Approach-specific, multi-GPU, multi-tool, high-realism neurosurgery simulation

描述（由申请人提供）：特定入路、多GPU、多工具、高逼真度神经外科手术模拟神经系统疾病的手术治疗非常复杂，需要高级培训。然而，医院的实习时间表压缩对年轻的外科医生来说是一个挑战，他们需要掌握这种复杂手术所需的技术。手术模拟器是强大的工具，可为复杂手术提供高级培训;加速住院医师的培训，而不会降低发病率和死亡率;并提高患者结局与手术经验明显相关的技能。手术模拟训练已经提出了近十年，计算技术和算法开发的进步现在使其成为一个更现实的训练选择。双盲验证研究表明，在执行复杂手术（如腹腔镜胆囊切除术）时，接受模拟器培训的外科住院医师系统地优于未接受此类培训的住院医师。然而，尽管使用模拟与传统的框架结合的优势，模拟只取得了有限的进展，在外科教育，特别是在神经外科领域。 本提案的总体目标是开发和评估一个交互式神经外科模拟器，临床上是现实的和经过充分验证的。在第一阶段，我们实施了一个原型交互式神经外科模拟器，用于培训医学生切除脑干深部肿瘤。该模拟器包括一个多分辨率，槽可分离的网格算法翼点的方法，一个有效的和物理现实的非线性模型有限元公式，和GPU加速。我们的临床合作者认为我们的第1阶段模拟器在肿瘤切除手术的主要组成部分方面取得了成功。然而，他们还指出，考虑血管系统将受益，因为血管系统的操作对于肿瘤切除和许多其他神经外科手术至关重要。在该第2阶段项目中，我们建议使用模拟血管系统所需的其他组件扩展和改进我们的模拟器，并构建用于动静脉畸形（AVM）手术治疗的模拟器应用程序，AVM是涉及脑血管系统的最复杂手术之一。我们将与北卡罗来纳州大学神经外科系的神经外科医生合作，对神经外科模拟器进行有效性研究。将要开发的交互式模拟器将促进复杂的、需要高级培训的神经外科手术的培训。这将转化为更少的手术室错误，降低患者发病率和改善患者的结果。

DESCRIPTION (provided by applicant): 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.