Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

  • E. Yeniaras
  • D. T. Fuentes
  • S. J. Fahrenholtz
  • J. S. Weinberg
  • F. Maier
  • J. D. Hazle
  • R. J. Stafford
Original Article



   An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility.


   A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes’ bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images.


   The total time to initialize and simulate an MRgLITT procedure using the GUI was \(<\)5 min. Each independent simulation took \(<\)30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour \((57\,^{\circ }\hbox {C})\) was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm \((\sigma \,=\,0.4\,\hbox {mm})\), whereas the mean Dice similarity coefficient was 0.93 \((\sigma =0.026)\).


   We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.


Planning software MRI guidance  Laser-induced thermal therapy 3D Slicer  Treatment simulation Tissue ablation 



This research is supported in part by the MD Anderson Cancer Center Support Grant CA016672 and the National Institutes of Health (NIH) award 1R21EB010196-01 and Apache Corporation. All opinions, findings, conclusions, or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of our sponsors. The data were acquired in part from Visualase Inc. (Houston, TX, USA). Conflict of interest   None


  1. 1.
    Stafford RJ, Fuentes D, ElliottAA Weinberg JS, Ahrar K (2010) Laser-induced thermal therapy for tumor ablation. Crit Rev Biomed Eng 38:79–100PubMedCrossRefGoogle Scholar
  2. 2.
    Denis de Senneville B, Quesson B, Moonen CT (2005) Magnetic resonance temperature imaging. Int J Hyperth 21:515–531CrossRefGoogle Scholar
  3. 3.
    Rieke V, Butts Pauly K (2008) MR thermometry. J Magn Reason Imaging 27:376–390CrossRefGoogle Scholar
  4. 4.
    Dewhirst MW, Viglianti BL, Lora-Michiels M, Hanson M, Hoopes PJ (2003) Basic principles of thermal dosimetry and thermal thresholds for tissue damage from hyperthermia. Int J Hyperth 19:267–294CrossRefGoogle Scholar
  5. 5.
    McDannold N, Tempany CM, Fennessy FM, So MJ, Rybicki FJ, Stewart EA et al (2006) Uterine leiomyomas: MR imaging-based thermometry and thermal dosimetry during focused ultrasound thermal ablation. Radiology 240:263–272PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    McNichols RJ, Kangasniemi M, Gowda A, Bankson JA, Price RE, Hazle JD (2004) Technical developments for cerebral thermal treatment: water-cooled diffusing laser fibre tips and temperature-sensitive MRI using intersecting image planes. Int J Hyperth 20:45–56CrossRefGoogle Scholar
  7. 7.
    Schulze PC, Vitzthum HE, Goldammer A, Schneider JP, Schober R (2004) Laser-induced thermotherapy of neoplastic lesions in the brain-underlying tissue alterations, MRI-monitoring and clinical applicability. Acta Neurochir (Wien) 146:803–812CrossRefGoogle Scholar
  8. 8.
    Schwarzmaier HJ, Eickmeyer F, von Tempelhoff W, Fiedler VU, Niehoff H, Ulrich SD et al (2006) MR-guided laser-induced interstitial thermotherapy of recurrent glioblastoma multiforme: preliminary results in 16 patients. Eur J Radiol 59:208–215PubMedCrossRefGoogle Scholar
  9. 9.
    Paiva MB, Bublik M, Castro DJ, Udewitz M, Wang MB, Kowalski LP et al (2005) Intratumor injections of cisplatin and laser thermal therapy for palliative treatment of recurrent cancer. Photomed Laser Surg 23:531–535PubMedCrossRefGoogle Scholar
  10. 10.
    Schwarzmaier H-J, Eickmeyer F, Fiedler VU, Ulrich F (2002) Basic principles of laser induced interstitial thermotherapy in brain tumors. Med Laser Appl 17:147–158CrossRefGoogle Scholar
  11. 11.
    Schwarzmaier H-J, Eickmeyer F, von Tempelhoff W, Fiedler VU, Niehoff H, Ulrich SD et al (2006) MR-guided laser-induced interstitial thermotherapy of recurrent glioblastoma multiforme: preliminary results in 16 patients. Eur J Radiol 59:208–215PubMedCrossRefGoogle Scholar
  12. 12.
    Carpentier A, McNichols RJ, Stafford RJ, Guichard JP, Reizine D, Delaloge S et al (2011) Laser thermal therapy: real-time MRI-guided and computer-controlled procedures for metastatic brain tumors. Lasers Surg Med 43:943–950PubMedCrossRefGoogle Scholar
  13. 13.
    Curry DJ, Gowda A, McNichols RJ, Wilfong AA (2012) MR-guided stereotactic laser ablation of epileptogenic foci in children. Epilepsy Behav 24:408–414PubMedCrossRefGoogle Scholar
  14. 14.
    Bondarenko E, Iur’eva E, Zykov V, Alekseeva N (1997) The laser therapy of children with Tourette’s syndrome. Zhurnal nevrologii i psikhiatrii imeni SS Korsakova/Ministerstvo zdravookhraneniia i meditsinskoĭ promyshlennosti Rossiĭskoĭ Federatsii. Vserossiĭskoe obshchestvo nevrologov [i] Vserossiĭskoe obshchestvo psikhiatrov 97:29Google Scholar
