Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea. Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
Phys Med Biol. 2019 Jul 4;64(13):135010. doi: 10.1088/1361-6560/ab28a1.
Lung densitometry is being frequently adopted in CT-based emphysema quantification, yet known to be affected by the choice of reconstruction kernel. This study presents a two-step deep learning architecture that enables accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT. Deep learning is used to convert a CT image of a sharp kernel to that of a standard kernel with restoration of truncation artifacts and smoothing-free pixel size normalization. We selected 353 scans reconstructed by both standard and sharp kernels from four different CT scanners from the United States National Lung Screening Trial program database. A truncation artifact correction model was constructed with a combination of histogram extrapolation and a deep learning model trained with truncated and non-truncated image sets. Then, we performed frequency domain zero-padding to normalize reconstruction field of view effects while preventing image smoothing effects. The kernel normalization model has a U-Net based architecture trained for each CT scanner dataset. Three lung density measurements including relative lung area under 950 HU (RA950), lower 15th percentile threshold (perc15), and mean lung density were obtained in the datasets from standard, sharp, and normalized kernels. The effect of kernel normalization was evaluated with pair-wise differences in lung density metrics. The mean of pair-wise differences in RA950 between standard and sharp kernel reconstructions was reduced from 10.75% to -0.07% using kernel normalization. The difference for perc15 decreased from -31.03 HU to -0.30 HU after kernel normalization. Our study demonstrated the feasibility of applying deep learning techniques for normalizing CT kernel effects, thereby reducing the kernel-induced variability in lung density measurements. The deep learning model could increase the accuracy of emphysema quantification, thereby allowing reliable surveillance of emphysema in lung cancer screening even when follow-up CT scans are acquired with different reconstruction kernels.
肺部密度计量学在基于 CT 的肺气肿定量分析中被广泛应用，但已知其受到重建核选择的影响。本研究提出了一种两步深度学习架构，能够准确地对低剂量 CT 中重建核效应对肺气肿定量的影响进行归一化。深度学习用于将锐化核的 CT 图像转换为标准核的图像，同时恢复截断伪影并实现无平滑像素尺寸归一化。我们从美国国家肺癌筛查试验计划数据库中的四个不同的 CT 扫描仪中选择了 353 个由标准核和锐化核重建的扫描。我们构建了一个截断伪影校正模型，该模型结合了直方图外推和一个使用截断和非截断图像集训练的深度学习模型。然后，我们进行了频域零填充，以归一化重建视场的效果，同时防止图像平滑效果。核归一化模型具有基于 U-Net 的架构，针对每个 CT 扫描仪数据集进行训练。在标准、锐化和归一化核的数据集上获得了三个肺密度测量值，包括低于 950 HU 的相对肺面积（RA950）、低于 15%的阈值（perc15）和平均肺密度。通过比较标准核和锐化核重建的肺密度指标的成对差异来评估核归一化的效果。使用核归一化后，标准核和锐化核重建的 RA950 之间的成对差异的平均值从 10.75%减少到-0.07%。归一化后，perc15 的差异从-31.03 HU 减少到-0.30 HU。我们的研究证明了应用深度学习技术进行 CT 核效应归一化的可行性，从而减少了肺密度测量中的核诱导变异性。该深度学习模型可以提高肺气肿定量的准确性，从而允许在即使使用不同的重建核获取后续 CT 扫描时，也可以对肺癌筛查中的肺气肿进行可靠监测。
