Sparse View Computed Tomography

Sparse-view computed tomography (CT) aims to reconstruct high-quality images from significantly reduced projection data, minimizing radiation exposure in medical and industrial applications. Current research focuses on improving reconstruction accuracy using advanced deep learning models, such as diffusion models and implicit neural representations, often incorporating novel loss functions designed to enhance signal detectability and address artifacts. These advancements are crucial for improving the quality and safety of CT scans, particularly in applications requiring low-dose imaging like breast cancer screening.