A New MLEM Reconstruction Algorithm for Ultra-low Dose PET

This study introduces a novel ML-EM estimation method for reconstructing images in positron emission tomography. The concept proposed here utilizes a continuous-to-continuous data model, with the reconstruction problem expressed as a shift-invariant system. The primary objective of this research is to illustrate the methodology founded on probabilistic principles, emphasizing the consideration of statistical characteristics of PET signal data. The central focus of this paper is to establish that our method is grounded in statistical theory, offering alternative strategies to improve image resolution in low-dose PET scans.