基于深度学习的图像重建算法在胸部薄层CT中的降噪效果评估

曾文; 曾令明; 徐旭; 胡斯娴; 刘科伶; 张金戈; 彭婉琳; 夏春潮; 李真林

doi:10.12182/20210360506

基于深度学习的图像重建算法在胸部薄层CT中的降噪效果评估

Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT

摘要

摘要:
目的为了评估基于深度学习的重建算法在胸部薄层计算机断层扫描（computed tomography，CT）图像中的降噪效果，对滤波反投影重建（filtered back projection，FBP）、自适应统计迭代重建（adaptive statistical iterative reconstruction，ASIR）与深度学习图像重建（deep learning image reconstruction，DLIR）图像进行分析。
方法回顾性纳入47例患者胸部CT平扫原始数据，利用FBP，ASIR混合重建（ASIR50%、ASIR70%），深度学习低、中、高3种模式（DL-L、DL-M、DL-H）共6种，重建出0.625 mm的图像。在每组图像的主动脉内、骨骼肌以及肺组织内分别勾画感兴趣区，测量感兴趣区内的CT值、SD值和信噪比（signal-to-noise ratio，SNR）进行客观评价，并对图像进行主观评价。
结果 6种重建图像CT、SD和SNR值的差异有统计学意义（P<0.001）。6种重建图像主观评分差异有统计学意义（P<0.001）。DLIR在主动脉和骨骼肌处的图像噪声明显低于传统的FBP和ASIR，图像质量能够满足临床需求。而且呈现出DL-H降噪效果最佳、噪声最低，ASIR70%、DL-M、ASIR50%、DL-L、FBP 图像噪声依次增加。通过主观评分的比较发现，DL-H的图像整体质量有明显的提升，但不能使肺纹理重建更清晰。
结论基于深度学习的模型能够有效减少胸部薄层CT图像的噪声，提高图像的质量。而在3种深度学习模型中，DL-H的降噪效能最佳。

Abstract:
Objective To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms.
Methods The chest CT scan raw data of 47 patients were included in this study. Images of 0.625 mm were reconstructed using six reconstruction methods, including FBP, ASIR hybrid reconstruction (ASIR50%, ASIR70%), and deep learning low, medium and high modes (DL-L, DL-M, and DL-H). After the regions of interest were outlined in the aorta, skeletal muscle and lung tissue of each group of images, the CT values, SD values and signal-to-noise ratio (SNR) of the regions of interest were measured, and two radiologists evaluated the image quality.
Results CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference (P<0.001). There were statistically significant differences in the image quality scores of the six reconstruction methods (P<0.001). Images reconstruced with DL-H have the lowest noise and the highest overall quality score.
Conclusion The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect.

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