Improving the fidelity of CT image colorization based on pseudo-intensity model and tumor metabolism enhancement

Subject to the principle of imaging, most medical images are gray-scale images. Human eyes are more sensitive to color images compared to gray-scale images. The state-of-the-art medical image colorization results are unnatural and unrealistic, especially in some organs, such as the lung field. We propose a CT image colorization network that consists of a pseudo-intensity model, tumor metabolic enhancement, and MemoPainter-cGAN colorization network. First, the distributions of both the density of CT images and the intensity of anatomical images are analyzed with the aim of building a pseudo-intensity model. Then, the PET images, which are sensitive to tumor metabolism, are used to highlight the tumor regions. Finally, the MemoPainter-cGAN is used to generate colorized anatomical images. Our experiment verified that the mean structural similarity between the colorized images and the original color images is 0.995, which indicates that the colorized image maintains the features of the original images enormously. The average image information entropy is 6.62, which is 13.4% higher than that of the images before metabolism enhancement and colorization. It indicates that the image fidelity is significantly improved. Our method can generate vivid and fresh anatomical images based on prior knowledge of tissue or organ intensity. The colorized PET/CT images with abundant anatomical knowledge and high sensitivity of metabolic information provide radiologists with access to a new modality that offers additional reference information.