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Non-rigid registration of longitudinal brain tumor treatment MRI

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3 Author(s)
Chitphakdithai, N. ; Dept. of Biomed. Eng., Yale Univ., New Haven, CT, USA ; Chiang, V.L. ; Duncan, J.S.

To evaluate changes in brain structure or function, longitudinal images of brain tumor patients must be non-rigidly registered to account for tissue deformation due to tumor growth or treatment. Most standard non-rigid registration methods will fail to align these images due to the changing feature correspondences between treatment time points and the large deformations near the tumor site. Here we present a registration method which jointly estimates a label map for correspondences to account for the substantial changes that may occur during tumor treatment. Under a Bayesian parameter estimation framework, we employ different probability distributions depending on the correspondence labels. We incorporate models for image similarity, an image intensity prior, label map smoothing, and a transformation prior that encourages deformation near the estimated tumor location. Our proposed algorithm increases registration accuracy compared to a traditional voxel-based registration method as shown using both synthetic and real patient images.

Published in:

Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2011-Sept. 3 2011

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