVp-like图像上胸主动脉强化欠佳（胸主动脉CT值<250 HU）的50例患者图像资料。对120 kVp-like图像，40、50和60 keV单能量图像分别进行迭代重建（ASIR-V）和两种深度学习图像重建（DLIR-M、DLIR-H）。对比包括客观图像质量参数（胸主动脉CT值、噪声、信噪比、对比噪声比和硬化伪影指数）和主观图像质量评分。将胸主动脉CT值≥250 HU且主观评分≥3分的图像定义为满足诊断要求的图像。结果：CT值：40 keV>50 keV>60 keV>120 kVp-like图像。而对于同一类型/能级，不同重建算法图像的胸主动脉CT值之间差异无统计学意义。SD、SNR、CNR和BHA值：40 keV>50 keV>60 keV>120 kVp-like图像，其中SD和BHA值：ASIR-V40％>DLIR-M>DLIR-H。同一能级下，DLIR-M/H图像的SNR和CNR均高于ASIR-V图像；对于主观评分，同一能级下：DLIR-H>DLIR-M>ASIR-V；同一重建算法下：40 keV>50 keV>60 keV>120 kVp-like，差异均有统计学意义；所有病例均能通过40keV-DLIR-H获得补救成功的可诊断图像。结论：能谱单能量图像结合深度学习重建算法能够为低对比剂流速下强化效果欠佳的胸主动脉CT造影图像提供满足诊断需求的客观参数，同时显著提升整体图像质量。

     

    Abstract: Objective: To investigate the value of combining a deep learning image reconstruction algorithm and an energy spectral single-energy technique to improve the image quality of the thoracic aorta with a low contrast agent flow rate. Materials and Methods: The imaging data of 50 patients with thoracic aorta energy spectral CTA scans with contrast agent flow rate ≤1.5mL/s from January 2016 to December 2023 at Fujian Medical University Union Hospital were retrospectively analyzed and whose thoracic aorta enhancement was insufficient (thoracic aorta CT value <250HU) on 120 kVp-like images. ASIR-V and two deep-learning image reconstructions (DLIR-M and DLIR-H) were performed on kVp-like images, 40 keV, 50 keV, and 60 keV single-energy images. The objective image quality parameters (thoracic aorta CT value, noise, SNR, CNR, and BHA) were compared with the subjective image quality scores. Images with thoracic aorta CT value ≥250HU and subjective score ≥3 were defined as meeting the diagnostic requirements. Results: CT values were 40 keV>50 keV>60 keV>120 kVp-like images. There was no statistically significant difference in the thoracic aortic CT values between the different reconstruction algorithms for the same type/energy level. SD, SNR, CNR, and BHA values were 40 keV>50 keV>60 keV>120 kVp-like images, respectively, and SD and BHA values were ASIR-V40%>DLIR-M>DLIR-H. The SNR and CNR of all the DLIR images (DLIR-M/H) at different energy levels were higher than those of the ASIR-V images. For subjective scoring, at the same energy level, DLIR-H>DLIR-M>ASIR-V, and under the same reconstruction algorithm: 40 keV>50 keV>60 keV>120 kVp-like. All differences were statistically significant. All cases could obtain successful diagnostic images through 40 keV-DLIR-H. Conclusion: Spectral single-energy images combined with deep learning reconstruction algorithms can provide objective parameters that meet the diagnostic needs of thoracic aorta CT images with a poor enhancement effect under a low contrast agent flow rate while significantly improving the overall image quality.

     

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