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Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data

Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education....

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Autores principales: Li, Feiyan, Tai, Yonghang, Li, Qiong, Peng, Jun, Huang, Xiaoqiao, Chen, Zaiqing, Shi, Junsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614959/
https://www.ncbi.nlm.nih.gov/pubmed/31341513
http://dx.doi.org/10.1155/2019/9756842
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author Li, Feiyan
Tai, Yonghang
Li, Qiong
Peng, Jun
Huang, Xiaoqiao
Chen, Zaiqing
Shi, Junsheng
author_facet Li, Feiyan
Tai, Yonghang
Li, Qiong
Peng, Jun
Huang, Xiaoqiao
Chen, Zaiqing
Shi, Junsheng
author_sort Li, Feiyan
collection PubMed
description Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared to the original intraoperative data, there has always been an argument that these data are represented by lower fidelity in virtual surgical training. In this paper, a dynamic biomechanics experimental framework is designed to achieve a highly immersive haptic sensation during the biopsy therapy with human respiratory motion; it is the first time to introduce the idea of periodic extension idea into the dynamic percutaneous force modeling. Clinical evaluation is conducted and performed in the Yunnan First People's Hospital, which not only demonstrated a higher fitting degree (AVG: 99.36%) with the intraoperation data than previous algorithms (AVG: 87.83%, 72.07%, and 66.70%) but also shows a universal fitting range with multilayer tissue. 27 urologists comprising 18 novices and 9 professors were invited to the VR-based training evaluation based on the proposed haptic rendering solution. Subjective and objective results demonstrated higher performance than the existing benchmark training simulator. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled medical simulation systems would be able to provide a more immersive and effective training environment.
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spelling pubmed-66149592019-07-24 Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data Li, Feiyan Tai, Yonghang Li, Qiong Peng, Jun Huang, Xiaoqiao Chen, Zaiqing Shi, Junsheng Appl Bionics Biomech Research Article Realistic tool-tissue interactive modeling has been recognized as an essential requirement in the training of virtual surgery. A virtual basic surgical training framework integrated with real-time force rendering has been recognized as one of the most immersive implementations in medical education. Yet, compared to the original intraoperative data, there has always been an argument that these data are represented by lower fidelity in virtual surgical training. In this paper, a dynamic biomechanics experimental framework is designed to achieve a highly immersive haptic sensation during the biopsy therapy with human respiratory motion; it is the first time to introduce the idea of periodic extension idea into the dynamic percutaneous force modeling. Clinical evaluation is conducted and performed in the Yunnan First People's Hospital, which not only demonstrated a higher fitting degree (AVG: 99.36%) with the intraoperation data than previous algorithms (AVG: 87.83%, 72.07%, and 66.70%) but also shows a universal fitting range with multilayer tissue. 27 urologists comprising 18 novices and 9 professors were invited to the VR-based training evaluation based on the proposed haptic rendering solution. Subjective and objective results demonstrated higher performance than the existing benchmark training simulator. Combining these in a systematic approach, tuned with specific fidelity requirements, haptically enabled medical simulation systems would be able to provide a more immersive and effective training environment. Hindawi 2019-06-25 /pmc/articles/PMC6614959/ /pubmed/31341513 http://dx.doi.org/10.1155/2019/9756842 Text en Copyright © 2019 Feiyan Li et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Feiyan
Tai, Yonghang
Li, Qiong
Peng, Jun
Huang, Xiaoqiao
Chen, Zaiqing
Shi, Junsheng
Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title_full Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title_fullStr Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title_full_unstemmed Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title_short Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data
title_sort real-time needle force modeling for vr-based renal biopsy training with respiratory motion using direct clinical data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614959/
https://www.ncbi.nlm.nih.gov/pubmed/31341513
http://dx.doi.org/10.1155/2019/9756842
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