title: A fast and stable vascular deformation scheme for interventional surgery training system.
abstract: BACKGROUND
  The emergence and development of robot assistant interventional vascular surgery technologies have benefited many patients with cardiovascular or cerebrovascular diseases. Due to the absence of effective training measures, these new advanced technologies have not been fully utilized and only few experienced surgeons can perform such complicated surgeries so far. In order to solve such problems, virtual reality based vascular interventional surgery training system, a promising way to train young surgeons or assist experienced surgeons to perform surgery, has been widely studied.
  
  
  METHODS
  In this paper, we mainly conduct a thorough study on both reliable deformation and high real-time performance of an interactive surgery training system. An efficient hybrid geometric blood vessel model which handles the collision detection query and vascular deformation calculation separately is employed to enhance the real-time performance of our surgery training system. In addition, a position-based dynamic approach with volume conservation constraint is used to improve the vascular deformation result. Finally, a hash table based spatial adaptive acceleration algorithm which makes the training system much more efficient and reliable is described.
  
  
  RESULTS
  Several necessary experiments are conducted to validate the vascular deformation scheme presented in this paper. From the results we can see that the position-based dynamic modeling method with volume conservation constraint can prevent the vascular deformation from the issue of penetration. In addition, the deformation calculation with spatial acceleration algorithm has enhanced the real-time performance significantly.
  
  
  CONCLUSIONS
  The corresponding experimental results indicate that both the hybrid geometric blood vessel model and the hash table based spatial adaptive acceleration algorithm can enhance the performance of our surgery training system greatly without losing the deformation accuracy.
authors.affiliation: College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China. yexiufen@hrbeu.edu.cn.
authors.forename: Xiufen
authors.initials: X
authors.lastname: Ye
authors.affiliation: College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.
authors.forename: Jianguo
authors.initials: J
authors.lastname: Zhang
authors.affiliation: College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Li
authors.affiliation: College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.
authors.forename: Tian
authors.initials: T
authors.lastname: Wang
authors.affiliation: College of Automation, Harbin Engineering University, No. 145, Nantong street, Harbin, China.|Intelligent Mechanical Systems Engineering, Kagawa University, Hayashi-cho, Takamatsu, 761-0396, Japan.
authors.forename: Shuxiang
authors.initials: S
authors.lastname: Guo
mesh_terms: D001808:Blood Vessels / Q000033:anatomy & histology / Q000502:physiology; D000069550:Machine Learning; D008953:Models, Anatomic; D012371:Robotics; D014656:Vascular Surgical Procedures
keywords: Position-based dynamic; Spatial acceleration; Surgery training; Vascular interventional surgery; Volume conservation