title: Non-rigid registration of longitudinal brain tumor treatment MRI.
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.
authors.affiliation: Department of Biomedical Engineering,Yale University, New Haven, CT 06520, USA. nicha.chitphakdithai@yale.edu
authors.forename: Nicha
authors.initials: N
authors.lastname: Chitphakdithai
authors.forename: Veronica L
authors.initials: VL
authors.lastname: Chiang
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique