Maximum-Likelihood Expectation-Maximization Algorithm Versus Windowed Filtered Backprojection Algorithm: A Case Study.

Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative algorithms are able to reduce noise without sacrificing image resolution, and thus iterative algorithms, especially maximum-likelihood expectation maximization (MLEM), are used in nuclear medicine to replace FBP algorithms. This short paper uses counter examples to show that this belief is not true. We compare image noise variance for FBP and MLEM reconstructions having the same spatial resolution. The truth is that although MLEM suppresses image noise, it does so by sacrificing image resolution as well; the performance of windowed FBP may be better than that of MLEM in our case study. The myth of the superiority of iterative algorithms is caused by comparing them with conventional FBP instead of with windowed FBP. However, we do not intend to generalize the comparison results to imply which algorithm is more favorable.