Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features

• URL: http://arxiv.org/abs/2001.08741v1
• Date: Wed, 22 Jan 2020 23:41:29 GMT
• Title: Using a Generative Adversarial Network for CT Normalization and its Impact on Radiomic Features
• Authors: Leihao Wei and Yannan Lin and William Hsu
• Abstract summary: Radiomic features are sensitive to differences in acquisitions due to variations in dose levels and slice thickness. A 3D generative adversarial network (GAN) was used to normalize reduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice (1.0mm) images.
• Score: 3.4548443472506194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radiomic features are sensitive to differences in acquisitions due to variations in dose levels and slice thickness. This study investigates the feasibility of generating a normalized scan from heterogeneous CT scans as input. We obtained projection data from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and 50% dose and reconstructing the scans at 1.0mm and 2.0mm slice thickness. A 3D generative adversarial network (GAN) was used to simultaneously normalize reduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice (1.0mm) images. We evaluated the normalized image quality using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS). Our GAN improved perceptual similarity by 35%, compared to a baseline CNN method. Our analysis also shows that the GAN-based approach led to a significantly smaller error (p-value < 0.05) in nine studied radiomic features. These results indicated that GANs could be used to normalize heterogeneous CT images and reduce the variability in radiomic feature values.

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