Training System Of Virtual Vascular Interventional Surgery Doctors Based On Collision Detection And Weighted Fusion Algorithm

With the continuous development of society,people are facing more and more pressure in life,which leads to cardiovascular diseases that threaten human health for a long time.At present,minimally invasive vascular interventional surgery is one of the mainstream methods for the treatment of such diseases.In order to guide novice doctors to master the surgery,In this paper,a virtual surgical system is designed to provide training for novice doctors.This paper puts forward some solutions to some problems of the system.Firstly,in virtual vascular interventional surgery,the collision detection between medical devices and human vascular tissue is the basis of force feedback calculation.In view of the complexity of virtual surgery and the need for high computing power,the existing collision detection methods can not meet the requirements of accuracy and real-time training.In this paper,a collision detection algorithm of mixed hierarchy bounding box is proposed,which reduces the system response time by two-stage detection.Secondly,in the virtual vascular interventional surgery training system,the force feedback information received by the hand of the trainer is an important index that affects the training effect of the user.Due to the complex structure of blood vessels,it is difficult to perform mechanical analysis after the collision between catheter guide wire and blood vessels,so the force feedback accuracy of the existing training system is poor.Rigorous mechanical analysis is difficult to realize in the whole virtual training system,so this paper adopts a weighted fusion estimation algorithm.Firstly,each component force in the blood vessel is simulated,and then compared with each force in the actual environment.Based on the premise of minimizing the total mean square error,an optimal weight is assigned to each component force.Finally,a feedback force is synthesized and fed back to the trainer’s hand.Finally,the comparative experiment of different collision detection methods and the experiment of improving the accuracy of tactile force feedback are carried out to verify the real-time performance and accuracy of the new collision detection method and the effect of tactile force feedback.Firstly,28 collision experiments were carried out for different collision detection methods.Compared with other detection methods,the hybrid hierarchical bounding box algorithm greatly improved the real-time performance and accuracy.In addition,the algorithm of tactile force feedback was improved.Since it is difficult to measure the real vascular force feedback data in the actual environment,we took the force feedback information in the dynamics simulation software Adams as the reference value,and calculated that the average force value error after using the weighted fusion estimation algorithm was about8.9m N.The average force error without using this algorithm is 15.4m N.In summary,the hybrid hierarchical bounding box collision detection algorithm proposed in this paper can improve the real-time performance and accuracy of the training system.At the same time,the accuracy of haptic force feedback after using the weighted fusion algorithm is also improved,which greatly improves the training effect of the training system.