Year: 2023
Type: article
Abstract: PURPOSE: Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations. METHODS: We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI. RESULTS: Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI. CONCLUSION: Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,390
Funders: University of Western Australia, National Institutes of Health, Medical Research Council, Australian Research Council, National Health and Medical Research Council, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Continuous Compensation of Brain Shift during Neurosurgery, G1649372110, Neuroimaging Analysis Center (NAC), G3618943016, G4007236124, G5313365987, G6012682559, Towards better neuronavigation in epilepsy surgery: pre-operative MRI to intra-operative CT registration, Resection-induced brain shift estimation: biomechanics-based approach, Biomechanics Meets Robotics: Methods for Accurate and Fast Needle Targeting, Brain-skull interface: discovering the missing piece of head biomechanics, G8422249695, G8882569155, G9000002381, G9000029088, Boston Children's Hospital/HMS Intellectual and Developmental Disabilities Research Center, Lymph Node Quantification System for Multisite Clinical Trials
OpenAlex ID: W4362506320