Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

Abstract

The resolution of a PET scanner (2.5–4.5mm for brain imaging) is similar to the thickness of the cortex in the (human) brain (2.5mm on average), hampering accurate activity distribution reconstruction. Many techniques to compensate for the limited resolution during or post-reconstruction have been proposed in the past and have been shown to improve the quantitative accuracy. In this study, state-of-the-art reconstruction techniques are compared on a voxel-basis for quantification accuracy and group analysis using both simulated and measured data of healthy volunteers and patients with epilepsy. MethodsMaximum a posteriori (MAP) reconstructions using either a segmentation-based or a segmentation-less anatomical prior were compared to maximum likelihood expectation maximization (MLEM) reconstruction with resolution recovery. As anatomical information, a spatially aligned 3D T1-weighted magnetic resonance image was used. Firstly, the algorithms were compared using normal brain images to detect systematic bias with respect to the true activity distribution, as well as systematic differences between two methods. Secondly, it was verified whether the algorithms yielded similar results in a group comparison study. ResultsSignificant differences were observed between the reconstructed and the true activity, with the largest errors when using (post-smoothed) MLEM. Only 5–10% underestimation in cortical gray matter voxel activity was found for both MAP reconstructions. Higher errors were observed at GM edges. MAP with the segmentation-based prior also resulted in a significant bias in the subcortical regions due to segmentation inaccuracies, while MAP with the anatomical prior which does not need segmentation did not. Significant differences in reconstructed activity were also found between the algorithms at similar locations (mainly in gray matter edge voxels and in cerebrospinal fluid voxels) in the simulated as well as in the clinical data sets. Nevertheless, when comparing two groups, very similar regions of significant hypometabolism were detected by all algorithms. ConclusionIncluding anatomical a priori information during reconstruction in combination with resolution modeling yielded accurate gray matter activity estimates, and a significant improvement in quantification accuracy was found when compared to post-smoothed MLEM reconstruction with resolution modeling. AsymBowsher provided the most accurate subcortical GM activity estimates. It is also reassuring that the differences found between the algorithms did not hamper the detection of hypometabolic regions in the gray matter when performing a voxel-based group comparison. Nevertheless, the size of the detected clusters differed. More elaborated and application-specific studies are required to decide which algorithm is best for a group analysis.