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基于深度学习的重建算法在健康志愿者肝脏低剂量薄层CT检查中的应用研究

Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers

    摘要
  • 摘要:
      目的  比较基于深度学习（deep learning, DL）的重建算法、滤波反投影（filtered back projection filtering, FBP）重建算法和迭代重建（iterative reconstruction, IR）算法，探讨DL重建算法在健康志愿者肝脏低剂量薄层CT检查中临床应用的可行性。
      方法  采用联影160层CT对直径180 mm的标准水模进行扫描，比较DL、FBP和IR算法的噪声功率谱。前瞻性纳入健康志愿者100例，其中常规剂量组（normal dose, ND）50例、低剂量组（low dose, LD）50例。ND组采用IR算法；LD组分别采用DL、FBP和IR算法。使用单因素方差分析比较ND-IR、LD-FBP、LD-IR和LD-DL 4组的肝CT值、肝噪声、肝信噪比（signal-to-noise ratio, SNR）、对比噪声比（contrast noise ratio, CNR）和质量因数（figure of merit, FOM）。采用Kruskal-Wallis检验比较4组图像的解剖结构主观评分。
      结果  DL噪声功率谱平均峰值最低，形态与中等迭代等级IR算法相似。ND-IR、LD-FBP、LD-IR和LD-DL的肝CT值差异无统计学意义，LD-DL的噪声低于LD-FBP、LD-IR和ND-IR（P<0.05），LD-DL的SNR、CNR和FOM均高于LD-FBP、LD-IR和ND-IR（P<0.05）。LD-DL解剖结构的主观评分均与ND-IR无明显差异（P>0.05），且均高于LD-FBP和LD-IR（P<0.05）。LD组相对于ND组减少约50.2%辐射剂量。
      结论  噪声形态与临床常用的中等迭代等级IR相近的DL算法降噪能力高于IR，与FBP相比噪声形态较平滑但降噪能力大幅提高，在健康志愿者肝脏低剂量薄层CT检查中可获得肝脏常规剂量厚层CT的图像质量。

     

    Abstract:
      Objective  To explore the clinical feasibility of applying deep learning (DL) reconstruction algorithm in low-dose thin-slice liver CT examination of healthy volunteers by comparing the reconstruction algorithm based on DL, filtered back projection (FBP) reconstruction algorithm and iterative reconstruction (IR) algorithm.
      Methods  A standard water phantom with a diameter of 180 mm was scanned, using the 160 slice multi-detector CT scanning of United Imaging Healthcare, to compare the noise power spectrums of DL, FBP and IR algorithms. 100 healthy volunteers were prospectively enrolled, with 50 assigned to the normal dose group (ND) and 50 to the low dose group (LD). IR algorithm was used in the ND group to reconstruct images, while DL, FBP and IR algorithms were used in the LD group to reconstruct images. One-way analysis of variance was used to compare the liver CT values, the liver noise, liver signal-to-noise ratio (SNR), contrast noise ratio (CNR) and figure of merit (FOM) of the images of ND-IR, LD-FBP, LD-IR and LD-DL. The Kruskal-Wallis test was used to analyse subjective scores of anatomical structures.
      Results  The DL algorithm had the lowest average peak value of noise power spectrum, and its shape was similar to that of medium-level IR algorithm. Liver CT values of ND-IR, LD-FBP, LD-IR and LD-DL did not show statistically significant difference. The noise of LD-DL was lower than that of LD-FBP, LD-IR and ND-IR (P<0.05), and the SNR, CNR and FOM of LD-DL were higher than those of LD-FBP, LD-IR and ND-IR (P<0.05). The subjective scores of anatomical structures of LD-DL did not show significant difference compared to those of ND-IR (P>0.05), and were higher than those of LD-FBP and LD-IR. The radiation dose of the LD group was reduced by about 50.2% compared with that of the ND group.
      Conclusion  The DL algorithm with noise shape similar to the medium iterative grade IR commonly used in clinical practice showed higher noise reduction ability than IR did. Compared with FBP, the DL algorithm had smoother noise shape, but much better noise reduction ability. The application of DL algorithm in low-dose thin-slice liver CT of healthy volunteers can help achieve the standard image quality of liver CT.

     

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