Efficient Lattice Boltzmann Solver for Patient-Specific Radiofrequency Ablation of Hepatic Tumors

Heat: high-efficiency simulation for thermal ablation therapy

PURPOSE

Percutaneous thermal ablation is increasingly popular but still suffers from a complex preoperative planning, especially with multiple needles. Existing planning methods either use theoretical ablation shapes for faster estimates or are computationally intensive when incorporating realistic thermal propagation. This paper introduces a multi-resolution approach that accelerates thermal propagation simulation, enabling users to adjust ablation parameters and see the results in interactive time.

METHODS

For static needle positions, a high-resolution simulation based on GPU-accelerated implementation of the Pennes bioheat equation is used. During user interaction, intermediate frames display a lower-resolution estimation of the ablated volume. Two methods are compared, based on GPU-accelerated reimplementations of finite difference and lattice Boltzmann approaches. A parameter study was conducted to identify the optimal balance between speed and accuracy for the low- and high-resolution frames. The chosen parameters are finally tested in multi-needle scenarios to validate the interactive capability in this context.

RESULTS

Tested with percutaneous radiofrequency data, our multi-resolution method significantly reduces computation time while maintaining good accuracy compared to the reference simulation. For high-resolution frames, we can reach up to 5.8 fps, while for intermediate low-resolution frames we can reach a frame rate of 32 fps with less than 20% loss of accuracy.

CONCLUSION

This multi-resolution approach allows for smooth interaction with multiple needles, with instant visualization of the predicted ablation volume, in the context of percutaneous radiofrequency treatments. It could also be applied to automated planning, reducing the time required for iterative adjustments.

Assessment of thermal damage for plasmonic photothermal therapy of subsurface tumors

Plasmonic photothermal therapy (PPTT) involves the use of nanoparticles and near-infrared radiation to attain a temperature above 50 °C within the tumor for its thermal damage. PPTT is largely explored for superficial tumors, and its potential to treat deeper subsurface tumors is dealt feebly, requiring the assessment of thermal damage for such tumors. In this paper, the extent of thermal damage is numerically analyzed for PPTT of invasive ductal carcinoma (IDC) situated at 3-9 mm depths. The developed numerical model is validated with suitable tissue-tumor mimicking phantoms. Tumor (IDC) embedded with gold nanorods (GNRs) is subjected to broadband near-infrared radiation. The effect of various GNRs concentrations and their spatial distributions [viz. uniform distribution, intravenous delivery (peripheral distribution) and intratumoral delivery (localized distribution)] are investigated for thermal damage for subsurface tumors situated at various depths. Results show that lower GNRs concentrations lead to more uniform internal heat generation, eventually resulting in uniform temperature rise. Also, the peripheral distribution of nanoparticles provides a more uniform spatial temperature rise within the tumor. Overall, it is concluded that PPTT has potential to induce thermal damage for subsurface tumors, at depths of upto 9 mm, by proper choice of nanoparticle distribution, dose/concentration and irradiation parameters based on the tumor location. Moreover, intravenous administration of nanoparticles seems a good choice for shallower tumors, while for deeper tumors, uniform distribution is required to attain the necessary thermal damage. In the future, the algorithm may be extended further, involving 3D patient-specific tumors and through mice model-based experiments.

Role of periodic irradiation and incident beam radius for plasmonic photothermal therapy of subsurface tumors

