  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.
