Virtual bronchoscopy method based on marching cubes and an efficient collision detection and resolution algorithm

Ciobirca, Catalin; T, Popa; Gruionu, Gabriel; Langø, Thomas; Leira, Håkon Lasse; Pastramă, Ştefan Dan; Gruionu, Lucian Gheorghe

doi:10.1016/j.ctmat.2015.12.002

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Among them, M represents the mass of the mass point, D is the damping coefficient, X is the space coordinate of the mass point, K is the elastic coefficient of the spring, and t is the time constant [ 21 ]. The solution of the system equations can be transformed into the problem of solving differential equations of discrete mass points, and the approximate solutions that meet the accuracy range are calculated [ 22 ].…”

Section: Collision Detection and Force Feedback Algorithm In Virtumentioning

confidence: 99%

Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery

Luo

2021

Journal of Healthcare Engineering

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The development of virtual reality technology is expected to solve traditional surgical training. The lack of methods has brought revolutionary advances in technology. The virtual surgery system based on collision detection and force feedback can enable the operator to have stronger interaction, which is an exploration of the feature of touch in virtual reality technology. Reality is an important indicator of the virtual surgical system. This article improves the realism of the system from the visual and tactile senses and uses the surrounding ball collision detection and force feedback algorithms to build a realistic surgical platform. In the virtual surgery training system, the introduction of force feedback greatly improves the sense of presence during virtual surgery interaction. The operator can feel the softness and hardness of different tissues and organs through the force feedback device. Virtual reality is an interdisciplinary comprehensive technology that has been widely used in military, film, medical, and gaming fields. Virtual reality can simulate the objective world and display it visually, making people feel immersive. Virtual surgery provides surgeons with a recyclable surgical practice platform and can help doctors perform preoperative rehearsals and predict the results of surgery. The design of collision detection and force feedback algorithms is a prerequisite to ensure the immersion and transparency of the virtual surgical training system. This article mainly introduces the collision detection and force feedback algorithm research in virtual surgery, with the intention of providing some ideas and directions for the development of virtual surgery. This paper proposes two collision detection algorithms, space decomposition method and hierarchical bounding box method, and three force feedback algorithms including spring mass point algorithm, Runge–Kutta method, and Euler method to construct virtual surgery collision detection and force feedback. Experiment with the Overall System Architecture. This paper proves through experimental results that the average collision detection time after the application of the improved collision detection and force feedback algorithm in the virtual surgery system is more than 80.7% less than the traditional method, which greatly improves the detection speed.

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Section: Collision Detection and Force Feedback Algorithm In Virtumentioning

confidence: 99%

Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery

Luo

2021

Journal of Healthcare Engineering

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“…In order to constrain the visualization camera to remain always inside the airways, an algorithm for collisions detection and resolution is used. The algorithm is adapted from the one presented in [3], with the main improvement that, in the new one, there is no segmentation of the airways. Only the Hounsfield values for the voxels and the "line" tool iterator are used to detect the collision and to find the position where the camera trajectory intersected the lungs tissue.…”

Section: Virtual Navigation and Collision Detectionmentioning

confidence: 99%