A technology framework for distributed preoperative planning and medical training in deep brain stimulation

Area of Science:

• Neurosurgery
• Medical Technology
• Computational Imaging

Background:

• Deep brain stimulation (DBS) requires precise electrode placement for treating movement disorders.
• Current planning and training methods for DBS can be limited by accessibility and real-time collaboration.

Purpose of the Study:

• To introduce an innovative technology framework for distributed, real-time preoperative planning and medical training in DBS.
• To enhance accuracy in identifying anatomical structures and refining surgical plans for DBS procedures.

Main Methods:

• Integration of advanced imaging, interactive 3D visualization, and real-time data synchronization.
• Utilizing multi-volume rendering, segmentation, and virtual tool modeling with transparency controls.
• Development of a web-based synchronization architecture for remote, simultaneous user interaction.

Main Results:

• The framework provides enhanced visual clarity and detailed depictions of critical brain structures.
• Demonstrated high rendering speed and low-latency data synchronization across various hardware and browsers.
• Enabled seamless collaboration for neurologists and surgeons, facilitating live feedback and improved decision-making.

Conclusions:

• The developed platform sets a new standard for collaborative DBS training and procedural preparation.
• The technology supports improved surgical outcomes through enhanced planning and real-time feedback.
• The framework is adaptable for medical training, preoperative planning, and intraoperative support in neurostimulation therapies.