Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion

Objective: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (QCT) measures.

Methods: Patients with idiopathic pulmonary fibrosis (IPF) who underwent unenhanced chest CTs with vendor A and a pulmonary function test (PFT) were retrospectively evaluated. As deep-learning based ILD quantification software was mainly developed using vendor B CT, style-converted images from vendor A to B style were generated using RouteGAN. Quantification was performed in both original and converted images. Measurement variability in QCT between original and converted images was evaluated using the concordance correlation coefficient (CCC). Two radiologists visually evaluated quantification accuracy using original and converted images. Correlations between CT parameters and PFT measures were assessed.

Results: Total 112 patients (mean age, 61; 82 men) were studied. Measurement variability between original and converted CT was a CCC of 0.20 for reticulation, 0.72 for honeycombing, and 0.59 for ground-glass opacity. The median visual accuracy scores were higher for the quantification using converted compared with the original images (P < 0.001). Correlation between fibrosis score increased significantly after CT conversion for both forced vital capacity (original vs. converted; -0.35 vs. -0.50; P = 0.005) and diffusing capacity of the lung for carbon monoxide (-0.50 vs. -0.66; P < 0.001).

Conclusion: The improved accuracy in deep learning based ILD quantification after applying GAN-based CT style conversion can result in the improved functional correlation of QCT measurements in patients with IPF.