Registration of Brain Tumor Images Using Hyper-Elastic Regularization
In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean
- PDF / 629,007 Bytes
- 14 Pages / 439.36 x 666.15 pts Page_size
- 100 Downloads / 193 Views
Abstract In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems.
1 Introduction Registration of brain volumes with tumors is important to track the changes between two instances in order to assess the progression of the tumor and the treatment response. The first step is a rigid/affine registration between volumes. Although this is a challenging problem due to the changes caused by the tumor, various approaches on the problem reported successful results in the literature [4, 13, 15]. The total deformation caused by the tumor growth can be taught as the combination of infiltration to the healthy tissue and mass effect components. Our aim in this work is to separate the mass effect and infiltration components, so that malignancy and the reversibility of the destruction can be determined. The healthy brain tissue in one of the images can be warped onto the other ignoring the tumor tissue regions, as the latter may contain uncertainty due to highly complex tumor growth and therapy processes. Hence, matching only the healthy tissues in baseline and follow-up tumor images provides an estimation of the intracranial pressure caused by the tumor growth plus the mass effect.
A. Hamamci • G. Unal () Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey e-mail: [email protected] A. Wittek et al. (eds.), Computational Biomechanics for Medicine: Models, Algorithms and Implementation, DOI 10.1007/978-1-4614-6351-1 10, © Springer Science+Business Media New York 2013
101
102
A. Hamamci and G. Unal
Fig. 1 A sample axial slice from baseline (on the right) and follow-up (on the left) MRI
A similar problem arises in deformable registration of the brain with tumors to a healthy population atlas. The main difference to the intra-subject registration problem is that the deformation also includes inter-subject variations. Hence, a general strategy to solve this problem is to iterate the forward model by simulating the tumor growth on the atlas and refining the parameters of the simulation model by comparing it to the tumor image [6]. This requires strong models, which rely on realistic models of tumor growth and deformations due to the mass effect [2, 6, 8, 14]. The main problem with those approaches is that the growth of the tumor is mostly affected by the uncontrolled parameters such as treatment and requires sophisticated tumor growth models even without treatment. In “Geometric Metamorphosis” paper, Niethammer et al., proposed an interesting approach to the problem using a weak model by separating the foreground, hence the tumor growth, and the background changes [11]. For the problem of intr
Data Loading...
