ArticleFrontiers in neuroscience2023
3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Mixed reality assisted target localization for transcranial magnetic stimulation navigation: a feasibility study.Journal of neuroengineering and rehabilitation · 2026Article
- Customized 3D printed helmet in the treatment of metopic craniosynostosis in a 7-month-old infant, a case report.Frontiers in pediatrics · 2025Article
- Clinical implementation, barriers, and unmet needs of rTMS and neuro-navigation systems in stroke rehabilitation: a nationwide survey in South Korea.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuro-navigation is a key technology to ensure the clinical efficacy of TMS. However, the neuro-navigation system based on positioning sensor is currently unable to be promoted and applied in clinical practice due to its time-consuming and high-cost. In the present study, we designed I-Helmet system to promote an individualized and clinically friendly neuro-navigation approach to TMS clinical application. I-Helmet system is based on C++ with a graphical user interface that allows users to design a 3D-printed helmet model for coil navigation. Besides, a dedicated coil positioning accuracy detection method was promoted based on three-dimensional (3D) printing and 3D laser scanning for evaluation. T1 images were collected from 24 subjects, and based on each image, phantom were created to simulate skin and hair. Six 3D-printed helmets with the head positioning hole enlarged by 0-5% tolerance in 1% increments were designed to evaluate the influences of skin, hair, and helmet-tolerance on the positioning accuracy and contact force of I-Helmet. Finally, I-Helmet system was evaluated by comparing its positioning accuracy with three skin hardnesses, three hair styles, three operators, and with or without landmarks. The accuracy of the proposed coil positioning accuracy detection method was about 0.30 mm in position and 0.22° in orientation. Skin and hair had significant influences on positioning accuracy (
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