DocumentCode
1043139
Title
ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images
Author
Zacharaki, Evangelia I. ; Shen, Dinggang ; Lee, Seung-koo ; Davatzikos, Christos
Author_Institution
Dept. of Radiol., Univ. of Pennsylvania, Philadelphia, PA
Volume
27
Issue
8
fYear
2008
Firstpage
1003
Lastpage
1017
Abstract
A deformable registration method is proposed for registering a normal brain atlas with images of brain tumor patients. The registration is facilitated by first simulating the tumor mass effect in the normal atlas in order to create an atlas image that is as similar as possible to the patient´s image. An optimization framework is used to optimize the location of tumor seed as well as other parameters of the tumor growth model, based on the pattern of deformation around the tumor region. In particular, the optimization is implemented in a multiresolution and hierarchical scheme, and it is accelerated by using a principal component analysis (PCA)-based model of tumor growth and mass effect, trained on a computationally more expensive biomechanical model. Validation on simulated and real images shows that the proposed registration framework, referred to as ORBIT (optimization of tumor parameters and registration of brain images with tumors), outperforms other available registration methods particularly for the regions close to the tumor, and it has the potential to assist in constructing statistical atlases from tumor-diseased brain images.
Keywords
biomechanics; biomedical MRI; brain; image registration; optimisation; statistical analysis; tumours; biomechanical model; brain atlas; brain tumor images; deformable registration; multiresolution framework; optimization framework; principal component analysis; statistical atlases; tumor growth model; tumor mass effect; Biomedical imaging; Brain modeling; Deformable models; Diseases; Image analysis; Image resolution; Magnetic resonance imaging; Neoplasms; Predictive models; Radiology; Atlas registration; atlas registration; brain tumor; deformable registration; image attributes; tumor growth model; Algorithms; Artificial Intelligence; Brain Neoplasms; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Software; Subtraction Technique;
fLanguage
English
Journal_Title
Medical Imaging, IEEE Transactions on
Publisher
ieee
ISSN
0278-0062
Type
jour
DOI
10.1109/TMI.2008.916954
Filename
4436040
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