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

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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<0.001) and time-activity curves were found to deliver biologically more plausible activity concentrations.A new method for Partial Volume Correction (PVC) is also proposed, PARtially-Segmented Lucy-Richardson (PARSLR),that combines the strength of image based deconvolution approach of the Lucy-Richardson (LR) Iterative Deconvolution Algorithm with a partial segmentation of homogenous regions. Such an approach is of value where reliable segmentation is possible for part but not all of the image volume or sub-volume. Its superior performance with respect to region-based methods like Rousset or voxel-based methods like LR was successfully demonstrated via simulations and measured phantom data. The approach is of particular importance for studies with pathological abnormalities where complete and accurate segmentation across or with a sub-volume of the image volume is challenging and for regions of the brain containing heterogeneous structures which cannot be accurately segmented from co-registered images.The developed methods have been shown to recover radioactivity concentrations from small structures in the presence of motion and limited resolution with higher accuracy when compared to existing methods. It is expected that they will contribute significantly to future PET studies where accurate quantitation in small or atrophic brain structures is essential.

Keyword(s)

Head movements; PARSLR; motion correction; partial volume correction; positron emission tomography; resolution recovery

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