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

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收录情况: ◇ SCIE ◇ EI

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
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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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出版当年[2019]版:
大类 | 4 区 工程技术
小类 | 4 区 工程:生物医学 4 区 机器人学
最新[2025]版:
大类 | 4 区 计算机科学
小类 | 4 区 工程:生物医学 4 区 机器人学
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出版当年[2018]版:
Q3 ENGINEERING, BIOMEDICAL Q3 ROBOTICS
最新[2023]版:
Q3 ENGINEERING, BIOMEDICAL Q3 ROBOTICS

影响因子: 最新[2023版] 最新五年平均 出版当年[2018版] 出版当年五年平均 出版前一年[2017版] 出版后一年[2019版]

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第一作者机构: [1]Yunnan Key Laboratory of Opto-electronic Information Technology, Yunnan Normal University, Kunming, China
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