Sparse View CT Reconstruction

Sparse-view computed tomography (SVCT) aims to reconstruct high-quality images from significantly reduced X-ray projection data, minimizing radiation exposure. Current research focuses on deep learning methods, particularly diffusion models and implicit neural representations, often incorporating multi-scale or multi-domain processing and task-specific sampling strategies to improve reconstruction accuracy and reduce artifacts. These advancements are crucial for enhancing the safety and efficiency of CT imaging in both medical diagnostics and industrial applications, enabling lower-dose scans while maintaining diagnostic image quality.