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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-01

AUTHORS

Panchatcharam Mariappan, Phil Weir, Ronan Flanagan, Philip Voglreiter, Tuomas Alhonnoro, Mika Pollari, Michael Moche, Harald Busse, Jurgen Futterer, Horst Rupert Portugaller, Roberto Blanco Sequeiros, Marina Kolesnik

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. More... »

PAGES

59-68

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11548-016-1469-1

    DOI

    http://dx.doi.org/10.1007/s11548-016-1469-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049285964

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27538836


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