分布式脑深部刺激术前规划和医疗训练的技术框架

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Qi Zhang , Roy Eagleson , Sandrine de Ribaupierre
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引用次数: 0

摘要

脑深部刺激(DBS)是一种突破性的运动障碍治疗方法,需要精确的计划和广泛的训练,以确保准确的电极放置在大脑的关键区域,如丘脑核。本文介绍了一种创新的DBS技术框架,以支持分布式、实时的术前规划和医疗培训。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,以帮助临床医生准确识别基本解剖结构并完善术前计划。该平台的核心是多体渲染、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,在3D中无缝合并和可视化不同的组织类型,以及它们与手术工具的交互。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述,确保了有效DBS规划所需的精度。通过提供动态、实时的反馈,该框架支持改进决策,并为协同DBS培训和程序准备设定了新的标准。该平台基于网络的同步架构允许神经科医生和外科医生同时与来自任何位置的可视化数据进行交互,从而增强了协作。该功能支持实时反馈,促进协作决策,简化手术计划,从而改善手术结果。跨各种硬件配置和web浏览器的性能评估证明了该平台的高渲染速度和低延迟数据同步,确保了对临床使用至关重要的响应和可靠的交互。它的适应性使其适用于医疗培训、术前规划和术中支持,适应各种硬件设置和网络环境,以满足dbs相关手术的特定需求。本研究为推进分布式临床规划、综合医学教育和改善神经刺激疗法的患者护理奠定了坚实的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A technology framework for distributed preoperative planning and medical training in deep brain stimulation
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.
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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