PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

• Published in: IEEE transactions on medical imaging Vol. 33; no. 3; pp. 651 - 667
• Main Authors: Kwon, Dongjin, Niethammer, Marc, Akbari, Hamed, Bilello, Michel, Davatzikos, Christos, Pohl, Kilian M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain - pathology; Brain Neoplasms - pathology; Brain tumor magnetic resonance imaging (MRI); Cavity resonators; deformable registration; Deformation mechanisms; discrete-continuous optimization; Holes; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Image segmentation; Magnetic Resonance Imaging - methods; Pathology; Patients; Registers; Segments; Sociology; Statistics; Tasks; tumor growth model; tumor segmentation; Tumors
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2013.2293478

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.