PET image clustering

Image clustering is a method where image voxels are grouped by the similarity of their time courses (Ashburner et al., 1996). The volumes-of-interest (VOIs) are created automatically and objectively, instead of manual VOI drawing, and are based on functional, not anatomical, data. Clustering process may include spatial constraints, for example when used to delineate tissue lesions. Enhanced random walk algorithm for VOI delineation (Stefano et al., 2017) includes clustering step.

Clustering could be used to extract image-derived input function. Spatial information is not used in extraction of reference region curve, because in many brain diseases the pathology is widely spread throughout the entire brain, and an anatomically defined reference region cannot be found (Gunn et al., 1998). Tissue TACs with different kinetics are needed for model-based input function, and clustering could be used for delineation of ROIs for this purpose (Zheng et al., 2011).

Clustering can also be applied in calculation of parametric images to reduce the computation times and to improve signal-to-noise ratio (Kimura et al., 1999; Bentourkia, 2001). Compartmental model fitting and clustering can be combined to further enhance the quality of parametric image in case of very noisy data (Mohy-ud-Din et al., 2015).

The similarity of two time-activity curves should be not be based on sum-of-squares alone. Other straight-forward approaches are the Akaike Information Criterion (Kletting et al., 2009), runs test, and maximum run length (MRL) (Herholz et al., 1989).

The k-means method remains as the most widely used clustering method because of its simplicity. Its speed and accuracy can be improved with seeding techniques, such as k-means++.

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Kletting P, Kull T, Reske SN, Glatting G: Comparing time activity curves using the Akaike information criterion. Phys Med Biol. 54: N501-N507, 2009. doi: 10.1088/0031-9155/54/21/N01

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Mohy-ud-Din H, Lodge MA, Rahmim A. Quantitative myocardial perfusion PET parametric imaging at the voxel-level. Phys Med Biol. 2015; 60: 6013-6037. doi: 10.1088/0031-9155/60/15/6013.

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Wong K-P, Feng D, Meikle SR, Fulham MJ. Segmentation of dynamic PET images using cluster analysis. IEEE Trans Nucl Sci. 2002; 49(1): 200-207. doi: 10.1109/TNS.2002.998752.

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Updated at: 2019-01-11
Created at: 2015-02-25
Written by: Vesa Oikonen