{"title":"A technology framework for distributed preoperative planning and medical training in deep brain stimulation","authors":"Qi Zhang , Roy Eagleson , Sandrine de Ribaupierre","doi":"10.1016/j.compmedimag.2025.102533","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"123 ","pages":"Article 102533"},"PeriodicalIF":5.4000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611125000424","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.