Non Contrast

Non-contrast imaging, using techniques like non-contrast CT and MRI, aims to improve medical diagnosis and monitoring without the need for contrast agents, reducing risks and costs. Current research heavily utilizes deep learning, employing architectures such as U-Nets, convolutional neural networks, and transformers, to analyze non-contrast images for automated segmentation, lesion detection, and quantitative measurements of various organs and pathologies (e.g., coronary calcium, kidney volume, stroke lesion volume). These advancements offer the potential for faster, cheaper, and safer diagnostic tools across numerous medical specialties, improving patient care and streamlining clinical workflows. The focus is on improving the accuracy and generalizability of these AI-driven methods across diverse patient populations and imaging protocols.