MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

Flexible Alignment Super-Resolution Network for Multi-Contrast MRI

Magnetic resonance images play an essential role in clinical diagnosis by acquiring the structural information of biological tissue. However, during acquiring magnetic resonance images, patients have to endure physical and psychological discomfort, including irritating noise and acute anxiety. To make the patient feel cozier, technically, it will reduce the retention time that patients stay in the strong magnetic field at the expense of image quality. Therefore, Super-Resolution plays a crucial role in preprocessing the low-resolution images for more precise medical analysis. In this paper, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast magnetic resonance images Super-Resolution. The core of multi-contrast SR is to match the patches of low-resolution and reference images. However, the inappropriate foreground scale and patch size of multi-contrast MRI sometimes lead to the mismatch of patches. To tackle this problem, the Flexible Alignment module is proposed to endow receptive fields with flexibility. Flexible Alignment module contains two parts: (1) The Single-Multi Pyramid Alignmet module serves for low-resolution and reference image with different scale. (2) The Multi-Multi Pyramid Alignment module serves for low-resolution and reference image with the same scale. Extensive experiments on the IXI and FastMRI datasets demonstrate that the FASR-Net outperforms the existing state-of-the-art approaches. In addition, by comparing the reconstructed images with the counterparts obtained by the existing algorithms, our method could retain more textural details by leveraging multi-contrast images