A fast and stable vascular deformation scheme for interventional surgery training system
© Ye et al. 2016
Received: 19 August 2015
Accepted: 21 March 2016
Published: 6 April 2016
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.
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.
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.
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.
KeywordsVascular interventional surgery Surgery training Position-based dynamic Volume conservation Spatial acceleration
There are already several academic or commercial vascular interventional assistant surgery robotic prototypes which have been promoting the development of the minimally invasive surgery. Guo and Guo proposed a novel master–slave based robotic catheter operating system with visual and force feedback , but a corresponding surgery training system is urgent to be studied. Wang et al. also designed a master–slave based vascular interventional surgery with an image navigation system, and validated their robotic system with transparent glass vascular model and animal experiments [5, 6]. Meng et al. addressed several key technologies of image guided system, like distortion correction, catheter localization and clinical simulation . Since a vascular surgery training system can be used for training and assisting a surgeon to perform interventional surgery and so on, lots of researchers have shown their great interests in developing surgery training system. Zhang et al. used a mass-spring model to simulate vascular deformation , whose spring coefficient is driven from a reference model, and used more accurate finite element method to validate the simulation results. However, the collision detection and its response performance are not given. Wu et al. used flexible elastic rods to model the guidewire in interventional radiology with different resolutions, which are adaptive to the curvature of the vessel dynamically , moreover, they also proposed a force correction strategy to decrease the overhead of collision detection and thus improve the computation efficiency significantly . Lenoir et al. used an incremental linear finite element model to approximate the geometric non-linearity of large bending deformation of the catheter. In addition, a substructure analysis is used for improving the computational efficiency . Tang et al. presented a real-time physically based model to simulate catheter insertions, and non-linear elastic cosserat rods is used to simulate the long, slender body of the catheter shaft .
In the field of mechanics, many feasible modeling approaches are available for deformable simulation and mainly range from simple and fast mass-spring system (MSS) to complicated but accurate finite element method (FEM). The early MSSs are widely used due to its efficiency and simplicity, and some improvements are well studied to make them more suitable for medical simulation with complicated biomechanics properties in real-time [13–16]. Continuous mechanics based FEMs are much more accurate than MSSs but accompanied with huge computation overhead and not suitable for a real-time interactive surgery training system. Some efficient measures are taken to accelerate the computation, like pre-computation, linear approximation, and GPUs acceleration so as to simulate the deformation with haptic feedback in real-time properly . Physics modeling method used in our surgery training system is a position-based dynamic approach, proposed by Müller et al. [18, 19]. Position-based method is well established and used widely, even in famous physics dynamic libraries like PhysX and Bullet. The main advantages of this approach are its controllability, and the problem of overshooting in force based explicit integration systems can be avoided. All these advanced properties make the approach standout when comparing with above mentioned MSS and FEM in developing a real-time interactive surgery training system. Moreover, position-based dynamic approach is geometry motivated and unconditional stable even with large time step so that this method can guarantee the stability of the surgery training system better than using MSSs which may become unstable when facing large time and the parameters in a mass-spring system are much more arbitrary. General position-based method is often used in computer animation and mainly handles with triangular meshes, while the basic element is tetrahedron in our blood vessel geometric model. Therefore, an extra volume conservation constraint need to be considered in our implementation to guarantee the deformation accuracy.
This paper contributes to a master–slave based vascular interventional surgery training system mainly in three aspects. First, a hybrid blood vessel model was designed to process collision detection and deformation calculation separately to improve the real-time performance of our surgery training system. Second, volume conservation constraint of a tetrahedral element was deduced and then used to solve the problem of tetrahedral elements overlapping which improves the vascular deformation result. Lastly, spatial acceleration algorithm is employed to enhance the real-time performance of our interactive training system.
The remainder of the paper is organized as follows. In "Methods" section, we introduce the hybrid geometric blood vessel modeling approach and the volume conservation constraint of a tetrahedral element, and then a hash table based spatial acceleration algorithm is depicted. Simulation results are given in "Conclusion" section, and finally we discuss our approaches and future work planning systematically.
Vascular deformation modeling scheme
Efficient vascular deformation modeling scheme plays an important role in virtual reality based vascular interventional surgery training system since it determines the visual deformation result and real-time performance directly. In this section, we first discuss a hybrid geometric blood vessel model which processes collision detection and deformation calculation separately, and then introduce a position-based vascular deformation method with volume conservation.
Hybrid geometric blood vessel model
The motion states of a flexible catheter and its contact information with vascular wall are also important in this paper. In our implementation, the catheter is also modeled with tetrahedral mesh and its tip has four degrees of freedom (DOF) just as a real catheter, including three degrees of freedom in rotation (i.e. rotation around x, y and z axis) and one translation along the catheter axis direction. The rest parts are controlled by elastic potential energy produced by the collision response (collision between the catheter and the ghost triangular mesh). In this way, a catheter can move freely in the blood vessel with continuous transmission commands governed by an experienced surgeon.
