What does iterative reconstruction primarily aim to reduce in imaging?

Iterative reconstruction primarily aims to reduce noise and artifacts in imaging. This advanced algorithmic approach enhances image quality by processing the acquired raw data multiple times, re-evaluating and improving the image after each iteration. As a result, the final images exhibit significantly less noise and improved detail compared to traditional methods, which can be particularly beneficial in low-dose CT scans where the risk of noise is higher.

By focusing on refining the imaging while maintaining or potentially reducing the radiation dose, iterative reconstruction techniques enhance the clarity and diagnostic accuracy of the images. This is especially important in medical imaging, where clear images can lead to better decision-making and patient outcomes. This enhances the overall utility of CT scans in clinical practice, making it easier to identify pathologies and abnormalities.