Which reconstruction method involves multiple passes to achieve final image quality with reduced noise and artifacts?

Iterative Reconstruction is the appropriate choice because it uses multiple computational passes to refine images, effectively reducing noise and minimizing artifacts. This technique adjusts the image incrementally, analyzing discrepancies between the estimated image and the actual acquired data, which allows for continuous improvement in image quality. As the algorithm cycles through the data more than once, it progressively enhances the image, leading to clearer visualization of critical anatomical structures.

In contrast, Conventional Reconstruction typically relies on a single pass through the raw data to generate images, which does not provide the same level of noise reduction as iterative methods. Fast Reconstruction methods emphasize rapid image generation, potentially sacrificing some detail and quality during the process. Averaged Reconstruction focuses on averaging multiple images to minimize noise but lacks the sophisticated iterative processing that enhances overall image fidelity like the iterative approach. Thus, Iterative Reconstruction stands out for its effective handling of noise and artifacts through its multi-pass methodology.