PURPOSE: Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favourable trade-off between the suppression of noise and the retention of unique features present in the PET data.
METHODS: The reconstruction methods investigated were: the MR-informed conventional and spatially-compact kernel methods, referred to as KEM and KEM LVS (largest value sparsification) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison to post-smoothed MLEM, evaluated in terms of structural similarity index (SSIM), normalised root mean square error (NRMSE), bias and standard deviation. Both simulated BrainWeb (10 noise realisations) and real [18 F]FDG 3-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumours to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level.
RESULTS: For the high count simulated and real data study, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and post-smoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET unique regions to attain similarly low levels of bias as unsmoothed MLEM, whilst moderately improving the whole brain image quality for low levels of regularisation. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularisation term. For the low counts simulated dataset the KEM LVS method and to a lesser extent the HKEM method performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE and bias vs standard deviation.
CONCLUSION: For the reconstruction of noisy data, multiple MR-informed methods produce favourable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of post-smoothed MLEM.