Abstract
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.
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Keywords
- White Matter
- Grey Matter
- Mild Cognitive Impairment
- Standardize Uptake Value Ratio
- Inhomogeneity Correction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Raniga, P., Bourgeat, P., Villemagne, V., O’Keefe, G., Rowe, C., Ourselin, S. (2007). Spline Based Inhomogeneity Correction for 11C-PIB PET Segmentation Using Expectation Maximization. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75757-3_28
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DOI: https://doi.org/10.1007/978-3-540-75757-3_28
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