机构:[1]Yunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China[2]Department of Urology Surgery, Yunnan First People’s Hospital, Kunming, China外科片泌尿外科云南省第一人民医院
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.
基金:
This research is funded by the National Natural Science
Foundation of China (61650401) and the Natural Science
Foundation of Yunnan Province, China (ZD2014004) of
Yunnan Key Laboratory of Opto-electronic Information
Technology, Kunming, China.
第一作者机构:[1]Yunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China
通讯作者:
推荐引用方式(GB/T 7714):
Li Feiyan,Tai Yonghang,Li Qiong,et al.Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data[J].APPLIED BIONICS AND BIOMECHANICS.2019,2019:doi:10.1155/2019/9756842.
APA:
Li, Feiyan,Tai, Yonghang,Li, Qiong,Peng, Jun,Huang, Xiaoqiao...&Shi, Junsheng.(2019).Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data.APPLIED BIONICS AND BIOMECHANICS,2019,
MLA:
Li, Feiyan,et al."Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data".APPLIED BIONICS AND BIOMECHANICS 2019.(2019)