Approach-specific, Multi-GPU, Multi-tool, High-realism Neurosurgery Simulation Surgical treatment of neurological diseases is complicated and requires advanced training. However, compressed internship schedules in hospitals creates a challenge to young surgeons to master techniques needed for such complex procedures. Surgical simulators are powerful tools to provide advanced training for complex procedures; accelerate the training of residents without a penalty in morbidity and mortality; and improve skills where patient outcome clearly correlates with surgical experience. Surgical simulation training has been proposed for nearly a decade, and advancement in computing technology and algorithm development has now made it a more realistic training option. Double-blind validation studies have shown that surgical residents trained with a simulator systematically outperformed residents without such training in performing complex procedures such as laparoscopic cholecystectomies. However, despite demonstrated advantages of using simulation in conjunction with traditional frameworks, simulation has only made limited inroads in surgical education, particularly in the field of neurosurgery. The overall goal of this proposal is to develop and evaluate an interactive neurosurgery simulator that is clinically realistic and well-validated. During Phase I, we implemented a prototype interactive neurosurgery simulator for training medical students on the resection of deep seated tumors in the brain stem. The simulator consists of a multi-resolution, sulcal- separable meshing algorithm for pterional approach, an efficient and physically realistic non- linear model FEM formulation, and GPU acceleration. Our clinical collaborators deemed our Phase 1 simulator a success for major components of tumor resection procedures. However, they also noted that it would benefit from consideration of vasculature, as manipulation of vasculature is critical to tumor resection and many other neurosurgical procedures. In this Phase 2 project, we propose to extend and refine our simulator with additional components required to simulate vasculature and build a simulator application for surgical treatment of arteriovenous malformations (AVMs), one of the most complex surgeries involving brain vasculature. We will conduct a validity study of the neurosurgery simulator in collaboration with neurosurgeons in the Department of Neurosurgery at the University of North Carolina. The interactive simulator that will be developed will facilitae training in neurosurgical procedures that are complicated and require advanced training. This will translate to fewer operating room errors, reduced patient morbidity and improved patient outcomes.
Approach-specific, Multi-GPU, Multi-tool, High-realism Neurosurgery Simulation Surgical mistakes have catastrophic consequences. Therefore, surgeons use all tools available in their arsenal to avoid these errors. Surgical simulation, one such tool, allows young residents and experts to practice and advance their surgical skills in a risk-free environment. This project aims to develop and validate an interactive simulator for neurosurgical procedures that are complicated and require advanced training. This will translate to fewer operating room errors, reduced patient morbidity and improved patient outcomes.
| Karaki, Wafaa; Rahul; Lopez, Carlos A et al. (2018) A Two-Scale Model of Radio-Frequency Electrosurgical Tissue Ablation. Comput Mech 62:803-814 |
| Demirel, Doga; Yu, Alexander; Baer-Cooper, Seth et al. (2017) Generative Anatomy Modeling Language (GAML). Int J Med Robot 13: |
| Qi, Di; Panneerselvam, Karthikeyan; Ahn, Woojin et al. (2017) Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS). J Biomed Inform 75:48-62 |
| Suvranu De, Rahul (2017) A multi-physics model for ultrasonically activated soft tissue. Comput Methods Appl Mech Eng 314:71-84 |
| Demirel, Doga; Butler, Kathryn L; Halic, Tansel et al. (2016) A hierarchical task analysis of cricothyroidotomy procedure for a virtual airway skills trainer simulator. Am J Surg 212:475-84 |
| Arikatla, Venkata S; Ortiz, Ricardo; Halic, Tansel et al. (2016) An Approach for Automated Scene Management in Real-Time Medical Simulation Framework. Stud Health Technol Inform 220:25-8 |
| Arikatla, Venkata S; Ortiz, Ricardo; De, Suvranu et al. (2016) A Unified Framework for Haptic Interaction in Multimodal Virtual Environments. Stud Health Technol Inform 220:19-24 |
| Arikatla, Venkata S; Ortiz, Ricardo; Thompson, David et al. (2015) A HYBRID APPROACH TO SIMULATE TISSUE BEHAVIOR DURING SURGICAL SIMULATION. Int Conf Comput Math Biomed Eng 2015:332-335 |
| He, Liang; Ortiz, Ricardo; Enquobahrie, Andinet et al. (2015) Interactive Continuous Collision Detection for Topology Changing Models Using Dynamic Clustering. Proc ACM SIGGRAPH Symp Interact 3D Graph Games 2015:47-54 |
| Tang, Min; Tong, Ruofeng; Wang, Zhendong et al. (2014) Fast and Exact Continuous Collision Detection with Bernstein Sign Classification. ACM Trans Graph 33: |