  15. 15.
    Rahmathulla G, Recinos PF, Valerio JE, Chao S, Barnett GH (2012) Laser interstitial thermal therapy for focal cerebral radiation necrosis: a case report and literature review. Stereotact Funct Neurosurg 90:192–200PubMedCrossRefGoogle Scholar
  16. 16.
    Beccaria K, Canney MS, Carpentier AC (2012) Magnetic resonance-guided laser interstitial thermal therapy for brain tumors. In: Hayat MA (ed) Tumors of the central nervous system. Springer, Amsterdam, pp 173–185Google Scholar
  17. 17.
    Carpentier A, Chauvet D, Reina V, Beccaria K, Leclerq D, McNichols RJ et al (2012) MR-guided laser-induced thermal therapy (LITT) for recurrent glioblastomas. Lasers Surg Med 44:361–368PubMedCrossRefGoogle Scholar
  18. 18.
    Hawasli AH, Ray WZ, Murphy RK, Dacey RG Jr, Leuthardt EC (2012) Magnetic resonance imaging-guided focused laser interstitial thermal therapy for subinsular metastatic adenocarcinoma: technical case report. Neurosurgery 70:332–338PubMedCrossRefGoogle Scholar
  19. 19.
    Jones S, Barnett G, Sunshine JL, Griswold M, Sloan A, Phillips MD, Tyc R, Torchia M (2009) First human application of laser interstitial thermal therapy in GBM using MR guided autolitt system. In: Proceedings of the 17th scientific meeting, international society for magnetic resonance in medicineGoogle Scholar
  20. 20.
    Sunshine J, Sandhu G, Sloan A, Griswold M (2012) O-030 Laser thermotherapy of malignant brain lesions using MRI for needle guidance and real-time temperature mapping. J NeuroInterv Surg 4:A17–A17CrossRefGoogle Scholar
  21. 21.
    Fahrenholtz S, Fuentes D, Stafford R, Hazle J (2012) Uncertainty quantification by generalized polynomial chaos for MR-guided laser induced thermal therapy. Med Phys 39:3857CrossRefGoogle Scholar
  22. 22.
    Topaloglu U, Yan Y, Novak P, Spring P, Suen J, Shafirstein G, (2008) Virtual thermal ablation in the head and neck using comsol MultiPhysics. In: Proceedings of the COMSOL conference 2008, pp 1–7Google Scholar
  23. 23.
    Neufeld E, Paulides MM et al (2012) Numerical modeling for simulation and treatment planning of thermal therapy. In: Moros EG (ed) Physics of thermal therapy: fundamentals and clinical applications. CRC Press, Boca Raton Google Scholar
  24. 24.
    Kirk BS, Peterson JW, Stogner RH, Carey GF (2006) libMesh: a C++ library for parallel adaptive mesh refinement/coarsening simulations. Eng Comput 22:237–254Google Scholar
  25. 25.
    Tokuda J, Fischer GS, Papademetris X, Yaniv Z, Ibanez L, Cheng P et al (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robotics Comput Assist Surg 5:423–434Google Scholar
  26. 26.
    Pieper S, Halle M, Kikinis R (2004) 3D Slicer. In: Biomedical imaging: nano to macro, 2004. IEEE international symposium on, vol 1, pp 632–635Google Scholar
  27. 27.
    Kikinis R, Pieper S (2011) 3D Slicer as a tool for interactive brain tumor segmentation. Conf Proc IEEE Eng Med Biol Soc 4:6982–6984Google Scholar
  28. 28.
    Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G (2012) SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 39:6332PubMedCrossRefGoogle Scholar
  29. 29.
  30. 30.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Welch AJ, Van Gemert MJ (2010) Optical-thermal response of laser-irradiated tissue. Springer, BerlinGoogle Scholar
  32. 32.
    Welch A (1984) The thermal response of laser irradiated tissue. Quantum Electron IEEE J 20:1471–1481CrossRefGoogle Scholar
  33. 33.
    Bergman TL, Lavine AS, Incropera, FP DeWitt D (2011) Introduction to heat transfer, 6th edn. Wiley, HobokenGoogle Scholar
  34. 34.
    Fuentes D, Yusheng F, Elliott A, Shetty A, McNichols RJ, Oden JT et al (2010) Adaptive real-time bioheat transfer models for computer-driven MR-guided laser induced thermal therapy. Biomed Eng IEEE Trans 57:1024–1030CrossRefGoogle Scholar
  35. 35.
    Vezhnevets V, Konouchine V (2005) GrowCut: interactive multi-label ND image segmentation by cellular automata. In: Proceedings of graphicon, pp 150–156Google Scholar
  36. 36.
    Rockafellar RT, Wets RJ-B (1998) Variational analysis: Grundlehren der mathematischen wissenschaften, vol 317. Springer, NewyorkGoogle Scholar

Copyright information

© CARS 2013

Authors and Affiliations

  • E. Yeniaras
    • 1
  • D. T. Fuentes
    • 1
  • S. J. Fahrenholtz
    • 1
  • J. S. Weinberg
    • 2
  • F. Maier
    • 1
  • J. D. Hazle
    • 1
  • R. J. Stafford
    • 1
  1. 1.Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of NeurosurgeryThe University of Texas MD Anderson Cancer CenterHoustonUSA

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