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).

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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References:

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Bentourkia M. A flexible image segmentation prior to parametric estimation. Comput Med Imaging Graph. 2001; 25: 501-506. doi: 10.1016/S0895-6111(01)00016-7.

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Gunn RN, Lammertsma AA, Cunningham VJ. Parametric imaging of ligand-receptor interactions using a reference tissue model and cluster analysis. In: Quantitative Functional Brain Imaging with Positron Emission Tomography. Academic Press, 1998, pp 401-406.

Herholz K, Heiss WD, Pietrzyk U, Wienhard K. Pixel-by-pixel fits of blood volume, transport, and metabolic processes: principle, normal values, and brain tumor studies with dynamic FDG-PET. In: Beckers C, Goffinet A, Bol A (eds.): Positron Emission Tomography in Clinical Research and Clinical Diagnosis: Tracer Modelling and Radioreceptors, pp 148-161. Kluwer, 1989. ISBN: 0-7923-0254-0.

Kimura Y, Hsu H, Toyama H, Senda M, Alpert NM. Improved signal-to-noise ratio in parametric images by cluster analysis. NeuroImage 1999; 9(5): 554-561. doi: 10.1006/nimg.1999.0430.

Kimura Y. Formation of parametric images with statistical clustering. Int Congr Ser. 2004; 1265: 25-30.

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

Liptrot M, Adams KH, Martiny L, Pinborg LH, Lonsdale MN, Olsen NV, Holm S, Svarer C, Knudsen GM. Cluster analysis in kinetic modelling of the brain: a noninvasive alternative to arterial sampling. NeuroImage 2004; 21: 483-493. doi: 10.1016/j.neuroimage.2003.09.058.

Mateos-Pérez JM, García-Villalba C, Pascau J, Desco M, Vaquero JJ. jClustering, an open framework for the development of 4D clustering algorithms. PLoS One 2013; 8(8): e70797. doi: 10.1371/journal.pone.0070797.

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.

Saad A, Smith B, Hamarneh G, Möller T. Simultaneous segmentation, kinetic parameter estimation, and uncertainty visualization of dynamic PET images. In: Ayache N, Ourselin S, Maeder A (eds): MICAAI 2007, Part II, LNCS 4792, pp 726-733, 2007.

Terry JL, Crampton A, Talbot CJ. Associating families of curves using feature extraction and cluster analysis. In: Algorithms for Approximation. Proceedings of the 5th International Conference, Chester, July 2005. Iske A, Levesley J (Eds.), Springer, 2007, pp 71-80.

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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Created at: 2015-02-25
Updated at: 2018-06-01
Written by: Vesa Oikonen