NDLI: Non-rigid registration of longitudinal brain tumor treatment MRI

Content Provider	IEEE Xplore Digital Library
Author	Chitphakdithai, N. Chiang, V.L. Duncan, J.S.
Copyright Year	2011
Description	Author affiliation: Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520 USA (Chiang, V.L.) \|\| Department of Biomedical Engineering, Yale University, New Haven, CT 06520 USA (Chitphakdithai, N.) \|\| Departments of Electrical Engineering, Biomedical Engineering, and Diagnostic Radiology at Yale University, New Haven, CT 06520 USA (Duncan, J.S.)
Abstract	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.
Starting Page	4893
Ending Page	4896
File Size	1241544
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Biomedical imaging Brain models Gaussian distribution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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