The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT texture can impact measurements during quantitative image analysis. Harmonization (i.e., kernel conversion) minimizes differences in measurements due to inconsistent reconstruction kernels. Existing methods investigate harmonization of CT scans in single or multiple manufacturers. However, these methods require paired scans of hard and soft reconstruction kernels that are spatially and anatomically aligned. Additionally, a large number of models need to be trained across different kernel pairs within manufacturers. In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN). We use hard and soft reconstruction kernels from the Siemens and GE vendors from the National Lung Screening Trial dataset. We use 50 scans from each reconstruction kernel and train a multipath cycle GAN. To evaluate the effect of harmonization on the reconstruction kernels, we harmonize 50 scans each from Siemens hard kernel, GE soft kernel and GE hard kernel to a reference Siemens soft kernel (B30f) and evaluate percent emphysema. We fit a linear model by considering the age, smoking status, sex and vendor and perform an analysis of variance (ANOVA) on the emphysema scores. Our approach minimizes differences in emphysema measurement and highlights the impact of age, sex, smoking status and vendor on emphysema quantification.

翻译：计算机断层扫描（CT）生成过程中的重建内核决定了图像的纹理特征。由于CT纹理的差异性会影响定量图像分析中的测量结果，因此保持重建内核的一致性至关重要。统一（即内核转换）可减少因重建内核不一致导致的测量差异。现有方法研究了单一或多厂商CT扫描的统一问题，但这些方法需要空间和解剖结构上严格配准的硬重建内核与软重建内核配对扫描数据，且需针对厂商内不同内核组合训练大量模型。本研究采用非配对图像翻译方法，通过构建多路径循环生成对抗网络（GAN），研究不同厂商间重建内核的统一问题。我们使用国家肺癌筛查试验数据集中西门子和通用电气（GE）厂商的硬重建内核与软重建内核，从每种重建内核中选取50组扫描数据训练多路径循环GAN。为评估统一对重建内核的影响，我们将50组西门子硬内核、GE软内核及GE硬内核扫描数据统一至参考标准西门子软内核（B30f），并计算肺气肿百分比。通过构建包含年龄、吸烟史、性别及厂商因素的线性模型，对肺气肿评分进行方差分析（ANOVA）。本方法有效减小了肺气肿测量的差异，并揭示了年龄、性别、吸烟史及厂商因素对肺气肿定量分析的显著影响。