Clustering-Based Linear Least Square Fitting Method for Generation of Parametric Images in Dynamic FDG PET Studies
Parametric images generated from dynamic positron emission tomography (PET) studies are useful for presenting functional/biological information in the 3-dimensional space, but usually suffer from their high sensitivity to image noise. To improve the quality of these images, we proposed in this study...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2007-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2007/65641 |
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| Summary: | Parametric images generated from dynamic positron emission tomography (PET)
studies are useful for presenting functional/biological information in the
3-dimensional space, but usually suffer from their high sensitivity to image noise.
To improve the quality of these images, we proposed in this study a modified
linear least square (LLS) fitting method named cLLS that incorporates a
clustering-based spatial constraint for generation of parametric images from
dynamic PET data of high noise levels. In this method, the combination of
K-means and hierarchical cluster analysis was used to classify dynamic PET data.
Compared with conventional LLS, cLLS can achieve high statistical reliability in
the generated parametric images without incurring a high computational burden.
The effectiveness of the method was demonstrated both with computer simulation
and with a human brain dynamic FDG PET study. The cLLS method is expected
to be useful for generation of parametric images from dynamic FDG PET study. |
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| ISSN: | 1687-4188 1687-4196 |