Plasmonic photothermal therapy (PPTT) is a potential technique to treat tumors selectively. However, during PPTT, issue of high temperature region and damage to the surrounding healthy is still need to be resolved. Also, treatment of deeper tumors non-invasively is a challenge for PPTT. In this paper, the effect of periodic irradiation and incident beam radius (relative to tumor size) for various gold nanorods (GNRs) concentrations is investigated to avoid much higher temperatures region with limiting thermal damage to the surrounding healthy tissue during PPTT of subsurface breast tumors located at various depths. Lattice Boltzmann method is used to solve Pennes' bioheat model to compute the resulting photothermal temperatures for the subsurface tumor embedded with GNRs subjected to broadband near infrared radiation of intensity 1 W/cm2. Computation revealed that low GNRs concentration leads to uniform internal heat generation than higher GNRs concentrations. The results show that deeper tumors, due to attenuation of incident radiation, show low temperature rise than shallower tumors. For shallower tumors situated 3 mm deep, 70% irradiation period resulted in around 20 °C reduction (110 °C-90 °C) of maximum temperature than that with the continuous irradiation. Moreover, 70% beam radius (i.e., beam radius as 70% of the tumor radius) causes less thermal damage to the nearby healthy tissue than 100% beam radius (i.e., beam radius equal to the tumor radius). The thermal damage within the healthy tissue is minimized to the 1 mm in radial direction and 3 mm in axial direction for 70% beam radius with 70% irradiation period. Overall, periodic heating and changing beam radius of the incident irradiation lead to reduce high temperature and limit healthy tissue damage. Hence, discussed results are useful for selection of the irradiation parameters for PPTT of sub-surface tumors.

A comparative study on computational models of multi-electrode radiofrequency ablation of large liver tumors

PURPOSE

Thermal ablation of liver tumors has emerged as a first-line curative treatment for single small tumors (diameter < 2.5 cm) due to similar overall survival rates as surgical resection. Moreover, it is far less invasive, has lower complication rates, a superior cost-effectiveness, and an extremely low treatment-associated mortality. However, in many cases, complete tumor coverage cannot be achieved only with a single electrode and several electrodes are used to create overlapping ablations. Multi-electrode planning is a challenging 3D task with many contradictive constraints to consider, a dimensionality difficult to assess even for experts. It requires extremely long planning time since it is mostly performed mentally by clinicians looking at 2D CT views. An accurate and reliable prediction of the ablation zone would help to turn thermal ablation into a first-line curative treatment also for large liver tumors treated with multiple electrodes. In order to determine the level of model simplification that can be acceptable, we compared three computational models, a simple spherical model, a biophysics-based model and an Eikonal model.

METHODS

RF ablation electrodes were virtually placed at a desired position in the patient pre-operative CT image and the models predicted the ablation zone generated by multiple electrodes. The last two models are patient-specific. In these cases, hepatic structures were automatically segmented from the pre-operative CT images to predict a patient-specific ablation zone.

RESULTS

The three models were used to simulate multiple electrode ablations on 12 large tumors from 11 patients for which the procedure information was available. Biophysics-based simulations approximate better the post-operative ablation zone in term of Hausdorff distance, Dice Similarity Coefficient, radius, and volume compared to two other methods. It also predicts better the coverage percentage and thus the tumor ablation margin.

CONCLUSION

The results obtained with the biophysics-based model indicate that it could improve ablation planning by accurately predicting the ablation zone, avoiding over or under-treatment. This is particularly beneficial for multi-electrode radiofrequency ablation of larger liver tumors where the planning phase is particularly challenging.

MoS2-based nanocomposites for cancer diagnosis and therapy

Molybdenum is a trace dietary element necessary for the survival of humans. Some molybdenum-bearing enzymes are involved in key metabolic activities in the human body (such as xanthine oxidase, aldehyde oxidase and sulfite oxidase). Many molybdenum-based compounds have been widely used in biomedical research. Especially, MoS2-nanomaterials have attracted more attention in cancer diagnosis and treatment recently because of their unique physical and chemical properties. MoS2 can adsorb various biomolecules and drug molecules via covalent or non-covalent interactions because it is easy to modify and possess a high specific surface area, improving its tumor targeting and colloidal stability, as well as accuracy and sensitivity for detecting specific biomarkers. At the same time, in the near-infrared (NIR) window, MoS2 has excellent optical absorption and prominent photothermal conversion efficiency, which can achieve NIR-based phototherapy and NIR-responsive controlled drug-release. Significantly, the modified MoS2-nanocomposite can specifically respond to the tumor microenvironment, leading to drug accumulation in the tumor site increased, reducing its side effects on non-cancerous tissues, and improved therapeutic effect. In this review, we introduced the latest developments of MoS2-nanocomposites in cancer diagnosis and therapy, mainly focusing on biosensors, bioimaging, chemotherapy, phototherapy, microwave hyperthermia, and combination therapy. Furthermore, we also discuss the current challenges and prospects of MoS2-nanocomposites in cancer treatment.

