Panchatcharam Mariappan1, Phil Weir2, Ronan Flanagan2, Philip Voglreiter3, Tuomas Alhonnoro4, Mika Pollari4, Michael Moche5, Harald Busse5, Jurgen Futterer6, Horst Rupert Portugaller7, Roberto Blanco Sequeiros8, Marina Kolesnik9. 1. NUMA Engineering Services Ltd, Dundalk, Ireland. panchatcharam.mariappan@numa.ie. 2. NUMA Engineering Services Ltd, Dundalk, Ireland. 3. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria. 4. Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. 5. Department of Diagnostic and Interventional Radiology, Leipzig University Hospital, Leipzig, Germany. 6. Radbound University Nijmegen Medical Center, Nijmegen, The Netherlands. 7. University Clinic of Radiology Graz, Graz, Austria. 8. Medical Imaging Center of Southwest Finland, Turku University Hospital, Turku, Finland. 9. Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany.
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.
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.
Authors: Tuomas Alhonnoro; Mika Pollari; Mikko Lilja; Ronan Flanagan; Bernhard Kainz; Judith Muehl; Ursula Mayrhauser; Horst Portugaller; Philipp Stiegler; Karlheinz Tscheliessnigg Journal: Med Image Comput Comput Assist Interv Date: 2010
Authors: Marco Nolden; Sascha Zelzer; Alexander Seitel; Diana Wald; Michael Müller; Alfred M Franz; Daniel Maleike; Markus Fangerau; Matthias Baumhauer; Lena Maier-Hein; Klaus H Maier-Hein; Hans-Peter Meinzer; Ivo Wolf Journal: Int J Comput Assist Radiol Surg Date: 2013-04-16 Impact factor: 2.924
Authors: S Masunaga; K Ono; M Mitsumori; Y Nishimura; M Hiraoka; K Akuta; Y Nagata; M Abe; M Takahashi; S Jo Journal: Jpn J Clin Oncol Date: 1996-12 Impact factor: 3.019
Authors: Martin Reinhardt; Philipp Brandmaier; Daniel Seider; Marina Kolesnik; Sjoerd Jenniskens; Roberto Blanco Sequeiros; Martin Eibisberger; Philip Voglreiter; Ronan Flanagan; Panchatcharam Mariappan; Harald Busse; Michael Moche Journal: Contemp Clin Trials Commun Date: 2017-08-18