Comparison of two statistical image reconstruction algorithms for quantitative assessment of pathological lesions using gamma emission tomography

Abstract

This study compares two statistical approaches to image reconstruction in single-photon emission computed tomography (SPECT). We evaluated the widely used Ordered Subset Expectation Maximization (OSEM) algorithm and the newer Maximum a Posteriori approach with Entropy prior (MAP-Ent) approach in the context of quantifying radiopharmaceutical uptake in pathological lesions. Numerical experiments were performed using a digital twin of the standardized NEMA IEC phantom, which contains six spheres of varying diameters to simulate lesions. Quantitative accuracy was assessed using the maximum recovery coefficient (RCmax), defined as the ratio of the reconstructed maximum activity to the true value. The study shows that OSEM exhibits unstable convergence during iterations, leading to noise and edge artifacts in lesion images. Post-filtering stabilizes the reconstruction and ensures convergence, producing RCmax-size curves that could be used as correction factors in clinical evaluations. However, this approach significantly underestimates uptake in small lesions and may even lead to the complete loss of small lesions on reconstructed images. In contrast, MAP-Ent demonstrates fundamentally different behavior: it achieves stable convergence and preserves quantitative accuracy without post-filtering, while maintaining the contrast of even the smallest lesions. However, the iteration number at which accurate reconstruction is achieved depends strongly on the choice of a single global regularization parameter, which limits optimal performance across lesions of different sizes. These results demonstrate the need for locally adaptive regularization in MAP-Ent to improve quantitative accuracy in lesion reconstruction.