Computing average shaped tissue probability templates

• Published in: NeuroImage (Orlando, Fla.) Vol. 45; no. 2; pp. 333 - 341
• Main Authors: Ashburner, John, Friston, Karl J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2009; Elsevier Limited
• Subjects: Algorithms; Brain; Brain - anatomy & histology; Computer Simulation; Data Interpretation, Statistical; Deformation; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Models, Biological; Models, Statistical; Pattern Recognition, Automated - methods; Probability; Reproducibility of Results; Sensitivity and Specificity
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2008.12.008

This note presents a framework for generating tissue probability maps that represent the average shape of a number of subjects' brain images. The procedure is formulated as finding maximum a posteriori estimates within a probabilistic generative model. Estimating the parameters involves alternating between estimating the deformations that match tissue class images of individual subjects to template, and updating the template according to the latest estimates of the deformations. A multinomial matching criterion is used, such that multiple tissue class images (e.g. grey and white matter) are registered simultaneously with the current template estimate. In order to generalise the resulting template to a broader range of subjects, a template blurriness prior is included within the model.