In vivo quantification of a homogeneous brain deformation model for updating preoperative images during surgery

• Published in: IEEE transactions on biomedical engineering 2000-02, Vol.47 (2), p.266-273
• Main Authors: Miga, M.I., Paulsen, K.D., Hoopes, P.J., Kennedy, F.E., Hartov, A., Roberts, D.W.
• Format: Article
• Language: English
• Subjects: Animals; Biological and medical sciences; Boundary conditions; Brain; Brain - anatomy & histology; Brain - surgery; Brain modeling; Calibration; Computational modeling; Computer Simulation; Deformable models; Deformation; Finite element method; High-resolution imaging; Image Processing, Computer-Assisted - methods; Image recognition; Image resolution; In vivo; Magnetic Resonance Imaging; Mathematical models; Medical sciences; Models, Neurological; Monitoring, Intraoperative; Neurosurgery; Neurosurgical Procedures - methods; Physics; Porous materials; Preoperative Care; Reproducibility of Results; Skull, brain, vascular surgery; Surgery; Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases; Swine; Tomography, X-Ray Computed
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.821778

Clinicians using image-guidance for neurosurgical procedures have recently recognized that intraoperative deformation from surgical loading can compromise the accuracy of patient registration in the operating room. While whole brain intraoperative imaging is conceptually appealing it presents significant practical limitations. Alternatively, a promising approach may be to combine incomplete intraoperatively acquired data with a computational model of brain deformation to update high resolution preoperative images during surgery. The success of such an approach is critically dependent on identifying a valid model of brain deformation physics. Towards this end, the authors evaluate a three-dimensional finite element consolidation theory model for predicting brain deformation in vivo through a series of controlled repeat-experiments. This database is used to construct an interstitial pressure boundary condition calibration curve which is prospectively tested in a fourth validation experiment. The computational model is found to recover 75%-85% of brain motion occurring under loads comparable to clinical conditions. Additionally, the updating of preoperative images using the model calculations is presented and demonstrates that model-updated image-guided neurosurgery may be a viable option for addressing registration errors related to intraoperative tissue motion.