Tensor-Based Statistical Algorithms for Positron Emission Tomography Partial Volume Correction

Abstract

The partial volume effect in Positron Emission Tomography (PET) is a problem for quantitative radiotracer studies. These studies can be used to study many well-known diseases such as Epilepsy. However partial volume effects can cause misinterpretation of the data. The talk will firstly introduce PET and then discuss the partial volume problem. This results from the limited spatial resolution of the imaging device (a few mm's) and results in a blurring of the data. Two factors are involved for pre-defined regions; spillover of radioactivity into neighboring regions and the underlying tissue inhomogeneity (mixed tissue types) of the particular region. Linear modeling methods have previously been used to correct for this effect on a regional level, using tissue classification from higher resolution imaging modalities, e.g. Magnetic Resonance Imaging, and anatomically defined regions which are assumed to contain homogeneous radiotracer (the PET data source) concentrations. We extend these methods to incorporate the underlying noise structure of the PET measurements, and develop fast tensor based algorithms to facilitate the computation of true radiotracer concentration estimates and their associated errors. Computationally efficient algorithms are essential due to the massive nature of the datasets where there is intrinsic spatial correlation in the data.