Dynamic Contrast-Enhanced MRI Parameter Mapping for Cervical Cancer Using CycleGAN-like model with UNet-Vision Transformer

Motivation: DCE-MRI plays an important role in non-invasive detection and monitoring of cervical cancer, providing key information for improving diagnosis and treatment accuracy. Goal(s): DCE-MRI faces complexities and noise issues in application and needs to be optimized and improved by deep learning techniques for parameter mapping. Existing deep learning based methods suffer from limited data and model efficiency. Approach: We propose a CycleGAN-like model with UNet-Vision-Transformer generator, enhance the discriminator with gradient penalty, and pre-train the model via self-supervised image inpainting. Results: The numerical experimental results demonstrate that the proposed model is quite efficient and robust compared with other deep learning-based methods. Impact: This research offers fresh avenues for processing medical imaging data by proposing a novel and efficient deep learning model, significantly impacting the improvement of disease diagnosis. Furthermore, it provides researchers with new directions and insights, advancing scientific and technological progress.