Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

Anatomically driven image reconstruction algorithms have become very popular in positron emission tomography (PET) where they have demonstrated improved image resolution and quantification. This paper examines the effects of spatial inconsistency between MR and PET images in hot and cold regions of PET images using the hybrid kernelized expectation maximization (HKEM) machine learning method. Our evaluation was conducted on Jaszczak phantom and patient data acquired with the Biograph Siemens mMR. The results show that even a small shift can cause a significant change in activity concentration. In general, the PET-MR inconsistencies can induce the partial volume effect, more specifically the “spill-in” for cold regions and the “spill-out” for hot regions. The maximum change was about 100% for the cold region and 10% for the hot lesion using kernelized expectation maximization, against the 37% and 8% obtained with HKEM. The findings of this paper suggest that including PET information in the kernel enhances the robustness of the reconstruction in case of spatial inconsistency. Nevertheless, accurate registration and choice of the appropriate MR image for the creation of the kernel is essential to avoid artifacts, blurring, and bias.