Combined Low-dose Simulation and Deep Learning for CT Denoising: Application in Ultra-low-dose Chest CT

In this study, we present a deep learning approach for denoising of ultra-low-dose chest CT by combining a low-dose simulation and convolutional neural network (CNN). A total of 18,456 anonymized regular-dose chest CT images were used for training of the CNN. The training CT images were fed into the low-dose simulation tool to generate a paired set of simulated low-dose CT and synthetic low-dose noise. A modified U-net model with 4x4 kernel size and five layers was trained with these paired datasets to predict the low-dose noise from the given low-dose CT image. Independent 10 ultra-low-dose chest CT scans at 120 kVp and 5 mAs were used for testing the denoising performance of the trained U-net. Denoised CT images were obtained by subtracting the predicted noise image from ultra-low-dose chest CT images. We evaluated the image quality by measuring noise standard deviation of soft tissue and with visual assessment of bronchial wall, lung fissure, and soft tissue. For comparison, the image quality was assessed on FBP, VEO, and deep learning-denoised FBP images. The visual assessment made with 4 points scale were 1.0, 3.4 and 4.0 in FBP, VEO, and deep learning-denoised FBP images. Image noise of soft tissue was 101 +/- 28HU, 20 +/- 5HU, 28 +/- 10HU in FBP, VEO, deep learning-denoised images.