Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images

Noise properties of emission tomographic reconstructed images are compared using maximum-likelihood-expectation-maximization (ML-EM) and filtered-backprojection (FBP) algorithms. Noise comparisons are made in terms of the covariance matrix which gives information on the noise magnitude and noise correlations. Noise properties are studied as a function of iteration for ML-EM and as a function of noise apodization filter for FBP. It is shown that FBP reconstruction spreads noise variance from image regions containing high count densities into regions of low count densities. It is demonstrated that at lower FBP filter cutoff frequencies the noise is correlated over relatively long distances and the correlation function has deep negative sidelobes. For ML-EM reconstruction it is shown that little noise variance is spread from high count density regions into low count regions and that at lower iteration number the noise is correlated over shorter distances than for FBP. For ML-EM, the correlation function has no negative sidelobes at low iteration numbers. It is concluded from these observations that ML-EM reconstruction offers properties that may exceed FBP in terms of detectability for certain emission tomographic imaging situations