MRI guided PET image filtering and partial volume correction

Abstract

Positron emission tomography (PET) image quantification is a challenging problem due to limited spatial resolution of acquired data and the resulting partial volume effects (PVE), which depend on the size of the structure studied in relation to the spatial resolution and which may lead to overor underestimation of the true tissue tracer concentration. Hybrid imaging devices such as PET/MRI can provide both functional PET image and higher definition morphologic image. In this study, first of all, we proposed a MRI guided PET filtering method by adapting a recently proposed local linear model and then incorporated PVE into the model to get a new partial volume correction (PVC) method without parcellation of MRI. The performance of the proposed methods were investigated with simulated dynamic FDG brain dataset and 18F-FDG brain data of a cervical cancer patient acquired with simultaneous hybrid PET/MR scanner. The initial simulation results demonstrated that MRI guided PET image filtering can produce less noisy image than traditional Gaussian filtering (GF) and bias and coefficient of variation (COV) can be further reduced by MRI guided PET PVC . In addition, structures can be much better delineated in MRI guided PET PVC with real brain data.