A Multi-Objective Comparative Analysis of Reconstruction Algorithms in the Context of Low-Statistics 90Y-PET Imaging

The popularity of yttrium-90 (90Y) PET is growing. However, due to the very low branching ratio of 90Y (3.2e-5), images reconstructed are characterized by a high noise level and positive bias in low-activity regions. To overcome this problem, some algorithms use penalized reconstructions, and others allow for negative values in the image. Recently, a post-processing method has also been proposed that removes the induced negative values while maintaining bias reduction. The work presented in this paper aims to evaluate and compare these methods to guide the reader in the choice of the best-suited reconstruction algorithm and associated parameters. First, several algorithms were tested using experimental phantom data. Pareto fronts and Pareto sets from the multi-objective optimization formalism were used for the comparison. Next, a dosimetric study (using phantom and patient data) was conducted to assess the final impact of these algorithms. The lowest biases were reached by unconstrained algorithms. When compared to penalized algorithms, the latter allowed for noise reduction at a fixed level of bias, with the best results obtained using a penalty based on relative differences. A good compromise between noise and bias could be reached by combining penalty, early stopping of iterative algorithms, unconstrained algorithms, and post-processing.