How does OSEM relate to MLEM according to processing techniques?

OSEM, or Ordered Subset Expectation Maximization, has a direct relationship with MLEM, which stands for Maximum Likelihood Expectation Maximization, in the context of image reconstruction techniques in Positron Emission Tomography (PET). The two methods share similar underlying principles of iterative reconstruction, yet OSEM improves upon MLEM by enhancing efficiency.

The core idea behind MLEM is that it utilizes the complete dataset in each iteration to optimize the image reconstruction process. This approach, while effective in producing high-quality images, can be computationally intensive and time-consuming, especially with large datasets.

OSEM addresses this limitation by dividing the data into subsets and performing reconstruction in a way that utilizes only part of the data at each iteration. By processing these subsets rather than the entire dataset, OSEM significantly accelerates the convergence of the iterative reconstruction process. Thus, while both techniques aim to achieve high-resolution images, OSEM does so in a considerably faster manner than MLEM.

This efficiency makes OSEM more practical for clinical applications where time and resources are critical factors. The improvement in speed without sacrificing image quality is what sets OSEM apart as a favored method in modern PET imaging protocols.