Interactive multi-criteria planning for radiofrequency ablation

  • Christian Schumann
  • Christian Rieder
  • Sabrina Haase
  • Katrin Teichert
  • Philipp Süss
  • Peter Isfort
  • Philipp Bruners
  • Tobias Preusser
Original Article

Abstract

Purpose

Image-guided radiofrequency ablation (RFA) is a broadly used minimally invasive method for the thermal destruction of focal liver malignancies using needle-shaped instruments. The established planning workflow is based on examination of 2D slices and manual definition of the access path. During that process, multiple criteria for all possible trajectories have to be taken into account. Hence, it demands considerable experience and constitutes a significant mental task.

Methods

An access path determination method based on image processing and numerical optimization is proposed. Fast GPU-based simulation approximation is utilized to incorporate the heat distribution including realistic cooling effects from nearby blood vessels. A user interface for intuitive exploration of the optimization results is introduced.

Results

The proposed methods are integrated into a clinical software assistant. To evaluate the suitability of the interactive optimization approach for the identification of meaningful therapy strategies, a retrospective study has been carried out. The system is able to propose clinically relevant trajectories to the target by incorporating multiple criteria.

Conclusions

A novel method for planning of image-guided radiofrequency ablation by means of interactive access path determination based on optimization is presented. A first retrospective study indicates that the method is suited to improve the classical planning of RFA.

