Multi-modal image set registration and atlas formation

• Published in: Medical image analysis 2006-06, Vol.10 (3), p.440-451
• Main Authors: Lorenzen, Peter, Prastawa, Marcel, Davis, Brad, Gerig, Guido, Bullitt, Elizabeth, Joshi, Sarang
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Atlas formation; Brain - anatomy & histology; Computational anatomy; Databases, Factual; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Information theory; Inverse consistent registration; Magnetic Resonance Imaging - methods; Medical image analysis; Multi-modal image set registration; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2005.03.002

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback–Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.