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 (2.5-4.5 mm for brain imaging) is similar to the thickness of the cortex in the (human) brain (2.5 mm on average), hampering accurate activity distribution reconstruction. Many techniques to compensate for the limited resolution during or postreconstruction 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. Methods: Maximum 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 3-D 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. Results: Significant differences were observed between the reconstructed and the true activity, with the largest errors when using (postsmoothed) 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. Conclusion: Including 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 more 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.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis

“…In our future work, we plan to simulate lesions in some phantom cases [40] and use more quantitative metrics (e.g. root mean squared analysis and overlap quantification) [46–48] to comprehensively evaluate the algorithm. Third, our current model does not deal with missing modalities, e.g., some subjects may not have a complete set of image modalities, which will make them excluded from the study and thus reduce the number of applicable cases.…”

Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies

“…These models contained cerebellar white and gray matter, brain white and gray matter, cerebrospinal fluid, skull, eyes, muscle, fat, and skin. We assigned a standard 18 F-FDG PET activity to each tissue according to relative values (gray matter, 4.0; white matter and rest of soft tissue, 1.0; cerebrospinal fluid and bone, 0.0) (24,25), obtaining the groundtruth PET maps. Then, these ground-truth maps were projected with the 3D ordered-subsets expectation maximization software (26), assuming the geometry, parameters, and sinogram format of the Biograph mMR scanner (27).…”

Advances in PET/MR instrumentation and image reconstruction

“…169 However, such an approach is exposed to segmentation errors. Alternatively, other methods avoid the segmentation step, like the Bowsher function 170 and modifications, [171][172][173] or approaches based on joint entropy between PET and MR images. 174,175 A comparison among some of the above-described approaches, using a simulated brain phantom with 18 F-FDG, is shown in Figure 7.…”