Advertisement

Development of a Multi-objective Optimized Planning Method for Microwave Liver Tumor Ablation

  • Libin Liang
  • Derek Cool
  • Nirmal Kakani
  • Guangzhi WangEmail author
  • Hui Ding
  • Aaron Fenster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

Microwave ablation (MWA) is an effective minimal invasive therapy of hepatic cancer. Preoperative treatment planning is key to successful ablation, which aims to find a plan with the minimum number of electrode trajectories, least damage to surrounding tissues, while satisfying multiple clinical constraints. However, this is a multiple objective optimization problem, making it very challenging to find an optimized plan while achieving all the above goals, especially for larger tumors. In this paper, we present a set cover-based method, which can provide Pareto optimal solutions for MWA planning. Evaluation has been performed on 6 tumors with varied sizes selected by interventionalists. Results show that all the generated plans satisfied the clinical constraints and the Pareto optimal solutions are useful to find a suitable trade-off between the number of electrode trajectories and damage to normal tissues.

Keywords

Treatment planning Microwave ablation Set cover Liver cancer Pareto optimization 

Supplementary material

Supplementary material 1 (MP4 12472 kb)

References

  1. 1.
    Simon, S.J., et al.: Microwave ablation: principles and applications. Radiographics 25, 69–83 (2005)CrossRefGoogle Scholar
  2. 2.
    Baegert, C., Villard, C., Schreck, P., Soler, L., Gangi, A.: Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors. Comput. Aided Surg. 12, 82–90 (2007)CrossRefGoogle Scholar
  3. 3.
    Altrogge, I., et al.: Towards optimization of probe placement for radio-frequency ablation. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 486–493. Springer, Heidelberg (2006).  https://doi.org/10.1007/11866565_60CrossRefGoogle Scholar
  4. 4.
    Schumann, C., et al.: Fast automatic path proposal computation for hepatic needle placement. In: Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, pp. 76251J. International Society for Optics and Photonics (2010)Google Scholar
  5. 5.
    Seitel, A., et al.: Computer-assisted trajectory planning for percutaneous needle insertions. Med. Phys. 38, 3246–3259 (2011)CrossRefGoogle Scholar
  6. 6.
    Schumann, C., et al.: Interactive multi-criteria planning for radiofrequency ablation. Int. J. Comput. Assist. Radiol. Surg. 10, 879–889 (2015)CrossRefGoogle Scholar
  7. 7.
    Zhai, W., Xu, J., Zhao, Y., Song, Y., Sheng, L., Jia, P.: Preoperative surgery planning for percutaneous hepatic microwave ablation. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 569–577. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85990-1_68CrossRefGoogle Scholar
  8. 8.
    Liu, F., et al.: A three-dimensional visualisation preoperative treatment planning system in microwave ablation for liver cancer: a preliminary clinical application. Int. J. Hyperth. 29, 671–677 (2013)CrossRefGoogle Scholar
  9. 9.
    Poggi, G., Tosoratti, N., Montagna, B., Picchi, C.: Microwave ablation of hepatocellular carcinoma. World J. Hepatol. 7, 2578 (2015)CrossRefGoogle Scholar
  10. 10.
    Dodd III, G.D., Frank, M.S., Aribandi, M., Chopra, S., Chintapalli, K.N.: Radiofrequency thermal ablation: computer analysis of the size of the thermal injury created by overlapping ablations. Am. J. Roentgenol. 177, 777–782 (2001)CrossRefGoogle Scholar
  11. 11.
    Ren, H., Guo, W., Ge, S.S., Lim, W.: Coverage planning in computer-assisted ablation based on genetic algorithm. Comput. Biol. Med. 49, 36–45 (2014)CrossRefGoogle Scholar
  12. 12.
    Chen, R., Lu, F., Wang, K., Kong, D.: Semiautomatic radiofrequency ablation planning based on constrained clustering process for hepatic tumors. IEEE Trans. Biomed. Eng. 65, 645–657 (2018)Google Scholar
  13. 13.
    Amanatides, J., Woo, A.: A fast voxel traversal algorithm for ray tracing. In: Eurographics, pp. 3–10 (1987)Google Scholar
  14. 14.
    GAMS - The Solver Manuals, GAMS Release 25.1.3 (2018)Google Scholar
  15. 15.
    D-IRCADb (3D Image Reconstruction for Comparison of Algorithms Database). http://www.ircad.fr/research/3dircadb/. Accessed 16 Feb 2019

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Libin Liang
    • 1
    • 2
  • Derek Cool
    • 3
  • Nirmal Kakani
    • 4
  • Guangzhi Wang
    • 1
    Email author
  • Hui Ding
    • 1
  • Aaron Fenster
    • 2
    • 3
  1. 1.Department of Biomedical EngineeringTsinghua UniversityBeijingChina
  2. 2.Robarts Research InstituteWestern UniversityLondonCanada
  3. 3.Department of Medical ImagingWestern UniversityLondonCanada
  4. 4.Department of RadiologyManchester Royal InfirmaryManchesterUK

Personalised recommendations