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

描述(由申请人提供)：特定方法，多图形处理器，多工具，高真实感神经外科模拟神经外科疾病的外科治疗是复杂的，需要高级培训。然而，医院紧凑的实习日程给年轻外科医生带来了挑战，他们要掌握如此复杂的手术所需的技术。手术模拟器是强大的工具，可以为复杂的程序提供高级培训；加速住院医生的培训，而不会在发病率和死亡率方面受到惩罚；以及在患者结果与手术经验明显相关的情况下提高技能。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使其成为一种更现实的训练选择。双盲验证研究表明，接受模拟器培训的外科住院医师在执行复杂程序(如腹腔镜胆囊切除术)方面系统地优于未接受此类培训的住院医师。然而，尽管将模拟与传统框架结合使用显示了优势，但模拟在外科教育中只取得了有限的进展，特别是在神经外科领域。这项提案的总体目标是开发和评估一个临床上现实的、经过充分验证的交互式神经外科模拟器。在第一阶段，我们实现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层肿瘤。该模拟器由翼面逼近的多分辨率、可分离网格算法、有效的物理逼真的非线性模型有限元格式和GPU加速组成。我们的临床合作者认为我们的第一阶段模拟器在肿瘤切除手术的主要组成部分上取得了成功。然而，他们也指出，这将受益于血管系统的考虑，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。在这个第二阶段的项目中，我们建议扩展和改进我们的模拟器，增加模拟血管系统所需的组件，并建立一个用于动静脉畸形(AVM)手术治疗的模拟器应用程序，这是涉及脑血管系统的最复杂的手术之一。我们将与北卡罗来纳大学神经外科的神经外科医生合作，对神经外科模拟器进行有效性研究。将开发的交互式模拟器将促进神经外科程序的培训，这些程序很复杂，需要高级培训。这将转化为更少的手术室错误，减少患者发病率，并改善患者预后。

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.