Transforming PET Imaging with AI: Achieving High-Quality Images at Reduced Radiation Levels

Positron Emission Tomography (PET) is an important diagnostic tool in medicine that provides information about tissue function and metabolism, but it involves radiation exposure, which can be risky for patients needing multiple scans. To address this, low-dose PET protocols have been created, although they often result in lower image quality due to increased noise. Recent advancements in artificial intelligence (AI), particularly deep learning techniques like convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown promise in enhancing low-dose PET images to achieve quality comparable to full-dose scans. Studies have demonstrated that AI can effectively reduce noise and improve image resolution, maintaining diagnostic accuracy while minimizing radiation exposure. However, challenges such as the need for large, diverse datasets and the interpretability of AI models remain. Overcoming these challenges is essential for the successful integration of AI into clinical practice, potentially making low-dose PET imaging safer and more effective for patients.

کلیدواژه ها:

Positron Emission Tomography ، Radiation Dosage ، Artificial Intelligence ، Image Enhancement Techniques