Cross-validation stopping rule for ML-EM reconstruction of dynamic PET series: effect on image quality and quantitative accuracy

A major shortcoming of the Maximum Likelihood Expectation Maximization (ML-EM) method for reconstruction of dynamic PET images is to decide when to stop the iterative process for image frames with largely different statistics and activity distributions. A widespread practice to overcome this problem involves over-iteration of an image estimate followed by smoothing. In this work, the authors investigate the qualitative and quantitative accuracy of the cross-validation procedure (CV) as a stopping rule, in comparison to over-iteration and post-filtering. For the reconstruction of phantom and small animal dynamic FDG-PET data acquired in 2-D mode. The CV stopping rule ensured visually acceptable image estimates with balanced resolution and noise characteristics. However, quantitative accuracy required more than 10 ⁵ events per image. The effect of the number of ML-EM iterations on time-activity curves and metabolic rates of glucose extracted from image series is discussed. A dependence of the CV defined number of iterations on projection counts was found which simplifies reconstruction and reduces computation time