Intercomparison of MR‐informed PET image reconstruction methods

Abstract

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 favorable 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 largest value sparsification (LVS) 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 with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18F] fluorodeoxyglucose (FDG) three‐dimensional datasets were used. The real [18F]FDG dataset was augmented with simulated tumors 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 studies, the anato‐functional MAP method performed better than the other methods under investigation (MR‐informed, PET‐MR‐informed and postsmoothed 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, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato‐functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM 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 t...

Anatomically aided PET image reconstruction using deep neural networks

“…Similar to previously reported methods, 16 the anatofunctional methods which combine both anatomical and functional information, perform better than anatomy‐aided only methods. It also should be noted that two co‐learning strategies behave differently in terms of recovering small lesions, while both MC and MB have similar noise reduction ability.…”

“…This method has been extended to incorporate both PET and MRI derived kernel matrices based on the structural similarity metric 13 . Other interesting methods and comparisons between them can be found in 14–16 . All of the above methods try to find an appropriate similarity measure to weigh PET and MRI features differently.…”

Quantitative bone SPECT/CT reconstruction utilizing anatomical information

“…A large variety of reconstruction methods utilizing anatomical information have been presented in the literature [5][6][7][14][15][16][17][18]. They differ in terms of the energy function and also on how the anatomical information is incorporated into the emission data reconstruction algorithm [19]. The two Bayesian algorithms utilizing anatomical information AMAP-S and AMAP-R, used in this study, have been well studied previously.…”

(An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

“…This has been used with the Radial Basis Function (RBF) similarity function (19) for multi-tracer PET by Ellis et al [74], who sparsified the weights by keeping only the n max largest weights for each voxel, similar to the Bowsher prior. 1 Bland et al extended this idea to PET/MRI by further adapting the weights over iterations by including Gaussian differences of the PET image of the previous update [75].…”