Abstract:
:We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Kwon D,Niethammer M,Akbari H,Bilello M,Davatzikos C,Pohl KMdoi
10.1109/TMI.2013.2293478subject
Has Abstractpub_date
2014-03-01 00:00:00pages
651-67issue
3eissn
0278-0062issn
1558-254Xjournal_volume
33pub_type
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