Information-Adaptive Denoising for Iterative PET Reconstruction

Understand

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.