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3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442160/ https://www.ncbi.nlm.nih.gov/pubmed/37609452 http://dx.doi.org/10.3389/fnins.2023.1224800 |
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author | Wang, He Cui, Dong Jin, Jingna Wang, Xin Li, Ying Liu, Zhipeng Yin, Tao |
author_facet | Wang, He Cui, Dong Jin, Jingna Wang, Xin Li, Ying Liu, Zhipeng Yin, Tao |
author_sort | Wang, He |
collection | PubMed |
description | 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 (p < 0.0001), whereas different skin hardnesses, hair styles, and operators did not (p > 0.05). The tolerance of the helmet presented significant influences on positioning accuracy (p < 0.0001) and contact force (p < 0.0001). The positioning accuracy significantly increased (p < 0.0001) with landmark guided I-Helmet. 3D-printed helmet-type Neuro-navigation approach (I-Helmet) with 3% tolerance and landmarks met the positioning requirements for TMS in clinical practice with less than 5 N mean contact force, 3–5 mm positioning accuracy, 65.7 s mean operation time, and 50-yuan material cost. All the results suggest that the cost of I-Helmet system may be much less than the that of training clinical doctors to position the coil of TMS operation during short period of time. |
format | Online Article Text |
id | pubmed-10442160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104421602023-08-22 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation Wang, He Cui, Dong Jin, Jingna Wang, Xin Li, Ying Liu, Zhipeng Yin, Tao Front Neurosci Neuroscience 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 (p < 0.0001), whereas different skin hardnesses, hair styles, and operators did not (p > 0.05). The tolerance of the helmet presented significant influences on positioning accuracy (p < 0.0001) and contact force (p < 0.0001). The positioning accuracy significantly increased (p < 0.0001) with landmark guided I-Helmet. 3D-printed helmet-type Neuro-navigation approach (I-Helmet) with 3% tolerance and landmarks met the positioning requirements for TMS in clinical practice with less than 5 N mean contact force, 3–5 mm positioning accuracy, 65.7 s mean operation time, and 50-yuan material cost. All the results suggest that the cost of I-Helmet system may be much less than the that of training clinical doctors to position the coil of TMS operation during short period of time. Frontiers Media S.A. 2023-08-07 /pmc/articles/PMC10442160/ /pubmed/37609452 http://dx.doi.org/10.3389/fnins.2023.1224800 Text en Copyright © 2023 Wang, Cui, Jin, Wang, Li, Liu and Yin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wang, He Cui, Dong Jin, Jingna Wang, Xin Li, Ying Liu, Zhipeng Yin, Tao 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title | 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title_full | 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title_fullStr | 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title_full_unstemmed | 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title_short | 3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation |
title_sort | 3d-printed helmet-type neuro-navigation approach (i-helmet) for transcranial magnetic stimulation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442160/ https://www.ncbi.nlm.nih.gov/pubmed/37609452 http://dx.doi.org/10.3389/fnins.2023.1224800 |
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