The Way of the Future: Personalizing Treatment Plans Through Technology

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.

Shape-shifting thermal coagulation zone during saline-infused radiofrequency ablation: A computational study on the effects of different infusion location

BACKGROUND AND OBJECTIVE

The majority of the studies on radiofrequency ablation (RFA) have focused on enlarging the size of the coagulation zone. An aspect that is crucial but often overlooked is the shape of the coagulation zone. The shape is crucial because the majority of tumours are irregularly-shaped. In this paper, the ability to manipulate the shape of the coagulation zone following saline-infused RFA by altering the location of saline infusion is explored.

METHODS

A 3D model of the liver tissue was developed. Saline infusion was described using the dual porosity model, while RFA was described using the electrostatic and bioheat transfer equations. Three infusion locations were investigated, namely at the proximal end, the middle and the distal end of the electrode. Investigations were carried out numerically using the finite element method.

RESULTS

Results indicated that greater thermal coagulation was found in the region of tissue occupied by the saline bolus. Infusion at the middle of the electrode led to the largest coagulation volume followed by infusion at the proximal and distal ends. It was also found that the ability to delay roll-off, as commonly associated with saline-infused RFA, was true only for the case when infusion is carried out at the middle. When infused at the proximal and distal ends, the occurrence of roll-off was advanced. This may be due to the rapid and more intense heating experienced by the tissue when infusion is carried out at the electrode ends where Joule heating is dominant.

CONCLUSION

Altering the location of saline infusion can influence the shape of the coagulation zone following saline-infused RFA. The ability to 'shift' the coagulation zone to a desired location opens up great opportunities for the development of more precise saline-infused RFA treatment that targets specific regions within the tissue.

Possible involvement of HSP70 in pancreatic cancer cell proliferation after heat exposure and impact on RFA postoperative patient prognosis

Purpose

As an alleviative treatment measured in patients with unresectable advanced pancreatic cancer, radiofrequency ablation (RFA) needed more clinical data to prove its advantages and to explore limitations in its utilization. This study was determined to observe the efficacy of RFA, and to explore its impact on perioperative periphery carcinoma as well as the normal pancreatic tissues.

Methods

Clinical data of 32 patients with pancreatic cancer accepted RFA surgery were collected. Followed up patients' pain degree and the changes in serum tumor markers CA19-9 and CA 242 before and after surgery. Ex vivo, gave human pancreatic cancer cell line PANC-1 heat treatment to simulate the heat exposure condition periphery carcinoma was experienced during RFA surgery, and to observe the proliferation rate and HSP70 expression change compared with control group.

Results

Of the 32 patients, 1 died of upper gastrointestinal hemorrhage, and 29 survived for more than 5 months, 2 of which for more than 16 months. The average CA19-9 and CA 242 levels of the patients were significantly decreased in 3 months after surgery (t = 9.873, 5.978, P < 0.001). During in vitro experiments, the proliferation rate of PANC-1 cells after heating was significantly increased, accompanied with the increased HSP70 expression. The addition of HSP70 inhibitor can inhibit the rise of proliferation after heat therapy.

Conclusion

Utilizing RFA treat patients with unresectable advanced pancreatic cancer, could effectively relieve the pain, decline jaundice, and deduce tumor marker levels significantly. However, it failed to extend the long-term survival rate of the patients significantly. This study found that a higher proliferative rate accompanied with a higher HSP70 expression level were observed on in vitro cultured pancreatic carcinoma cells after heat treatment, which could be altered by HSP70 inhibitor. And these findings indicated that the heat exposure might impact periphery carcinoma during RFA surgery and HSP70 might play an important role in patients' prognosis.