Position-based vascular deformation with volume conservation
Hash table based spatial acceleration
Calculating vascular deformation partially is the core idea of the spatial acceleration approach. Figure 3 depicts the spatial acceleration approach with a small piece of blood vessel model, in which the wireframe and the filled box represent the inactive and active bounding boxes respectively. In the initialization stage, all tetrahedral elements of a blood vessel model are first classified into several adjacent child bounding boxes whose states are inactive by default and each accompanies with a unique hash value. The hash value is extremely important since it determines which bounding box encloses the catheter tip. As we mentioned above, a catheter can move freely in the blood vessel under the control of a skilled surgeon, thus a hash value of the catheter tip need to be calculated in every time-step. A bounding box will be activated when its hash value matches the hash value of the catheter tip, namely the active bounding box encloses the catheter tip. And further, all tetrahedral elements in the active bounding box are also active and will be used for deformation calculation, which means that an active bounding box is a basic calculation unit in our implementation.
During the simulation, some particles are fixed to prevent the blood vessel from falling under the gravity and moving freely under the pulled force. In our study, the particles which are too close to the top or bottom of the active bounding box are fixed. However, the catheter moves from one bounding box to another dynamically and when the catheter tip just enters a new bounding box with small displacement, the vascular deformation can hardly occur because of the fixed particles. To address this issue and guarantee more smooth transition when the catheter tip moves from one bounding box to another, we expanded the child bounding box a little bigger than the regular one when classifying the tetrahedral elements into a specific child bounding box. As a result, a tetrahedral element may be existed in several adjacent bounding boxes at the same time because of the shared space. The larger the shared space is, the smoother the transition results can be obtained while the more computation overhead is required. The detailed hash table based spatial acceleration approach will be discussed below.
Since a regular child bounding box is designed to be a basic computation unit, we have to determine all the tetrahedral elements that a regular child bounding box contains. Each regular child bounding box is expanded with the size \(span_x=span_y=span_z=d/3\) to guarantee a much more smooth transition during the simulation. The elongation span is an experienced value and it can balance computation efficiency and visual deformation result quite well.
Results and discussion
In the final implementation of our real-time vascular interventional surgery simulator, besides efficient hybrid blood vessel model, position-based deformation with volume conservation and hash table based spatial acceleration were obtained. The final visual rendering results and system snapshot of our simulator are shown in Figs. 7 and 8 respectively. With video information provided by a monitor on the master side, an interventional radiologist operates the catheter along the main axis with the combination of translations and rotations. A series of continuous control instructions are transmitted to computer directly, once receiving the specific instructions, the catheter in the training system starts to follow the catheter in the master side. In addition, we have also invited two experienced surgeons from our partner hospital to perform a face validity of our surgery training simulator and their assessment mainly focus on three aspects, namely visual authenticity, haptic reality and user interaction, in which, the average score of visual authenticity is 0.78 (1.0 in total, the same as below) and they suggested that texture mapping technique can be used in the next version to improve the rendering results of the simulator. Besides, the average score of haptic reality and user interaction are 0.86 and 0.90 respectively which perform well during the evaluation and they thought that the simulator can simulate basic interventional operations well and its vascular deformation results are reliable, which should be helpful in clinic training and surgery scheme planning.
Vascular interventional surgery is rapid developing and will let more and more patients benefit from it. In recent years, vascular interventional robotic technique has been built but still need to be refined. With the development of such a new coming useful technology, efficient surgeon training measures are still a big challenge. In order to design a reusable training platform, many scientists and researchers have paid lots of attention on this subject. In this paper, we have adapted a new hybrid blood vessel model for efficient collision detection and deformation calculation, which makes the movement of a catheter in the blood vessel more realistic. In addition, a spatial adaptive acceleration algorithm is employed to improve the real-time performance and visual deformation result, and the experimental results show that the hybrid blood vessel model and hash table based spatial adaptive acceleration approach are robust and efficient to guarantee the performance of our vascular surgery training system.
In future study, we will focus on the realistic material behavior of the blood vessel, such as non-linear, stress relaxation, anisotropy and so on. By employing the more realistic material properties, the deformation can be modeled much closer to the real blood vessel under the same external force load. Finally, we can use the training system to assist remote vascular interventional surgery.
XF Ye contributed to the hybrid blood vessel model modeling and derive the volume conservation constraint in detail. JG Zhang has implemented the surgery training system and improved the method proposed in this paper. P Li has verified the efficiency and real-time performance of the system. T Wang has improved the visual deformation result and performed the comparison with previous related methods. SX Guo has built a corresponding experimental platform. All authors read and approved the final manuscript.
This research was supported by Natural Science Foundation of Heilongjiang Province of China (Grant No. F201416) and National High-tech R&D Program of China (863 key Program, Grant No. 2015AA0401).
