Low Dose

Low-dose imaging aims to minimize radiation exposure in medical imaging while preserving diagnostic image quality. Current research heavily focuses on deep learning techniques, employing architectures like U-Nets, transformers, and generative adversarial networks (GANs), often combined with traditional methods like bilateral filtering or expectation-maximization algorithms, to denoise and reconstruct images from low-dose scans. These advancements are crucial for improving patient safety and reducing healthcare costs by enabling lower radiation doses in various modalities, including CT, MRI, and PET scans. The ultimate goal is to achieve image quality comparable to standard-dose scans with significantly reduced radiation risk.