Spline based inhomogeneity correction for 11C-PIB PET segmentation using expectation maximization.
With the advent of biomarkers such as 11C-PIB and the increase in use of PET, automated methods are required for processing and analyzing datasets from research studies and in clinical settings. A common preprocessing step is the calculation of standardized uptake value ratio (SUVR) for inter-subject normalization. This requires segmented grey matter (GM) for VOI refinement. However 11C-PIB uptake is proportional to amyloid build up leading to inhomogeneities in intensities, especially within GM. Inhomogeneities present a challenge for clustering and pattern classification based approaches to PET segmentation as proposed in current literature. In this paper we modify a MR image segmentation technique based on expectation maximization for 11C-PIB PET segmentation. A priori probability maps of the tissue types are used to initialize and enforce anatomical constraints. We developed a Bézier spline based inhomogeneity correction techniques that is embedded in the segmentation algorithm and minimizes inhomogeneity resulting in better segmentations of 11C-PIB PET images. We compare our inhomogeneity with a global polynomial correction technique and validate our approach using co-registered MRI segmentations.
BioMedIA Lab, e-Health Research Centre, CSIRO ICT Centre, Brisbane, Australia.
Parnesh
P
Raniga
Pierrick
P
Bourgeat
Victor
V
Villemagne
Graeme
G
O'Keefe
Christopher
C
Rowe
Sébastien
S
Ourselin
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