Image Processing in MRI Guided Real-TimeAdaptive Radiotheraphy - Upsampling and Segmentation of Target Volume and Organs at Risk

Abstract: Magnetic Resonance Imaging (MRI) is a useful medical imaging technique that is used for cancer treatment.The major drawback of this method is the relatively long scan time, limiting its use for real time tracking of a potentially moving target during the radiotherapy session. In this work, we aim to develop a real-time segmentation method that generates high-resolution segmentation by combining prior knowledge about the patient geometry MRI and the online low-resolution MRI image data.The intended approach is based on Generative Adversarial Networks(GAN),which generate high-resolutionsegmentation based on the low-resolution images acquired during treatment. The two GAN networks implemented in this work are - Brain MRI super-resolution using 3D generative adversarial networks(3D GAN) and Super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI (SegSRGAN). The visual and numerical results, such as PSNR and SSIM show that the 3D GAN network has produced better SR reconstruction images compared to SegSRGAN network. Furthermore, SegSRGAN has produced promising results simultaneously for the SR reconstruction and multi-organ segmentation of Rectum, Bladder and Prostate. We conclude by implementing different GAN frameworks to develop real-time segmentation that generates high-resolution segmentation from low-resolution MRI images and could possibly, reduce the scan time.