What is the PET reconstruction algorithm called that compares numerical projection data with measured projection data?

The reconstruction algorithm that effectively compares numerical projection data with measured projection data is known as the Filtered Back Projection. This method operates by projecting and back-projecting the data through the image space, which allows the algorithm to reconstruct the image from the raw data collected by the PET scanner.

In this technique, the measured projection data (which reflects how radioactive tracer distribution translates into detection signals) are filtered to enhance the quality of the reconstructed images. This filtering process accounts for various issues, such as reducing noise and artifacts that may arise during image acquisition. Consequently, Filtered Back Projection is particularly useful in reconstructing images because it creates a clearer representation of the underlying distribution of radioactive tracers in the body, allowing for better diagnosis and analysis.

While other algorithms like Maximum Likelihood Expectation Maximization, Iterative Reconstruction, and Ordered Subset Expectation Maximization certainly have their own unique applications and advantages in PET imaging, the direct comparison between numerical and measured projection data characterizes Filtered Back Projection, making it the most appropriate answer to the question.