Advancements in Cross-modality Imaging: A Survey of CNN and GAN Applications in Medical Imaging

This paper presents a comprehensive survey of recent advancements in cross-modality imaging techniques. It focuses on the application of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) in medical imaging. Cross-modality imaging involves translating images from one modality to another (E.g.: MRI to CT scans) and can assist medical experts in accurate diagnostics and patient care. CNNs and GANs have been quite popular in various fields and their abilities to work with images and generate high quality outputs are quite an advantage for a situation like this. This survey highlights several studies utilizing CNN methodologies for various applications, including synthetic MRI generation, MRI to CT translations for different anatomical regions and bone structure identification. GANs, on the other hand, excel in generative modeling by training two neural networks, the generator and the discriminator, in a competitive setting. We discuss their application in generating missing MRI modalities, creating 3D medical images, and producing synthetic CT scans for radiotherapy. The integration of CNN and GAN models in cross-modality imaging shows significant promise in improving image quality, reducing noise, and enhancing the accuracy of diagnostic tools. This survey underscores the importance of advanced deep learning techniques in medical imaging and sets the stage for future research in this rapidly evolving field.