Comparison of depth of interaction encoding and resolution modelling image reconstruction in High Resolution PET imaging

Brain dedicated scanners such as the High Resolution Research Tomograph (HRRT) can achieve spatial resolutions of 2–3 mm FWHM due to their small scintillators crystal size and a double crystal layer phoswich detector which permits some level of depth of interaction (DOI) to minimise parallax errors. Resolution can further be improved by resolution models (RM) within image reconstruction which may be spatially invariant (SIRM) or variant (SVRM). In previous work we demonstrated substantial suppression of parallax errors on the HRRT using spatial variant kernel maps within a resolution modelling (RM) image reconstruction. This approach could potentially be used as an alternative to DOI electronics. In this work we compare the resolution improvements achieved through iterative reconstruction algorithms incorporating resolution models with and without DOI. Greater resolution improvements were observed with RM reconstruction compared to DOI electronics however when both DOI and RM are employed additional benefits were still observed. In order to perform RM, assumptions which simplify the model are normally made, which might limit the resolution performance of RM algorithms. RM algorithms come with the penalty of introducing Gibb's artefacts which we observed to be more predominant when performing (SVRM) instead of more conservative approaches such as (SIRM).