In how many kinetic classes can [11C]-R-PKll195 brain PET data be segmented?

Rainer Hinz*, Ronald Boellaard, Federico E. Turkheimer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Kinetic analysis of brain PET data with PK11195 frequently uses data partitioning techniques for the extraction of a reference tissue kinetic class. To date, these unsupervised or supervised clustering methods have not yet addressed the question of the optimal number of clusters to extract in total. Here, results from k-means clustering into 2 to 10 classes of a cohort of 12 non-diseased subjects are presented. To characterise the separation, the Mahalanobis distance is used to measure the distance between the centroids and the other clusters. The cluster maps suggest the presence of about 3 distinguishable clusters in brain tissue and a further 2 to 3 extracerebral clusters. The maximum mean Mahalanobis distance was observed for 7 clusters.

Original languageEnglish
Title of host publication2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008
Pages4459-4463
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2008
Event2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008 - Dresden, Germany
Duration: 19 Oct 200825 Oct 2008

Conference

Conference2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008
CountryGermany
CityDresden
Period19/10/200825/10/2008

Cite this

Hinz, R., Boellaard, R., & Turkheimer, F. E. (2008). In how many kinetic classes can [11C]-R-PKll195 brain PET data be segmented? In 2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008 (pp. 4459-4463). [4774272] https://doi.org/10.1109/NSSMIC.2008.4774272