Structure-Enhanced Translation from PET to CT Modality with Paired GANs

Computed Tomography (CT) images play a crucial role in medical diagnosis and treatment planning. However, acquiring CT images can be difficult in certain scenarios, such as patients inability to undergo radiation exposure or unavailability of CT scanner. An alternative solution can be generating CT images from other imaging modalities. In this work, we propose a medical image translation pipeline for generating high-quality CT images from Positron Emission Tomography (PET) images using a Pix2Pix Generative Adversarial Network (GAN), which are effective in image translation tasks. However, traditional GAN loss functions often fail to capture the structural similarity between generated and target image. To alleviate this issue, we introduce a Multi-Scale Structural Similarity Index Measure (MS-SSIM) loss in addition to the GAN…