Ablation Planning in Computer-Assisted Interventions

Project Goals

Tumor ablation is the removal of tumor tissue and is considered as one type of minimally invasive interventions. It can be performed using techniques like cryoablation, high-intensity focused ultrasound (HIFU), and radiofrequency ablation (RFA). These techniques rely on minimally invasive principles to ablate tumor tissues, without having to directly expose the target regions to the environment. It has been widely noted that the success of a tumor ablation procedure hinges greatly on its pre-operative planning, which is often assisted by computational interventions. The proposed ablation planning system in this paper focuses mainly on the radiofrequency ablation (RFA) of hepatic tumors. This project is to develop computational optimization algorithms to plan optimal ablation delivery. Ablation planning systems are necessary to model the 3D interventional environments, identify feasible needle insertion trajectories and deploy ablating electrodes, while avoiding many critical structures.


Genetic Algorithm (GA) was used as it can be designed to consider the multi-objective nature of a tumor ablation planning system. The proposed ablation planning system is designed based on the following objectives: to achieve complete tumor coverage; and to minimize the number of ablations, number of needle trajectories and healthy tissue damage. These objectives are taken into account using an optimization method, Genetic Algorithm (GA). GA is capable of generating many solutions within a defined search space, and these solutions can be selected to undergo evolution based on a quantified value given by a fitness function. An exponential weight-criterion fitness function is used to represent the multiple objectives such as the number of ablation spheres, the number of trajectories, the covariance, and the coverage volume.

Current Results

The proposed mathematical protocol to determine the range of ablation spheres required to achieve complete tumor coverage is feasible to be used as a reference in the context of tumor ablation planning. The following figure shows how tumor coverage changed when trajectory optimization was considered: 0% tumor coverage (top), 100% tumor coverage with [ablation radius]=15 and [number of spheres]=3 (orange spheres) (bottom).


  • Ren, H.; Guo, W.; Ge, S. S. & Lim, W. Coverage Planning in Computer-Assisted Ablation Based On Genetic Optimization Computers in Biology and Medicine, in press, 2014
  • Lim, W. & Ren, H. Cognitive Planning Based on Genetic Algorithm in Computer-Assisted Interventions CIS-RAM 2013, 6th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 6th IEEE International Conference on Robotics, Automation and Mechatronics (RAM), 2013

People Involved

FYP Student: Wan Cheng LIM
Graduate Student: Weian GUO
Advisor: Dr. Hongliang REN


[1] C. Baegert, C. Villard, P. Schreck, L. Soler, and A. Gangi, “Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors,” Computer Aided Surgery, 12(2): pp. 82-90, March, 2007.
[2] Z. Yaniv, P. Cheng, E. Wilson, T. Popa, D. Lindisch, E. Campos-Nanez, H. Abeledo, V. Watson, and F. Banovac, “Needle-Based Interventions With the Image-guided Surgery Toolkit (IGSTK): From Phantoms to Clinical Trials,” IEEE Trans. on Biomedical Engineering, vol. 57, no. 4, April, 2010.
[3] G. D. Dodd, M. C. Soulen, R. A. Kane, T. Livraghi, W. R. Lees, Y. Yamashita, A. R. Gillams, O. I. Karahan, H. Rhim. “Minimally invasive treatment of malignant hepatic tumors: At the threshold of a major breakthrough,” RadioGraphics, vol. 20, no. 1, January-February, 2000.
[4] C. Rieder, T. Kroger, C. Schumann, and H. K. Hahn, “GPU-Based Real-Time Approximation of the Ablation Zone for Radiofrequency Ablation”, IEEE Trans. On Visualization and Computer Graphics, vol. 17, no. 12, pp. 1812-1821, December, 2011.

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