title: Coverage planning in computer-assisted ablation based on Genetic Algorithm.
abstract: An ablation planning system plays a pivotal role in tumor ablation procedures, as it provides a dry run to guide the surgeons in a complicated anatomical environment. Over-ablation, over-perforation or under-ablation may result in complications during the treatments. An optimal solution is desired to have complete tumor coverage with minimal invasiveness, including minimal number of ablations and minimal number of perforation trajectories. As the planning of tumor ablation is a multi-objective problem, it is challenging to obtain optimal covering solutions based on clinician׳s experiences. Meanwhile, it is effective for computer-assisted systems to decide a set of optimal plans. This paper proposes a novel approach of integrating a computational optimization algorithm into the 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 over-ablation to the healthy tissue. These objectives are taken into account using a Genetic Algorithm, which is capable of generating feasible solutions within a constrained search space. The candidate ablation plans can be encoded in generations of chromosomes, which subsequently evolve based on a fitness function. In this paper, an exponential weight-criterion fitness function has been designed by incorporating constraint parameters that were reflective of the different objectives. According to the test results, the proposed planner is able to generate the set of optimal solutions for tumor ablation problem, thereby fulfilling the aforementioned multiple objectives.
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore. Electronic address: ren@nus.edu.sg.
authors.forename: Hongliang
authors.initials: H
authors.lastname: Ren
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore; Sino-Germany College of Applied Science, Tongji University, Shanghai, China.
authors.forename: Weian
authors.initials: W
authors.lastname: Guo
authors.affiliation: Department of Electrical & Computer Engineering, National University of Singapore, Singapore.
authors.forename: Shuzhi
authors.initials: S
authors.lastname: Sam Ge
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors.forename: Wancheng
authors.initials: W
authors.lastname: Lim
mesh_terms: D055011:Ablation Techniques / Q000379:methods*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D008954:Models, Biological; D008957:Models, Genetic; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000601:surgery; D013552:Swine; D013813:Therapy, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: Ablation planning system; Genetic Algorithm (GA); Minimally Invasive Surgery; Multi-objective problem; Tumor ablation