SlicerCBM: automatic framework for biomechanical analysis of the brain

• Published in: International journal for computer assisted radiology and surgery 2023-10, Vol.18 (10), p.1925-1940
• Main Authors: Safdar, Saima, Zwick, Benjamin F., Yu, Yue, Bourantas, George C., Joldes, Grand R., Warfield, Simon K., Hyde, Damon E., Frisken, Sarah, Kapur, Tina, Kikinis, Ron, Golby, Alexandra, Nabavi, Arya, Wittek, Adam, Miller, Karol
• Format: Article
• Language: English
• Subjects: Algorithms; Biomechanical engineering; Biomechanics; Brain; Brain - diagnostic imaging; Brain - pathology; Brain - surgery; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain Neoplasms - surgery; Brain research; Computer Imaging; Computer Science; Craniotomy; Health Informatics; Humans; Imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical research; Medicine; Medicine & Public Health; Neurosurgical Procedures; Open source software; Original; Original Article; Pattern Recognition and Graphics; Public domain; Radiology; Soft tissues; Software packages; Surgery; Tumors; Vision; Workflow
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-023-02881-7

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.