Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

Payne, Stephen J.; Flanagan, Ronan; Pollari, Mika; Alhonnoro, Tuomas; Bost, Claire; O'Neill, David; Peng, Tingying; Stiegler, Philipp

doi:10.1098/rsta.2011.0240

Cited by 49 publications

(51 citation statements)

References 41 publications

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“…Small ones are represented implicitly in the parenchyma as a porous medium using the Wulff and Klinger model [8]. The bioheat equation is weakly coupled to a computational fluid dynamics (CFD) solver to accurately take into account the effect of blood circulation on the dissipated heat, while the blood flow in the porous tissue is computed by solving the Darcy's equation [9]. The heat transfer depends on the blood flow, which is not modified as the organ is heated (the effect of heat on the viscosity of the flow is neglected as well as the coagulation effect).…”

Section: Introductionmentioning

confidence: 99%

Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Audigier¹,

Mansi²,

Delingette³

et al. 2013

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013

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Abstract. Radio-frequency ablation (RFA), the most widely used minimally invasive ablative therapy of liver cancer, is challenged by a lack of patient-specific planning. In particular, the presence of blood vessels and time-varying thermal diffusivity makes the prediction of the extent of the ablated tissue difficult. This may result in incomplete treatments and increased risk of recurrence. We propose a new model of the physical mechanisms involved in RFA of abdominal tumors based on Lattice Boltzmann Method to predict the extent of ablation given the probe location and the biological parameters. Our method relies on patient images, from which level set representations of liver geometry, tumor shape and vessels are extracted. Then a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver is solved to estimate the extent of ablated tissue. After quantitative verifications against an analytical solution, we apply our framework to 5 patients datasets which include pre-and post-operative CT images, yielding promising correlation between predicted and actual ablation extent (mean point to mesh errors of 8.7 mm). Implemented on graphics processing units, our method may enable RFA planning in clinical settings as it leads to near real-time computation: 1 minute of ablation is simulated in 1.14 minutes, which is almost 60× faster than standard finite element method.

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Section: Introductionmentioning

confidence: 99%

Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Audigier¹,

Mansi²,

Delingette³

et al. 2013

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013

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show abstract

“…This was a simplified FEM model since in-vivo the liver is a very complex electrical and thermal organ due to its inhomogeneity and three different types of blood vessels of different diameters and flow velocities. FEM models used to study the influence of blood vessel can be found from literature such as [19]. We also realize that FEM modelling has its accuracy limitation in mesh size selection.…”

Section: F Experimental Evaluationmentioning

confidence: 99%

“…FEM modelling was used to analyses radiofrequency cancer ablation [17] by solving RF Joule (resistive) heating and Penne's bio-heat equation [18]. FEM modelling was further used to study the influence of blood vessel on the thermal lesion formation (the heat sink effects) during RFA [19] and the influence of other parameters such as water evaporation [20] and tissue conductivities with efficient RFA experiment design [21,22]. In this study, an anatomical 3D FEM model was used for RFA simulation.…”

Section: B 3d Fem Modellingmentioning

confidence: 99%

Bi-component conformal electrode for radiofrequency sequential ablation and circumferential separation of large tumours in solid organs: development and in-vitro evaluation

Wang

Luo

Coleman

et al. 2016

IEEE Trans. Biomed. Eng.

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“…The problem is further confounded by the difficulty of monitoring the ablation process itself [4,5]. Patient-specific image-based 3D modeling could resolve these problems by permitting pre-treatment planning of the simulated thermal ablation based on preoperative images of the lesion [6].…”

Section: Introductionmentioning

confidence: 99%

Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom

et al. 2012

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Purpose Minimally invasive treatment of solid cancers, especially in the breast and liver, remains clinically challenging, despite a variety of treatment modalities, including radiofrequency ablation (RFA), microwave ablation or highintensity focused ultrasound. Each treatment modality has advantages and disadvantages, but all are limited by placement of a probe or US beam in the target tissue for tumor ablation and monitoring. The placement is difficult when the tumor is surrounded by large blood vessels or organs. Patientspecific image-based 3D modeling for thermal ablation simulation was developed to optimize treatment protocols that improve treatment efficacy. Methods A tissue-mimicking breast gel phantom was used to develop an image-based 3D computer-aided design (CAD) model for the evaluation of a planned RF ablation. First, the tissue-mimicking gel was cast in a breast mold to create a 3D breast phantom, which contained a simulated solid tumor. Second, the phantom was imaged in a medical MRI scanner using a standard breast imaging MR sequence. Third, the MR images were converted into a 3D CAD model using commercial software (ScanIP, Simpleware), which was input into another commercial package (COMSOL Multiphysics) for RFA simulation and treatment planning using a finite element method (FEM). For validation of the model, the breast phantom was experimentally ablated using a commercial (RITA) RFA electrode and a bipolar needle with an electrosurgi- Results A 3D CAD model, created from MR images of the complex breast phantom, was successfully integrated with an RFA electrode to perform FEM ablation simulation. The ablation volumes achieved both in the FEM simulation and the experimental test were equivalent, indicating that patientspecific models can be implemented for pre-treatment planning of solid tumor ablation. Conclusion A tissue-mimicking breast gel phantom and its MR images were used to perform FEM 3D modeling and validation by experimental thermal ablation of the tumor. Similar patient-specific models can be created from preoperative images and used to perform finite element analysis to plan radiofrequency ablation. Clinically, the method can be implemented for pre-treatment planning to predict the effect of an individual's tissue environment on the ablation process, and this may improve the therapeutic efficacy.

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Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

Cited by 49 publications

References 41 publications

Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Lattice Boltzmann Method for Fast Patient-Specific Simulation of Liver Tumor Ablation from CT Images

Bi-component conformal electrode for radiofrequency sequential ablation and circumferential separation of large tumours in solid organs: development and in-vitro evaluation

Image-based 3D modeling and validation of radiofrequency interstitial tumor ablation using a tissue-mimicking breast phantom

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