Title
On the modeling, construction, and evaluation of a probabilistic atlas of brain perfusion.
Abstract
To detect subtle, abnormal perfusion patterns in brain single photon emission computer tomography (SPECT) images, it is necessary to develop quantitative methods in which computer-aided statistical analysis takes advantage of information present in databases of normal subjects. The purpose of this study was to evaluate and examine aspects of the creation and the modeling power of three statistical models for representing brain perfusion as observed in ECD-SPECT. The first model is a local model of voxel-by-voxel mean and variance. The second model is a PCA-based global model that accounts for covariance patterns in the images. The third model is an original model that is a non-linear extension to the second model. This model is based on robust statistics for modeling abnormalities. To evaluate the models, a leave-one-out procedure combined with simulations of abnormal perfusion patterns was adopted. Abnormal perfusion patterns were simulated at different locations in the brain, with different intensities and different sizes. The procedure yields receiver operator characteristics (ROC) that present a combined measure of model-fit and model-sensitivity at detecting abnormalities. The scheme can further be used to compare models as well as the influence of different preprocessing steps. In particular, the influence of different registration approaches is studied and analyzed. The results show that the original non-linear model always performed better than the other models. Finally, location-dependent detection performance was found. Most notably, a higher variation of perfusion was observed in the right frontal cortex than in the other locations studied.
Year
DOI
Venue
2005
10.1016/j.neuroimage.2004.10.019
NeuroImage
Keywords
DocType
Volume
Brain perfusion atlas,SPECT,Probabilistic PCA,Robust estimation,Evaluation
Journal
24
Issue
ISSN
Citations 
4
1053-8119
4
PageRank 
References 
Authors
0.47
9
4
Name
Order
Citations
PageRank
Torbjørn Vik191.35
Fabrice Heitz240159.55
Izzie Namer36811.51
Jean-Paul Armspach422126.60