Cross Modality Synthesis

Cross-modality synthesis focuses on generating data in one modality (e.g., CT scan) from data in another (e.g., MRI scan), aiming to improve image quality, create synthetic datasets for training, or bridge information gaps between different imaging techniques. Current research employs various generative models, including diffusion models, GANs (Generative Adversarial Networks), and transformers, often incorporating techniques like subvolume merging or knowledge-based prompting to enhance synthesis accuracy and realism. This field is significant for its potential to improve medical imaging diagnostics, enable new forms of multimodal data analysis, and address data scarcity issues in various scientific domains, including engineering design and fashion.