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

Image registrationComputer scienceArtificial intelligenceAtlas (anatomy)Computer visionBrain tumorPrincipal component analysisOrbit (dynamics)Pattern recognition (psychology)Brain atlasImage (mathematics)PathologyMedicine

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Publication Info

Year
2008
Type
article
Volume
27
Issue
8
Pages
1003-1017
Citations
106
Access
Closed

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Evangelia I. Zacharaki, Dinggang Shen, Seung‐Koo Lee et al. (2008). ORBIT: A Multiresolution Framework for Deformable Registration of Brain Tumor Images. IEEE Transactions on Medical Imaging , 27 (8) , 1003-1017. https://doi.org/10.1109/tmi.2008.916954

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DOI
10.1109/tmi.2008.916954