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GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction.

Methods

Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne’s bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion.

Results

A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm.

Conclusion

A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

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Notes

  1. http://www.cgal.org.

  2. http://www.gmsh.info.

  3. https://github.com/google/protobuf.

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Authors and Affiliations

  1. NUMA Engineering Services Ltd, Dundalk, Ireland

    Panchatcharam Mariappan, Phil Weir & Ronan Flanagan

  2. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Philip Voglreiter

  3. Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland

    Tuomas Alhonnoro & Mika Pollari

  4. Department of Diagnostic and Interventional Radiology, Leipzig University Hospital, Leipzig, Germany

    Michael Moche & Harald Busse

  5. Radbound University Nijmegen Medical Center, Nijmegen, The Netherlands

    Jurgen Futterer

  6. University Clinic of Radiology Graz, Graz, Austria

    Horst Rupert Portugaller

  7. Medical Imaging Center of Southwest Finland, Turku University Hospital, Turku, Finland

    Roberto Blanco Sequeiros

  8. Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany

    Marina Kolesnik

Authors
  1. Panchatcharam Mariappan
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  2. Phil Weir
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Corresponding author

Correspondence to Panchatcharam Mariappan.

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Additional information

This research work is part of seventh Framework Programme (FP7) Project Clinical Intervention Modelling, Planning and Proof for Ablation Cancer Treatment (ClinicIMPPACT, http://www.clinicimppact.eu) and it is co-funded by the European commission under Grant Agreement Number 610886.

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Mariappan, P., Weir, P., Flanagan, R. et al. GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours. Int J CARS 12, 59–68 (2017). https://doi.org/10.1007/s11548-016-1469-1

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  • DOI: https://doi.org/10.1007/s11548-016-1469-1

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