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Davidson IJA, Lok C, Dolmatch B, Gallieni M, Nolen B, Pittiruti M, et al. Virtual reality: emerging role of simulation training in vascular access. Semin Nephrol. 2012;32:572–81.View ArticleGoogle Scholar
- Guo J, Guo S, Xiao N, Ma X, Yoshida S, Tamiya T, et al. A novel robotic catheter system with force and visual feedback for vascular interventional surgery. Int J Mechatron Autom. 2012;2:1–15.View ArticleGoogle Scholar
- Lyu SR, Lin YK, Huang ST, Yau HT. Experience-based virtual training system for knee arthroscopic inspection. Biomed Eng Online. 2013;12:63.View ArticleGoogle Scholar
- Ye X, Zhang J, Gu Y. An improved collision detection and cutting algorithm of the soft tissue in virtual surgical simulation. Int J Mechatron Autom. 2014;4:236–47.View ArticleGoogle Scholar
- Wang T, Zhang D, Da L. Remote-controlled vascular interventional surgery robot. Int J Med Robot Comput Assist Surg. 2010;6:194–201.Google Scholar
- Lu WS, Xu WY, Zhang J, Liu D, Wang DM, Jia P, et al. Application study of medical robots in vascular intervention. Int J Med Robot Comput Assist Surg. 2011;7:361–6.Google Scholar
- Meng C, Zhang J, Liu D, Liu B, Zhou F. A remote-controlled vascular interventional robot: System structure and image guidance. Int J Med Robot Comput Assist Surg. 2013;9:230–9.View ArticleGoogle Scholar
- Zhang D, Wang T, Liu D, Lin G. Vascular deformation for vascular interventional surgery simulation. Int J Med Robot Comput Assist Surg. 2010;6:170–7.View ArticleGoogle Scholar
- Wang H, Wu J, Wei M, Ma X. A robust and fast approach to simulating the behavior of guidewire in vascular interventional radiology. Comput Med Imaging Graph. 2015;40:160–9.View ArticleGoogle Scholar
- Wu J, Wang H, Zhang P, Ma X, Hu Q. A preliminary real-time and realistic simulation environment for percutaneous coronary intervention. Biomed Res Int. 2015;2015:1–10.Google Scholar
- Lenoir J, Cotin S, Duriez C, Neumann P. Interactive physically-based simulation of catheter and guidewire. Comput Graph. 2006;30:416–22.View ArticleGoogle Scholar
- Tang W, Wan TR, Gould DA, How T, John NW. A stable and real-time nonlinear elastic approach to simulating guidewire and catheter insertions based on cosserat rod. IEEE Transact Biomed Eng. 2012;59:2211–8.View ArticleGoogle Scholar
- Basafa E, Farahmand F. Real-time simulation of the nonlinear visco-elastic deformations of soft tissues. Int J Comput Assist Radiol Surg. 2011;6:297–307.View ArticleGoogle Scholar
- Gao W, Chu L, Fu Y, Wang S. A non-linear, anisotropic mass spring model based simulation for soft tissue deformation. In: 11th international conference on ubiquitous robots and ambient intelligence (URAI); 2014. p. 7–10.Google Scholar
- Patete P, Iacono MI, Spadea MF, Trecate G, Vergnaghi D, Mainardi LT, et al. A multi-tissue mass-spring model for computer assisted breast surgery. Med Eng Phys. 2013;35:47–53.View ArticleGoogle Scholar
- Ye X, Zhu L, Guo S. Deformation model of soft tissues for real-time surgical simulation. Inform Int Interdiscip J. 2010;13:2011–20.Google Scholar
- Dick C, Georgii J, Westermann R. A real-time multigrid finite hexahedra method for elasticity simulation using CUDA. Simul Model Pract Theor. 2011;19:801–16.View ArticleGoogle Scholar
- Müller M, Heidelberger B, Hennix M, Ratcliff J. Position based dynamics. J Vis Commun Image Represent. 2007;18:109–18.View ArticleGoogle Scholar
- Bender J, Müller M, Otaduy MA, Teschner M, Macklin M. A survey on position-based simulation methods in computer graphics. Comput Graph Forum. 2014;33:228–51.View ArticleGoogle Scholar
- Souza MS, Wangenheim A, Comunello E. Fast simulation of cloth tearing. SBC J Interact Syst. 2014;5:44–8.Google Scholar
- Teschner M, Hiedelberger B, Müller M, Pomeranets D, Gross M. Optimized spatial hashing for collision detection of deformable objects. Vision Model Vis. 2003;3:47–54.Google Scholar
- Si H. TetGen, a Delaunay-based quality tetrahedral mesh generator. ACM Transact Math Soft (TOMS). 2015;41:1–36.MathSciNetView ArticleGoogle Scholar
- Wang T, Zhao F, Gao W, Ye X, Yu D, Gao Y. A physical model identification method of soft tissue deformation for virtual surgery. In: 2014 IEEE international conference on mechatronics and automation (ICMA); 2014. p. 298–302.Google Scholar