Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging.

Abstract:

:Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov luminescence images (dCLI). In order to investigate the performances of the proposed approaches, two distinct dynamic data sets obtained by injecting mice with (32)P-ATP and (18)F-FDG were acquired using the IVIS 200 optical imager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data language 8.1. We show that cluster analysis allows us to obtain good agreement between the clustered and the corresponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an automatic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data.

journal_name

J Biomed Opt

authors

Spinelli AE,Boschi F

doi

10.1117/1.3663442

subject

Has Abstract

pub_date

2011-12-01 00:00:00

pages

120506

issue

12

eissn

1083-3668

issn

1560-2281

journal_volume

16

pub_type

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