| 李杨飞,朱卫萍,侯怡迪,等.深度学习重建算法在低辐射剂量头颈联合CT血管成像中的应用价值[J].中华放射医学与防护杂志,2024,44(1):53-59.Li Yangfei,Zhu Weiping,Hou Yidi,et al.Application value of the deep learning-based image reconstruction algorithm in combined head and neck CT angiography with low radiation dose[J].Chin J Radiol Med Prot,2024,44(1):53-59 |
| 深度学习重建算法在低辐射剂量头颈联合CT血管成像中的应用价值 |
| Application value of the deep learning-based image reconstruction algorithm in combined head and neck CT angiography with low radiation dose |
| 投稿时间：2023-06-26 |
| DOI：10.3760/cma.j.cn112271-20230626-00210 |
| 中文关键词: X射线计算机体层摄影术 辐射剂量 深度学习重建算法 图像质量 |
| 英文关键词:X-ray computed tomography Radiation dose Deep learning-based image reconstruction algorithm Image quality |
| 基金项目:浙江省医药卫生科技计划项目(2021KY1201) |
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| 摘要点击次数: 2544 |
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| 中文摘要: |
| 目的 探讨深度学习重建算法(DLIR)与自适应统计迭代重建算法(ASiR-V)在头颈部CT血管成像(CTA)中检查剂量和成像质量的差异。方法 前瞻性收集因头颈部血管疾病行头颈部CTA检查的患者80例。按照检查的先后顺序分为A组和B组,每组40例。A组采用管电压120 kV,噪声指数11.0,ASiR-V 50%重建;B组采用管电压80 kV,噪声指数9.0,分别采用ASiR-V 50%重建(B1组)和DLIR-H重建(B2组)。采用独立样本t检验比较两组的辐射剂量和图像质量。采用Kruskal-wallis检验和Wilcoxon秩和检验用于比较两种成像方式的辐射剂量和主观、客观图像质量。比较组间强化血管CT值,感兴趣区(ROI)的信号与噪声, 计算信噪比(SNR)和对比信噪比(CNR)。结果 A、B两组有效辐射剂量分别为(0.77±0.08)、(0.45±0.05)mSv,差异有统计学意义(t=21.96,P<0.001)。A、B1、B2 3组图像的主动脉弓、颈动脉起始部、颈动脉分叉层面、大脑中动脉M1段强化血管CT值、SD、SNR、CNR,差异均有统计学意义(F=67.69、68.50、50.52、74.10、63.10、91.22、69.16,P<0.001)。A、B1、B2 3组图像质量主观评分差异有统计学意义(Z=71.06,P<0.05)。结论 DLIR算法能够在进一步降低头颈部CTA检查辐射剂量的同时,明显地减少图像噪声,保证了图像质量,具有良好的临床应用价值。 |
| 英文摘要: |
| Objective To explore the differences between the deep learning-based image reconstruction (DLIR) and the adaptive statistical iterative reconstruction V (ASiR-V) algorithms in the radiation dose and image quality of head and neck CT angiography (CTA). Methods The data of 80 patients undergoing head and neck CTA due to vascular diseases in the head and neck were prospectively collected. These patients were randomly divided into groups A and B based on their examination sequence. The CTA images of group A were reconstructed based on ASiR-V 50%, with a tube voltage of 120 kV and a noise index of 11.0. In contrast, those of group B were reconstructed based on ASiR-V 50% (for group B1) and DLIR-H (for group B2), with a tube voltage of 80 kV and a noise index of 9.0. Then, the radiation doses and image quality of both groups were compared using the independent-sample t-test. The radiation doses, and both subjective and objective image quality of the two imaging method were compared through the Kruskal-Wallis test and the Wilcoxon rank-sum test. The independent- or paired-sample t-test was employed to measure inter-group vascular enhanced CT values, as well as signals and noise from regions of interest (ROIs), with signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) calculated. Results The effective doses of groups A and B were (0.77±0.08) and (0.45±0.05) mSv, respectively, with a statistically significant difference (t = 21.96, P< 0.001). The vascular enhanced CT values, SDs, SNRs, and CNRs in the arch of the aorta, the initial and bifurcation parts of the common carotid artery, and the M1 segment of the middle cerebral artery showed statistically significant differences among groups A, B1, and B2 (F= 67.69, 68.50, 50.52, 74.10, 63.10, 91.22, 69.16, P< 0.001). Additionally, statistically significant differences were observed in the subjective scores of image quality among groups A, B1, and B2 (Z = 71.06, P< 0.05). Conclusions The DLIR algorithm can further reduce the radiation dose in head and neck CTA examination while significantly reducing image noise and ensuring image quality, thus demonstrating high clinical application value. |
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