Multi Contrast

Multi-contrast imaging leverages the complementary information from multiple imaging modalities to improve diagnostic accuracy and efficiency, primarily in medical imaging (MRI, CT) and remote sensing. Current research focuses on developing deep learning models, including transformers, diffusion models, and convolutional neural networks, to address challenges like data scarcity, image reconstruction from undersampled data, and synthesis of missing contrasts. These advancements aim to reduce scan times, improve image quality, and enable more robust and accurate analyses, ultimately impacting clinical workflows and scientific understanding across various fields.