Zhang Qi, Eagleson Roy, Ribaupierre Sandrine de
School of Information Technology, Illinois State University, 100 North University Street, Normal, IL, 61761, United States.
Department of Electrical and Computer Engineering, Western University, London, Ontario, N6A 5B9, Canada.
Comput Med Imaging Graph. 2025 Jul;123:102533. doi: 10.1016/j.compmedimag.2025.102533. Epub 2025 Mar 24.
Deep brain stimulation (DBS) is a groundbreaking therapy for movement disorders, necessitating precise planning and extensive training to ensure accurate electrode placement in critical brain regions, such as the thalamic nuclei. This paper introduces an innovative technology framework for DBS to support distributed, real-time preoperative planning and medical training. The system integrates advanced imaging techniques, interactive graphical representation, and real-time data synchronization to assist clinicians in accurately identifying essential anatomical structures and refining pre-surgical plans. At the platform's core are multi-volume rendering, segmentation, and virtual tool modeling algorithms that employ transparency and refinement controls to seamlessly merge and visualize different tissue types in 3D alongside their interactions with surgical tools. This method enhances visual clarity and provides a highly detailed depiction of crucial structures, ensuring the precision required for effective DBS planning. By delivering dynamic, real-time feedback, the framework supports improved decision-making and sets a new standard for collaborative DBS training and procedural preparation. The platform's web-based synchronization architecture enhances collaboration by allowing neurologists and surgeons to simultaneously interact with visualized data from any location. This functionality supports live feedback, promotes collaborative decision-making, and streamlines procedural planning, leading to improved surgical outcomes. Performance evaluations across various hardware configurations and web browsers demonstrate the platform's high rendering speed and low-latency data synchronization, ensuring responsive and reliable interactions essential for clinical use. Its adaptability makes it suitable for medical training, preoperative planning, and intraoperative support, accommodating a wide range of hardware setups and web environments to address the specific demands of DBS-related procedures. This research lays a robust foundation for advancing distributed clinical planning, comprehensive medical education, and improved patient care in neurostimulation therapies.
深部脑刺激(DBS)是一种用于治疗运动障碍的开创性疗法,需要精确的规划和广泛的培训,以确保将电极准确放置在关键脑区,如丘脑核团。本文介绍了一种用于DBS的创新技术框架,以支持分布式实时术前规划和医学培训。该系统集成了先进的成像技术、交互式图形表示和实时数据同步,以协助临床医生准确识别重要的解剖结构并完善术前计划。该平台的核心是多体积渲染、分割和虚拟工具建模算法,这些算法采用透明度和细化控制,以无缝合并并以3D形式可视化不同组织类型及其与手术工具的相互作用。这种方法提高了视觉清晰度,并提供了关键结构的高度详细描绘,确保了有效DBS规划所需的精度。通过提供动态实时反馈,该框架支持改进决策,并为协作式DBS培训和程序准备设定了新标准。该平台基于网络的同步架构通过允许神经科医生和外科医生从任何位置同时与可视化数据进行交互来增强协作。此功能支持实时反馈,促进协作决策,并简化程序规划,从而改善手术效果。在各种硬件配置和网络浏览器上进行的性能评估表明,该平台具有高渲染速度和低延迟数据同步,确保了临床使用中至关重要的响应式和可靠交互。其适应性使其适用于医学培训、术前规划和术中支持,可适应广泛的硬件设置和网络环境,以满足与DBS相关程序的特定需求。这项研究为推进分布式临床规划、全面医学教育以及改善神经刺激疗法中的患者护理奠定了坚实基础。