Keywords

Radiofrequency ablation Therapy planning Optimization 

References

  1. 1.
    Altrogge I, Preusser T, Kröger T, Büskens C, Pereira PL, Schmidt D, Peitgen HO (2007) Multiscale optimization of the probe placement for radiofrequency ablation. Acad Radiol 14(11):1310–1324CrossRefPubMedGoogle Scholar
  2. 2.
    Baegert C, Villard C, Schreck P, Soler L (2007) Multi-criteria trajectory planning for hepatic radiofrequency ablation. In: Medical image computing and computer-assisted intervention-MICCAI 2007, Springer, pp 676–684Google Scholar
  3. 3.
    Butz T, Warfield SK, Tuncali K, Silverman SG, van Sonnenberg E, Jolesz FA, Kikinis R (2000) Pre-and intra-operative planning and simulation of percutaneous tumor ablation. In: Medical image computing and computer-assisted intervention-MICCAI 2000, Springer, pp 317–326Google Scholar
  4. 4.
    Chen CC, Miga M, Galloway R (2009) Optimizing electrode placement using finite-element models in radiofrequency ablation treatment planning. IEEE Trans Biomed Eng 56(2):237–245CrossRefPubMedGoogle Scholar
  5. 5.
    Craft D (2013) Multi-criteria optimization methods in radiation therapy planning: a review of technologies and directions. ArXiv Prepr. arXiv:1305.1546
  6. 6.
    Craft DL, Hong TS, Shih HA, Bortfeld TR (2012) Improved planning time and plan quality through multicriteria optimization for intensity-modulated radiotherapy. Int J Radiat Oncol 82(1):e83–e90CrossRefGoogle Scholar
  7. 7.
    Dodd GD III, Frank MS, Aribandi M, Chopra S, Chintapalli KN (2001) Radiofrequency thermal ablation: computer analysis of the size of the thermal injury created by overlapping ablations. Am J Roentgenol 177(4):777–782CrossRefGoogle Scholar
  8. 8.
    Garrean S, Hering J, Saied A, Helton WS, Espat NJ (2008) Radiofrequency ablation of primary and metastatic liver tumors: a critical review of the literature. Am J Surg 195(4):508–520CrossRefPubMedGoogle Scholar
  9. 9.
    Greene N (1986) Environment mapping and other applications of world projections. IEEE Comput graph Appl 6(11):21–29CrossRefGoogle Scholar
  10. 10.
    Klamroth K, Tind J, Wiecek MM (2003) Unbiased approximation in multicriteria optimization. Math Methods Oper Res 56(3):413–437CrossRefGoogle Scholar
  11. 11.
    Kröger T, Altrogge I, Preusser T, Pereira PL, Schmidt D, Weihusen A, Peitgen HO (2006) Numerical simulation of radio frequency ablation with state dependent material parameters in three space dimensions. In: Medical image computing and computer-assisted intervention-MICCAI 2006, Springer, pp 380–388Google Scholar
  12. 12.
    Kröger T, Pätz T, Altrogge I, Schenk A, Lehmann KS, Frericks BB, Ritz JP, Peitgen HO, Preusser T (2010) Fast estimation of the vascular cooling in RFA based on numerical simulation. Open Biomed Eng J 4:16PubMedCentralPubMedGoogle Scholar
  13. 13.
    Küfer KH, Monz M, Scherrer A, Süss P, Alonso F, Sultan ASA, Bortfeld T, Thieke C (2009) Multicriteria optimization in intensity modulated radiotherapy planning. Springer, BerlinGoogle Scholar
  14. 14.
    Laugwitz B, Held T, Schrepp M (2008) Construction and evaluation of a user experience questionnaire. In: Holzinger A (ed) HCI and usability for education and work, no. 5298 in lecture notes in computer science. Springer, Berlin, pp 63–76Google Scholar
  15. 15.
    McCreedy E, Cheng R, Hemler P, Viswanathan A, Wood B, McAuliffe M (2006) Radio frequency ablation registration, segmentation, and fusion tool. IEEE Trans Inf Technol Biomed 10(3):490–496CrossRefPubMedCentralPubMedGoogle Scholar
  16. 16.
    Moltz J, Bornemann L, Kuhnigk JM, Dicken V, Peitgen E, Meier S, Bolte H, Fabel M, Bauknecht HC, Hittinger M, Kiessling A, Pusken M, Peitgen HO (2009) Advanced segmentation techniques for lung nodules, liver metastases, and enlarged lymph nodes in CT scans. IEEE J Sel Topics Signal Process 3(1):122–134CrossRefGoogle Scholar
  17. 17.
    Mulier S, Ni Y, Jamart J, Ruers T, Marchal G, Michel L (2005) Local recurrence after hepatic radiofrequency coagulation: multivariate meta-analysis and review of contributing factors. Ann Surg 242(2):158–171Google Scholar
  18. 18.
    Pascoletti A, Serafini P (1984) Scalarizing vector optimization problems. J Optim Theory Appl 42(4):499–524CrossRefGoogle Scholar
  19. 19.
    Rieder C, Kroeger T, Schumann C, Hahn HK (2011) GPU-based real-time approximation of the ablation zone for radiofrequency ablation. IEEE Trans Vis Comput Graphics 17(12):1812–1821Google Scholar
  20. 20.
    Rieder C, Schwier M, Weihusen A, Zidowitz S, Peitgen HO (2009) Visualization of risk structures for interactive planning of image guided radiofrequency ablation of liver tumors. In: Proceedings SPIE—international society for optics and photonics, pp 726134–726134Google Scholar
  21. 21.
    Schumann C, Bieberstein J, Trumm C, Schmidt D, Bruners P, Niethammer M, Hoffmann RT, Mahnken AH, Pereira PL, Peitgen HO (2010) Fast automatic path proposal computation for hepatic needle placement. In: Wong KH, Miga MI (eds) Proceedings SPIE, pp 76251J–76251J–10Google Scholar
  22. 22.
    Seitel A, Engel M, Sommer CM, Radeleff BA, Essert-Villard C, Baegert C, Fangerau M, Fritzsche KH, Yung K, Meinzer HP et al (2011) Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys 38(6):3246–3259CrossRefPubMedGoogle Scholar
  23. 23.
    Solanki R (1991) Generating the noninferior set in mixed integer biobjective linear programs: an application to a location problem. Comput Oper Res 18(1):1–15CrossRefGoogle Scholar
  24. 24.
    Teichert K (2013) A hyperboxing pareto approximation method applied to radiofrequency ablation treatment planning. Ph.D. thesis, Universitaet KaiserslauternGoogle Scholar
  25. 25.
    Trovato K, Dalal S, Krücker J, Venkatesan A, Wood BJ (2009) Automated RFA planning for complete coverage of large tumors. In: Miga MI, Wong KH (eds) Proceedings SPIE, pp 72610D–72610D–7Google Scholar
  26. 26.
    Villard C, Baegert C, Schreck P, Soler L, Gangi A (2005) Optimal trajectories computation within regions of interest for hepatic RFA planning. In: Medical image computing and computer-assisted intervention-MICCAI 2005, Springer, pp 49–56Google Scholar
  27. 27.
    Villard C, Soler L, Gangi A, Mutter D, Marescaux J (2004) Toward realistic radiofrequency ablation of hepatic tumors 3D simulation and planning. In: Medical imaging 2004, international society for optics and photonics, pp 586–595Google Scholar
  28. 28.
    Weihusen A, Ritter F, Kröger T, Preusser T, Zidowitz S, Peitgen HO (2007) Workflow oriented software support for image guided radiofrequency ablation of focal liver malignancies. In: Cleary KR, Miga MI (eds) Proceedings SPIE, pp 650919–650919–9Google Scholar
  29. 29.
    Wong SL, Mangu PB, Choti MA, Crocenzi TS, Dodd GD, Dorfman GS, Eng C, Fong Y, Giusti AF, Lu D, Marsland TA, Michelson R, Poston GJ, Schrag D, Seidenfeld J, Benson AB (2010) American society of clinical oncology 2009 clinical evidence review on radiofrequency ablation of hepatic metastases from colorectal cancer. J Clin Oncol 28(3):493–508CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Christian Schumann
    • 1
  • Christian Rieder
    • 1
  • Sabrina Haase
    • 1
  • Katrin Teichert
    • 3
  • Philipp Süss
    • 3
  • Peter Isfort
    • 4
  • Philipp Bruners
    • 4
  • Tobias Preusser
    • 1
    • 2
  1. 1.Fraunhofer MEVISFraunhofer-GesellschaftBremenGermany
  2. 2.Jacobs UniversityBremenGermany
  3. 3.Fraunhofer ITWMFraunhofer-GesellschaftKaiserslauternGermany
  4. 4.Diagnostic and Interventional RadiologyRWTH Aachen University HospitalAachenGermany

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