GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement

Abstract

Computed tomography (CT) is a widely-used diagnostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging protocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. One approach to address the problem is to standardize and normalize CT images using image synthesis algorithms including generative adversarial network (GAN) models. GAN learns the data distribution of training images and generate synthesized images under the same distribution. However, existing GAN models are not directly applicable to this task mainly due to the lack of constraints on the mode of data to generate. Furthermore, they treat every image equally, but in real applications, certain images are more difficult to standardize than the others. All these may lead to the lack-of-detail problem in CT image synthesis. We present a new GAN model called GANai to mitigate the differences in radiomic features across CT images captured using non-standard imaging protocols. Given source images, GANai composes new images by specifying a high-level goal that the image features of the synthesized images should be similar to those of the standard images. GANai introduces a new alternative improvement training strategy to alternatively and gradually improve GAN model performance. The new training strategy enables a series of technical improvements, including phase-specific loss functions, phase-specific training data, and the adoption of ensemble learning, leading to better model performance. The experimental results show that efficiency and stability of GAN models have been much improved in GANai and our model is significantly better than the existing state-of-the-art image synthesis algorithms on CT image standardization.