Comparison of GPU-based and CPU-based Algorithms for Determining the Minimum Distance between a CUSA Scalper and Blood Vessels

In this study, we have designed a GPGPU (General-Purpose Graphics Processing Unit)-based algorithm for determining the minimum distance from the tip of a CUSA (Cavitron Ultrasonic Surgical Aspirator) scalpel to the closest point around three types of blood vessel STLs (STereo-Lithographies). The algorithm consists of the following two functions: First, we use z-buffering (depth buffering) as the classic matured function of the GPU in order to effectively obtain depths corresponding to image pixels. Second, we use multiple cores of the GPU for parallel processing so as to calculate the minimum Euclidean distance from the scalpel tip to the closest z-values of the depths. Therefore, the complexity of the GPU-based algorithm does not depend on the shape complexity (e.g., patch, edge, and vertex numbers) of the blood vessels.

[1]  Sean Quinlan,et al.  Efficient distance computation between non-convex objects , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[2]  Kaoru Watanabe,et al.  Virtual Liver Surgical Simulator by Using Z-Buffer for Object Deformation , 2015, HCI.

[3]  Masanao Koeda,et al.  Haptic AR Dental Simulator Using Z-buffer for Object Deformation , 2014, HCI.

[4]  S. Sathiya Keerthi,et al.  A fast procedure for computing the distance between complex objects in three-dimensional space , 1988, IEEE J. Robotics Autom..

[5]  CalculationMing C. Lin,et al.  A Fast Algorithm for Incremental Distance , 1991 .

[6]  Andinet Enquobahrie,et al.  Neurosurgery simulation using non-linear finite element modeling and haptic interaction , 2012, Medical Imaging.

[7]  Gino van den Bergen Efficient Collision Detection of Complex Deformable Models using AABB Trees , 1997, J. Graphics, GPU, & Game Tools.

[8]  Philippas Tsigas,et al.  On sorting and load balancing on GPUs , 2009, CARN.

[9]  Yen-Wei Chen,et al.  Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms , 2013, IEICE Trans. Inf. Syst..

[10]  Dinesh Manocha,et al.  OBBTree: a hierarchical structure for rapid interference detection , 1996, SIGGRAPH.

[11]  John F. Canny,et al.  Collision Detection for Moving Polyhedra , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Hiroshi Noborio,et al.  Fast interference check method using octree representation , 1988, Adv. Robotics.

[13]  Ming C. Lin,et al.  A fast algorithm for incremental distance calculation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[14]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[15]  Sébastien Ourselin,et al.  High-Speed Nonlinear Finite Element Analysis for Surgical Simulation Using Graphics Processing Units , 2008, IEEE Transactions on Medical Imaging.

[16]  Koji Yasuda,et al.  Accelerating Density Functional Calculations with Graphics Processing Unit. , 2008, Journal of chemical theory and computation.

[17]  Mathew G. Pelletier,et al.  Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash , 2008, Sensors.

[18]  David A. Bader,et al.  GPU merge path: a GPU merging algorithm , 2012, ICS '12.