Sinogram-based regularization parameter learning for 2D penalized maximum-likelihood PET image reconstruction

Positron emission tomography (PET) is a molecular imaging technique that creates an image of radiopharmaceutical distribution using acquired sinogram data. Accurate PET reconstruction requires a balance between data fidelity and regularization. The choice of penalty type and the adjustment of the regularization parameters have a critical effect on image quality, including noise suppression, edge preservation, and contrast recovery. However, manually determining optimal regularization strength is challenging due to their dependence on data properties and clinical needs. It often requires multiple reconstructions and considerable time to achieve satisfying results. To address this issue, we proposeSINORES, a supervised deep learning approach to predict the optimal regularization parameters for the modified block sequential regularized expectation maximization algorithm. The prediction is based on the raw sinogram data and the scaling coefficients that encode acquisition-related properties. By learning from a synthetic dataset of 2D sinograms and scaling coefficients paired with their corresponding optimal parameters,SINORESrapidly identifies suitable parameter values and avoids the need for manual setting. This work presents a proof of concept that demonstrates the feasibility of the proposed framework in the context of 2D PET imaging. The proposed method achieves consistent parameter estimation across different phantom types and effectively determines suitable parameters for reconstructing real PET data, leading to improved reconstruction quality and reliability in practical settings.