Quantitatively accurate image reconstruction for clinical whole-body PET imaging

We present a PET image reconstruction approach that aims for accurate quantitation through model-based physical corrections and rigorous noise control with clinically acceptable image properties. We focus particularly on image generation chain components that are critical to quantitation such as physical system modeling, scatter correction, patient motion correction and regularized image reconstruction. Through realistic clinical datasets with inserted lesions, we demonstrate the quantitation improvements due to detector point spread function modeling, model-based single scatter estimation and the associated object-dependent multiple scatter estimation and non-rigid patient motion estimation and motion correction. We also describe a penalized-likelihood (PL) whole-body clinical PET image reconstruction approach using the relative difference penalty that achieves superior quantitation over the clinically-widespread ordered subsets expectation maximization (OSEM) algorithm while maintaining visual image properties similar to OSEM and therefore clinical acceptability. We discuss the axial and in-plane smoothing modulation profiles that are necessary to avoid large variations in noise and resolution levels. The overall approach of accurate models for data acquisition, corrections for patient related effects and rigorous noise control greatly improve quantitation and when combined with repeatable imaging protocols, limit quantitation variability only to factors related to patient physiology and scanner performance differences.