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

Joint Authors

Li, Feiyan
Tai, Yonghang
Li, Qiong
Peng, Jun
Huang, Xiaoqiao
Chen, Zaiqing
Shi, Junsheng

Source

Applied Bionics and Biomechanics

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-06-25

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Biology

Abstract EN

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.

American Psychological Association (APA)

Li, Feiyan& Tai, Yonghang& Li, Qiong& Peng, Jun& Huang, Xiaoqiao& Chen, Zaiqing…[et al.]. 2019. Real-Time Needle Force Modeling for VR-Based Renal Biopsy Training with Respiratory Motion Using Direct Clinical Data. Applied Bionics and Biomechanics،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1114739

Modern Language Association (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 No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1114739

American Medical Association (AMA)

Li, Feiyan& Tai, Yonghang& Li, Qiong& Peng, Jun& Huang, Xiaoqiao& Chen, Zaiqing…[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. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1114739

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1114739