Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Mariappan, P; Weir, P; Flanagan, R; Voglreiter, P; Alhonnoro, T; Pollari, M; Moche, M; Busse, H; Futterer, J; Portugaller, HR; Sequeiros, RB; Kolesnik, M.
GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.
Int J Comput Assist Radiol Surg. 2017; 12(1): 59-68.
Web of Science PubMed FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Portugaller Rupert
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Abstract:
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. 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. 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. 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.
Find related publications in this database (using NLM MeSH Indexing)
Carcinoma, Hepatocellular - diagnostic imaging
Carcinoma, Hepatocellular - surgery
Catheter Ablation - methods
Computer Graphics -
Computer Simulation -
Humans -
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - surgery
Minimally Invasive Surgical Procedures -
Models, Theoretical -
Perfusion Imaging -
Retrospective Studies -
Surgery, Computer-Assisted -
Tomography, X-Ray Computed -

Find related publications in this database (Keywords)
Radiofrequency ablation
RFA solver
Perfusion
GPU
Bioheat equation
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