A novel prototype for virtual-reality-based deep brain stimulation trajectory planning using voodoo doll annotation and eye-tracking

ABSTRACT

Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s disease. The procedure requires precise placement of a stimulation electrode into the therapeutic target while avoiding vital anatomies (e.g. blood vessels) to prevent surgical risks. Therefore, multi-contrast imaging data are often employed to capture full anatomical details for electrode trajectory planning. However, with multiple constraints to consider from several image contrasts, surgical planning with conventional 2D-display-based neuro-navigation software can be time-consuming and challenging. Virtual reality (VR) allows intuitive interaction with 3D data, and thus is an excellent fit to navigate complex anatomy for neurosurgical planning. We present the first VR-based DBS trajectory planning system, where we used a novel voodoo doll interaction strategy to allow precise surgical target selection and a line-of-sight approach with eye-tracking to determine optimal DBS trajectories. With preliminary user studies, the proposed system demonstrates great promises for efficient and intuitive DBS planning.

KEYWORDS:

• Virtual reality
• deep brain stimulation
• image-guided surgery
• eye tracking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Owen Hellum

Owen Hellum is an undergraduate student at Concordia University (Montreal, Canada), pursuing a BCompSc degree in the Computer Applications - Computation Arts program. During the summer of 2021, he conducted his Concordia Undergraduate Student Research Award (CURSA) project at the Health-X Lab (Concordia University) under Dr. Yiming Xiao on the subject of surgical virtual reality. His research interests are interactive software systems, human-computer interaction, and interactive design.

Yanyu Mu

Yanyu Mu obtained her undergraduate degree in Electrical Engineering from Western University (London, Canada) in 2018. Afterwards, she pursued her MSc study in Biomedical Engineering at the Robarts Research Institute, Western University with Drs. Terry Peters and Roy Eagleson, and obtained her degree in 2020. For her MSc thesis, she developed augmented reality training systems for minimally invasive surgeries.

Marta Kersten-Oertel

Marta Kersten-Oertelreceived the BSc (Honours) degree in Computer Science and the BA degree in Art History from Queen’s University (Kingston, Canada) in 2002. In 2005, she completed her MSc in Computer Science at Queen’s University. After working as a research assistant at the GRaphisch-Interaktive Systeme at the University of Tübingen(Germany), in 2015 she received the PhD degree in Biomedical Engineering at McGill University (Montreal, Canada). She is an Associate Professor in Computer Science and Software Engineering, PERFORM Centre researcher and Concordia University Research Chair in Applied Perception. Her research is focused on developing and evaluating new visualization techniques, and display and interaction methods specifically for health and clinical applications.

Yiming Xiao

Yiming Xiao is an Assistant Professor at the Department of Computer Science and Software Engineering of Concordia University (Montreal, Canada) and an associate member at the PERFORM Centre (Montreal, Canada).  His research combines novel techniques in medical imaging principles, computer vision, and machine learning to improve the efficiency and accuracy of image-based diagnosis and medical procedures for the brain and body. Previously, Yiming completed his B.Eng degree in Electrical and Computer Engineering in 2009 at McGill University, and then obtained his Master's and PhD degrees in Biomedical Engineering from McGill University. Between 2018 and 2020, Yiming was a BrainsCAN and CIHR postdoctoral fellow at the Robarts Research Institute, Western University.