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

International Journal of Computer Assisted Radiology and Surgery volume 9, pages 659–667 (2014)Cite this article

Abstract

Purpose

   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.

Methods

   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.

Results

   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 \((\sigma =0.026)\).

Conclusions

   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.

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References

  1. Stafford RJ, Fuentes D, ElliottAA Weinberg JS, Ahrar K (2010) Laser-induced thermal therapy for tumor ablation. Crit Rev Biomed Eng 38:79–100

    Article  PubMed  Google Scholar 

  2. Denis de Senneville B, Quesson B, Moonen CT (2005) Magnetic resonance temperature imaging. Int J Hyperth 21:515–531

    Article  CAS  Google Scholar 

  3. Rieke V, Butts Pauly K (2008) MR thermometry. J Magn Reason Imaging 27:376–390

    Article  Google Scholar 

  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 19:267–294

    Article  CAS  Google Scholar 

  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 240:263–272

    Article  PubMed Central  PubMed  Google Scholar 

  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 20:45–56

    Article  CAS  Google Scholar 

  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) 146:803–812

    Article  CAS  Google Scholar 

  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 59:208–215

    Article  PubMed  Google Scholar 

  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 23:531–535

    Article  PubMed  Google Scholar 

  10. Schwarzmaier H-J, Eickmeyer F, Fiedler VU, Ulrich F (2002) Basic principles of laser induced interstitial thermotherapy in brain tumors. Med Laser Appl 17:147–158

    Article  Google Scholar 

  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 59:208–215

    Article  PubMed  Google Scholar 

  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 43:943–950

    Article  PubMed  Google Scholar 

  13. Curry DJ, Gowda A, McNichols RJ, Wilfong AA (2012) MR-guided stereotactic laser ablation of epileptogenic foci in children. Epilepsy Behav 24:408–414

    Article  PubMed  Google Scholar 

  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 97:29

  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 90:192–200

    Article  PubMed  Google Scholar 

  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 173–185

    Google Scholar 

  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 44:361–368

    Article  PubMed  Google Scholar 

  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 70:332–338

    Article  PubMed  Google Scholar 

  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 medicine

  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–A17

    Article  Google Scholar 

  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 39:3857

    Article  Google Scholar 

  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 1–7

  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

  24. Kirk BS, Peterson JW, Stogner RH, Carey GF (2006) libMesh: a C++ library for parallel adaptive mesh refinement/coarsening simulations. Eng Comput 22:237–254

    Google Scholar 

  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 5:423–434

    Google Scholar 

  26. Pieper S, Halle M, Kikinis R (2004) 3D Slicer. In: Biomedical imaging: nano to macro, 2004. IEEE international symposium on, vol 1, pp 632–635

  27. Kikinis R, Pieper S (2011) 3D Slicer as a tool for interactive brain tumor segmentation. Conf Proc IEEE Eng Med Biol Soc 4:6982–6984

    Google Scholar 

  28. Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G (2012) SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 39:6332

    Article  PubMed  Google Scholar 

  29. Available: http://www.slicer.org/pages/Slicer_Community

  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 30:1323–1341

    Article  PubMed Central  PubMed  Google Scholar 

  31. Welch AJ, Van Gemert MJ (2010) Optical-thermal response of laser-irradiated tissue. Springer, Berlin

    Google Scholar 

  32. Welch A (1984) The thermal response of laser irradiated tissue. Quantum Electron IEEE J 20:1471–1481

    Article  Google Scholar 

  33. Bergman TL, Lavine AS, Incropera, FP DeWitt D (2011) Introduction to heat transfer, 6th edn. Wiley, Hoboken

  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 57:1024–1030

    Article  Google Scholar 

  35. Vezhnevets V, Konouchine V (2005) GrowCut: interactive multi-label ND image segmentation by cellular automata. In: Proceedings of graphicon, pp 150–156

  36. Rockafellar RT, Wets RJ-B (1998) Variational analysis: Grundlehren der mathematischen wissenschaften, vol 317. Springer, Newyork

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA

    E. Yeniaras, D. T. Fuentes, S. J. Fahrenholtz, F. Maier, J. D. Hazle & R. J. Stafford

  2. Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA

    J. S. Weinberg

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  1. E. Yeniaras
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  2. D. T. Fuentes
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  3. S. J. Fahrenholtz
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  4. J. S. Weinberg
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  5. F. Maier
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  6. J. D. Hazle
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  7. R. J. Stafford
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Correspondence to R. J. Stafford.

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Yeniaras, E., Fuentes, D.T., Fahrenholtz, S.J. et al. Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain. Int J CARS 9, 659–667 (2014). https://doi.org/10.1007/s11548-013-0948-x

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