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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...

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Autores principales: Wang, He, Cui, Dong, Jin, Jingna, Wang, Xin, Li, Ying, Liu, Zhipeng, Yin, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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