Abstract
Introduction
Post-mortem computed tomography guided placement of co-axial introducer needles allows for the extraction of tissue and liquid samples for histological and toxicological analyses. Automation of this process can increase the accuracy and speed of the needle placement, thereby making it more feasible for routine examinations. To speed up the planning process and increase safety, we developed an algorithm that calculates an optimal entry point and end-effector orientation for a given target point, while taking constraints such as accessibility or bone collisions into account.
Technique
The algorithm identifies the best entry point for needle trajectories in three steps. First, the source CT data is prepared and bone as well as surface data are extracted and optimized. All vertices of the generated surface polygon are considered to be potential entry points. Second, all surface points are tested for validity within the defined hard constraints (reachability, bone collision as well as collision with other needles) and removed if invalid. All remaining vertices are reachable entry points and are rated with respect to needle insertion angle. Third, the vertex with the highest rating is selected as the final entry point, and the best end-effector rotation is calculated to avoid collisions with the body and already set needles.
Discussion
In most cases, the algorithm is sufficiently fast with approximately 5–6 s per entry point. This is the case if there is no collision between the end-effector and the body. If the end-effector has to be rotated to avoid collision, calculation times can increase up to 24 s due to the inefficient collision detection used here. In conclusion, the algorithm allows for fast and facilitated trajectory planning in forensic imaging.
References
Aghayev E, Thali MJ, Sonnenschein M, Jackowski C, Dirnhofer R, Vock P. Post-mortem tissue sampling using computed tomography guidance. Forensic Sci Int. 2007;166:199–203.
Hyodoh H, Shimizu J, Mizuo K, Okazaki S, Watanabe S, Inoue H. CT-guided percutaneous needle placement in forensic medicine. Leg Med. 2015;17:79–81.
Martinez RM, Ptacek W, Schweitzer W, Kronreif G, Fürst M, Thali MJ, et al. CT-guided, minimally invasive, postmortem needle biopsy using the B-Rob II needle-positioning robot. J Forensic Sci. 2014;59:517–21.
Ebert LC, Ptacek W, Naether S, Fürst M, Ross S, Buck U, et al. Virtobot—a multi-functional robotic system for 3D surface scanning and automatic post mortem biopsy. Int J Med Robot Comput Assist Surg. 2010;6:18–27.
Ebert LC, Ptacek W, Breitbeck R, Fürst M, Kronreif G, Martinez RM, et al. Virtobot : the future of automated surface documentation and CT-guided needle placement in forensic medicine. Forensic Sci Med Pathol. 2014;10:179–86.
Essert C, Haegelen C, Lalys F, Abadie A, Jannin P. Automatic computation of electrode trajectories for deep brain stimulation: a hybrid symbolic and numerical approach. Int J Comput Assist Radiol Surg. 2011;7:517–32.
Seitel A, Engel M, Sommer CM, Radeleff BA, Essert-Villard C, Baegert C, et al. Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys. 2011;38:3246–59.
Shamir RR, Joskowicz L, Antiga L, Foroni RI, Shoshan Y. Trajectory planning method for reduced patient risk in image-guided neurosurgery: concept and preliminary results. 2010. p. 76250I–76250I–8. 10.1117/12.843991. Accessed 6 Jan 2016.
Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000;2:315–37.
Shah S, Kapoor A, Ding J, Guion P, Petrisor D, Karanian J, et al. Robotically assisted needle driver: evaluation of safety release, force profiles, and needle spin in a swine abdominal model. Int J Comput Assist Radiol Surg. 2008;3:173–9.
Perry TS. Profile: veebot [Resources_Start-ups]. IEEE Spectr. 2013;50:23.
Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell. 1994;16:641–7.
Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface construction algorithm. In: Proceedings of the 14th annual conference on computer graphics and interactive techniques. New York, NY: ACM; 1987. p. 163–169. 10.1145/37401.37422. Accessed 30 April 2015.
Gottschalk S, Lin MC, Manocha D. OBBTree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd annual conference on computer graphics and interactive techniques. New York, NY: ACM; 1996. p. 171–180. 10.1145/237170.237244. Accessed 30 April 2015.
Staeheli SN, Gascho D, Fornaro J, Laberke P, Ebert LC, Martinez RM, et al. Development of CT-guided biopsy sampling for time-dependent postmortem redistribution investigations in blood and alternative matrices—proof of concept and application on two cases. Anal Bioanal Chem. 2016;4:1249–58.
Banik S, Rangayyan RM, Boag GS. Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images. J Digit Imaging. 2010;23:301–22.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ebert, L.C., Fürst, M., Ptacek, W. et al. Automatic entry point planning for robotic post-mortem CT-based needle placement. Forensic Sci Med Pathol 12, 336–342 (2016). https://doi.org/10.1007/s12024-016-9798-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12024-016-9798-5