GPU-based 3D iceball modeling for fast cryoablation simulation and planning

PURPOSE

The elimination of abdominal tumors by percutaneous cryoablation has been shown to be an effective and less invasive alternative to open surgery. Cryoablation destroys malignant cells by freezing them with one or more cryoprobes inserted into the tumor through the skin. Alternating cycles of freezing and thawing produce an enveloping iceball that causes the tumor necrosis. Planning such a procedure is difficult and time-consuming, as it is necessary to plan the number and cryoprobe locations and predict the iceball shape which is also influenced by the presence of heating sources, e.g., major blood vessels and warm saline solution, injected to protect surrounding structures from the cold.

METHODS

This paper describes a method for fast GPU-based iceball modeling based on the simulation of thermal propagation in the tissue. Our algorithm solves the heat equation within a cube around the cryoprobes tips and accounts for the presence of heating sources around the iceball.

RESULTS

Experimental results of two studies have been obtained: an ex vivo warm gel setup and simulation on five retrospective patient cases of kidney tumors cryoablation with various levels of complexity of the vascular structure and warm saline solution around the tumor tissue. The experiments have been conducted in various conditions of cube size and algorithm implementations. Results show that it is possible to obtain an accurate result within seconds.

CONCLUSION

The promising results indicate that our method yields accurate iceball shape predictions in a short time and is suitable for surgical planning.

An analytical solution for temperature distributions in hepatic radiofrequency ablation incorporating the heat-sink effect of large vessels

Fast prediction of the local thermal field induced by radiofrequency ablation (RFA) plays a critical role in hepatic RFA therapy. At present, it is still a challenging task to calculate and visualize the temperature distribution of RFA in real-time, especially when the heat-sink effect of adjacent large vessels is taken into account. To achieve this, the current investigation presented an analytical solution to calculate the temperature in RFA with an execution time of 0.05 s for three dimensional thermal field reconstruction. The presented temperature distribution is a combination of temperatures in homogeneous tissue and a quantification of the heat-sink effect of adjacent blood vessels. Temperatures in homogeneous tissue is calculated from a simplified Pennes bioheat equation, where several weighting parameters in the temperature expression are determined based on some reference point temperatures from the numerical simulation. The heat-sink effect is quantified based on a temperature factor, which measures the temperature difference between the vessel and the heated tissue, and a distance factor, which measures the distance to the vessel. The proposed method is validated to be able to gain similar temperature distributions to the numerical simulation but with its computational time being orders of magnitude smaller than that of numerical simulation, which improves the efficiency of interactive planning of RFA.

Ultrasound thermal monitoring with an external ultrasound source for customized bipolar RF ablation shapes

PURPOSE

Thermotherapy is a clinical procedure which delivers thermal energy to a target, and it has been applied for various medical treatments. Temperature monitoring during thermotherapy is important to achieve precise and reproducible results. Medical ultrasound can be used for thermal monitoring and is an attractive medical imaging modality due to its advantages including non-ionizing radiation, cost-effectiveness and portability. We propose an ultrasound thermal monitoring method using a speed-of-sound tomographic approach coupled with a biophysical heat diffusion model.

METHODS

We implement an ultrasound thermometry approach using an external ultrasound source. We reconstruct the speed-of-sound images using time-of-flight information from the external ultrasound source and convert the speed-of-sound information into temperature by using the a priori knowledge brought by a biophysical heat diffusion model.

RESULTS

Customized treatment shapes can be created using switching channels of radio frequency bipolar needle electrodes. Simulations of various ablation lesion shapes in the temperature range of 21-59 [Formula: see text]C are performed to study the feasibility of the proposed method. We also evaluated our method with ex vivo porcine liver experiments, in which we generated temperature images between 22 and 45 [Formula: see text]C.

CONCLUSION

In this paper, we present a proof of concept showing the feasibility of our ultrasound thermal monitoring method. The proposed method could be applied to various thermotherapy procedures by only adding an ultrasound source.

RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors

The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques.

