Development of a motion correction and partial volume correction algorithm for high resolution imaging in Positron Emission Tomography

Abstract

Since its inception around 1975, Positron Emission Tomography (PET) has proved to be an important tool in medical research as it allows imaging of the brain function in vivo with high sensitivity. It has been widely used in clinical dementia research with [18F]2-Fluoro-2-Deoxy-D-Glucose (FDG) and amyloid tracers as imaging biomarkers in Alzheimer's Disease (AD). The high resolution offered by modern scanner technology has the potential to provide new insight into the interaction of structural and functional changes in AD. However, the high resolution of PET is currently limited by movement and resolution (even for high resolution dedicated brain PET scanner) which results in partial volume effects, the undersampling of activity within small structures. A modified frame-by-frame (FBF) realignment algorithm has been developed that uses estimates of the centroid of activity within the brain to detect movement and subsequently reframe data to correct for intra-frame movement. The ability of the centroid to detect motion was assessed and the added benefit of reframing data for real clinical scans with patient motion was evaluated through comparison with existing FBF algorithms. Visual qualitative analysis on 6 FDG PET scans from 4 blinded observers demonstrated notable improvements (ANOVA with Tukey test,p

Date of Award	1 Aug 2012
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Stephen Williams (Supervisor), Julian Matthews (Supervisor) & Karl Herholz (Supervisor)