  Quantitative accuracy and thus diagnostic precision in Emission Tomography is
impaired by the inherent random characteristics of the data acquisition leading
to statistical image noise. Edge preserving spatial variation regularized
iterative image reconstruction approaches require case-specific control
parameter adaptation for optimized contrast-vs-noise tradeoff. For MLEM
reconstruction, we propose and evaluate iRDF which automatically adapts RDP
edge preservation parameters according to local image noise and PET data
characteristics. In order to distinguish between clustered noise spots and
small tumors, we introduce hot-spot artifact correction. The proposed method
was evaluated using NEMA IQ phantom data as well as clinical patient data.
After initial iRDF base parameter tuning, results showed that iRDF maintained
similar image quality regardless of statistics without requiring manual
parameter tuning, in contrast to e.g. RDP. With NEMA-IQ phantom data, local
image variance was reduced to ~33%, while contrast of small spheres could be
mostly preserved compared to nonregularized OSEM. Using a quarter of the
originally acquired list-mode data, a noise decreased to ~22% while SUV-max has
been reduced to ~75% of OSEM-based results. NEMA phantom and clinical data
showed improved signal-recovery-to-noise ratios, leading to an overall ~3 times
higher feature detectability especially in small lesions. Finally, the
processed examples illustrate the effectiveness of the proposed hot-pixel
artefact correction. The proposed auto-adaptive iRDF regularization
demonstrates high potential to reduce the burden of prior parameter tuning. It
realizes a trade-off between feature contrast and image noise on both local and
global scale. According to increased noise robustness at different count
statistics, iRDF can be considered an interesting alternative especially for
low-dose PET imaging applications.
