Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth

• Published in: NeuroImage (Orlando, Fla.) Vol. 46; no. 3; pp. 762 - 774
• Main Authors: Zacharaki, Evangelia I., Hogea, Cosmina S., Shen, Dinggang, Biros, George, Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2009; Elsevier Limited
• Subjects: Algorithms; APPSPACK; Artificial Intelligence; Atlas-based segmentation; Brain; Brain cancer; Brain Neoplasms - pathology; Brain tumor; Computer Simulation; Deformable registration; Deformation; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Methods; Models, Biological; Patients; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Studies; Subtraction Technique; Tumor mass effect; Tumor simulation; Tumors; Brain tumor; Atlas-based segmentation; APPSPACK; Deformable registration; Tumor simulation; Tumor mass effect
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2009.01.051

Although a variety of diffeomorphic deformable registration methods exist in the literature, application of these methods in the presence of space-occupying lesions is not straightforward. The motivation of this work is spatial normalization of MR images from patients with brain tumors in a common stereotaxic space, aiming to pool data from different patients into a common space in order to perform group analyses. Additionally, transfer of structural and functional information from neuroanatomical brain atlases into the individual patient's space can be achieved via the inverse mapping, for the purpose of segmenting brains and facilitating surgical or radiotherapy treatment planning. A method that estimates the brain tissue loss and replacement by tumor is applied for achieving equivalent image content between an atlas and a patient's scan, based on a biomechanical model of tumor growth. Automated estimation of the parameters modeling brain tissue loss and displacement is performed via optimization of an objective function reflecting feature-based similarity and elastic stretching energy, which is optimized in parallel via APPSPACK (Asynchronous Parallel Pattern Search). The results of the method, applied to 21 brain tumor patients, indicate that the registration accuracy is relatively high in areas around the tumor, as well as in the healthy portion of the brain. Also, the calculated deformation in the vicinity of the tumor is shown to correlate highly with expert-defined visual scores indicating the tumor mass effect, thereby potentially leading to an objective approach to quantification of mass effect, which is commonly used in diagnosis.