Image Synthesis in Medical Imaging

Image Synthesis in medical imaging refers to generating a target image modality from a given source modality. It has the potential to enhance the diagnostic efficiency of the clinical workflow by bypassing an imaging procedure when the acquisition is infeasible, or the information fusion between different image modalities is too challenging. Image Synthesis has been an active area of research in Radiology, with the rapid progress in the field of Computer Vision and Deep learning, advanced image synthesis techniques have led to better-generated images and sparked a growing interest in numerous clinical applications, such as MR-only radiotherapy planning.

The goal of this Research Topic is to offer opportunities to present the latest methodological developments and clinical applications of Image Synthesis.

We welcome submissions of both research and review articles covering but not limited to the following topics:
- Cross-modality (MR/CT, PET/MR, etc.) image synthesis
- Techniques for quality assessment of image synthesis
- Uncertainty in image synthesis
- Image synthesis in high-dimension space
- Large databases for image synthesis
- Segmentation/Registration using synthetic images
- Applications of image synthesis in image super-resolution
- Applications of image synthesis in image denoising
- Applications of image synthesis in image reconstruction

Keywords: Image Synthesis, Image Translation, Deep Learning, Radiation Therapy, MRI, CT, PET

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.