Lung attenuation coefficient estimation using Maximum Likelihood reconstruction of attenuation and activity for PET/MR attenuation correction

MR-based PET attenuation correction using segmented MR images is a promising approach to compensating for the lack of a transmission source in PET/MR and solving the problem of attenuation correction. However, the method is prone to errors in the choice of patient-individual lung attenuation coefficients (LAC), which are difficult to determine from MR data. Maximum Likelihood reconstruction of Attenuation and Activity (MLAA) can be applied to reconstruct attenuation maps from PET emission data. We present a constrained MLAA variant using segmentation of the lungs and tissue classification of the rest of the body, and focus on the evaluation of estimated mean LACs. In simple simulation studies, mean LACs can be estimated with errors as low as 5%, while in realistic ones, uncorrected out-of-field accidental coincidences seem to introduce bias.