Title :
Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI]
Author :
Kyriacou, Stelios K. ; Davatzikos, Christos ; Zinreich, S. James ; Bryan, R. Nick
Author_Institution :
Dept. of Radiol., Johns Hopkins Univ. Sch. of Med., Baltimore, MD, USA
fDate :
7/1/1999 12:00:00 AM
Abstract :
A biomechanical model of the brain is presented, using a finite-element formulation. Emphasis is given to the modeling of the soft-tissue deformations induced by the growth of tumors and its application to the registration of anatomical atlases, with images from patients presenting such pathologies. First, an estimate of the anatomy prior to the tumor growth is obtained through a simulated biomechanical contraction of the tumor region. Then a normal-to-normal atlas registration to this estimated pre-tumor anatomy is applied. Finally, the deformation from the tumor-growth model is applied to the resultant registered atlas, producing an atlas that has been deformed to fully register to the patient images. The process of tumor growth is simulated in a nonlinear optimization framework, which is driven by anatomical features such as boundaries of brain structures. The deformation of the surrounding tissue is estimated using a nonlinear elastic model of soft tissue under the boundary conditions imposed by the skull, ventricles, and the falx and tentorium. A preliminary two-dimensional (2-D) implementation is presented in this paper, and tested on both simulated and patient data. One of the long-term goals of this work is to use anatomical brain atlases to estimate the locations of important brain structures in the brain and to use these estimates in pre-surgical and radiosurgical planning systems.
Keywords :
biomechanics; biomedical MRI; brain models; finite element analysis; image registration; medical image processing; surgery; tumours; MRI; biomechanical model; brain images; brain structures boundaries; falx; finite-element formulation; magnetic resonance imaging; medical diagnostic imaging; nonlinear elastic model; nonlinear elastic registration; nonlinear optimization framework; skull; tentorium; tumor pathology; ventricles; Anatomy; Biological tissues; Boundary conditions; Brain modeling; Deformable models; Finite element methods; Magnetic resonance imaging; Neoplasms; Pathology; Skull; Biomechanics; Brain; Brain Neoplasms; Computer Simulation; Disease Progression; Humans; Magnetic Resonance Imaging; Reproducibility of Results; Tomography, X-Ray Computed;
Journal_Title :
Medical Imaging, IEEE Transactions on