MoS2 nanosheets encapsulated in sodium alginate microcapsules as microwave embolization agents for large orthotopic transplantation tumor therapy

In recent years, it is prevalent to treat various kinds of the tumors through microwave ablation method. However, it is still very difficult to ablate large tumors by the traditional microwave ablation therapy. In this work, an effective microwave embolization agent designed by encapsulating molybdenum sulfide nanosheets in the sodium alginate microcapsules, denoted as MSMCs, was prepared for the effective therapy of large tumor. The toxicity evaluation showed that MSMC had a good biocompatibility in vitro. The in vitro and in vivo experiments demonstrated that the MSMC was an excellent embolic and microwave susceptible agent that could be used for dual-enhanced microwave ablation therapy. As such, the MSMC showed excellent tumor therapeutic effect with 5 times larger ablation zone observed by magnetic resonance (MR) imaging than the microwave alone after 3 days treating. Besides, the tumor is nearly completely ablated and can not be recurrent due to the persistent hyperthermia. Moreover, MSMCs have a good biocompatibility and can be degraded and cleared from the body. It is believed that the MSMC is demonstrated to be a promising multifunctional theranostic agent used for treating the larger tumor via the synergistic therapy of enhanced microwave ablation and transcatheter arterial embolization (TAE).

Transport of Liposome Encapsulated Drugs in Voxelized Computational Model of Human Brain Tumors

There are many obstacles in the transport of chemotherapeutic drugs to tumor cells that lead to irregular and non-uniform uptake of drugs inside tumors. The study of these transport problems will help with accurate prediction of drug transport and optimizing treatment strategy. To this end, liposome mediated drug delivery has emerged as an excellent anticancer therapy due to its ability to deliver drugs at site of action and reducing the chances of side effects to the healthy tissues. In this paper, a computational fluid dynamics (CFD) model based on realistic vasculature of human brain tumor is presented. This model utilizes dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data to account for heterogeneity in tumor vasculature. Porosity of the interstitial space inside the tumor and normal tissue is determined voxel-wise by processing the DCE-MRI images by general tracer kinetic model (GTKM). The CFD model is applied to predict transport of two different types of liposomes (stealth and conventional) in tumors. The amount of accumulated liposomes is compared with accumulated free drug (doxorubicin) in the interstitial space. Simulation results indicate that stealth liposomes accumulate more and remain for longer periods of time in tumors as compared with conventional liposomes and free drug. The present model provides us a qualitative and quantitative examination on the transport and deposition of liposomes as well as free drugs in actual human brain tumors.

A fast forward solver of fluorescence diffuse optical tomography based on the lattice Boltzmann method

Fluorescence diffuse optical tomography (FDOT) is a new molecular imaging technology, which uses near-infrared light to excite the fluorophore in tissues. According to the measurements detected on the surface of imaged object, the fluorescent quantum yield as well as lifetime of the fluorescence can be reconstructed. However, the reconstruction of FDOT remains a challenging problem because the conventional forward solvers are time consuming. Thus, a forward model solver that would enable the fast imaging is necessary. This paper describes a new forward solver to simulate the propagation of photons in tissues based on the lattice Boltzmann method (LBM). This is accomplished by propagation photons in tissues guided by the LBM. To evaluate the performance of the proposed LBM, based on the numerical simulation, we compared the light distribution generated by the LBM with the diffusion equation implemented by COMSOL in four different cases. The experimental results indicate that compared to diffusion equation, the LBM can reduce the computation time for the forward solver of FDOT while preserving the similar accuracy.

MRI Based Bayesian Personalization of a Tumor Growth Model

The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution. Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images of a given patient. The estimation of the posterior probability is based on: 1) a highly parallelized implementation of the reaction-diffusion equation using the Lattice Boltzmann Method (LBM), and 2) a high acceptance rate Monte Carlo technique called Gaussian Process Hamiltonian Monte Carlo (GPHMC). We compare this personalization approach with two commonly used methods based on the spherical asymptotic analysis of the reaction-diffusion model, and on a derivative-free optimization algorithm. We demonstrate the performance of the method on synthetic data, and on seven patients with a glioblastoma, the most aggressive primary brain tumor. This Bayesian personalization produces more informative results. In particular, it provides samples from the regions of interest and highlights the presence of several modes for some patients. In contrast, previous approaches based on optimization strategies fail to reveal the presence of different modes, and correlation between parameters.

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

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.