Effect of high blood flow on heat distribution and ablation zone during microwave ablation-numerical approach.

Microwave ablation has become a viable alternative for cancer treatment for patients who cannot undergo surgery. During this procedure, a single-slot coaxial antenna is employed to effectively deliver microwave energy to the targeted tissue. The success of the treatment was measured by the amount of ablation zone created during the ablation procedure. The significantly large blood vessel placed near the antenna causes heat dissipation by convection around the blood vessel. The heat sink effect could result in insufficient ablation, raising the risk of local tumor recurrence. In this study, we investigated the heat loss due to large blood vessels and the relationship between blood velocity and temperature distribution. The hepatic artery, with a diameter of 4 mm and a height of 50 mm and two branches, is considered in the computational domain. The temperature profile, localized tissue contraction, and ablation zones were simulated for initial blood velocities 0.05, 0.1, and 0.16 m/s using the 3D Pennes bio-heat equation, temperature-time dependent model, and cell death model, respectively. Temperature-dependent blood velocity is modeled using the Navier-Stokes equation, and the fluid-solid interaction boundary is treated as a convective boundary. For discretization, we utilized H curl Ω $$ H\left(\operatorname{curl},\Omega \right) $$ elements for the wave propagation model, H 1 Ω $$ {H}^1\left(\Omega \right) $$ elements for the Pennes bio-heat model, and H 1 Ω 3 × L 0 2 Ω $$ {\left({H}^1\left(\Omega \right)\right)}^3\times {L}_0^2\left(\Omega \right) $$ elements for the Navier-Stokes equation, where Ω $$ \Omega $$ represents the computational domain. The simulated results show that blood vessels and blood velocity have a significant impact on temperature distribution, tissue contraction, and the volume of the ablation zone.

3D modeling of vector/edge finite element method for multi-ablation technique for large tumor-computational approach.

Microwave ablation (MWA) is a cancer thermal ablation treatment that uses electromagnetic waves to generate heat within the tissue. The goal of this treatment is to eliminate tumor cells while leaving healthy cells unharmed. During MWA, excess heat generation can kill healthy cells. Hence, mathematical models and numerical techniques are required to analyze the heat distribution in the tissue before the treatment. The aim of this research is to explain the implementation of the 3D vector finite element method in a wave propagation model that simulates the specific absorption rate in the liver. The 3D Nedelec elements from H(curl; Ω) space are used to discretize the wave propagation model, and this implementation is helpful in solving many real-world problems that involve electromagnetic propagation with perfect conducting and absorbing boundary conditions. One of the difficulties in ablation treatment is creating a large ablation zone for a large tumor (diameter greater than 3 cm) in a short period of time with minimum damage to the surrounding tissue. This article addresses the aforementioned issue by introducing four antennas into the different places of the tumor sequentially and producing heat uniformly over the tumor. The results demonstrated that 95.5% of the tumor cells were killed with minimal damage to the healthy cells when the heating time was increased to 4 minutes at each position. Subsequently, we studied the temperature distribution and localised tissue contraction in the tissue using the three-dimensional bio-heat equation and temperature-time dependent model, respectively. The local tissue contraction is measured at arbitrary points in the domain and is more noticeable at temperatures higher than 102°C. The thermal damage in the liver during MWA treatment is investigated using the three-state cell death model. The system of partial differential equations is solved numerically due to the complex geometry of the domain, and the results are compared with experimental data to validate the models and parameters.

A vector finite element approach to temperature dependent parameters of microwave ablation for liver cancer.

Microwave ablation (MWA) is a minimally invasive treatment for cancer that uses electromagnetic waves to kill the tumor cells without significantly damaging the surrounding healthy cells. A three-state cell death model calculates the thermal damage around the Hepatocellular carcinoma (HCC) tumor in the liver tissue. The temperature profile is simulated for a single-slot co-axial antenna with a 1 mm air slot located near the tip of the antenna to produce an adequate amount of heat. The aims of this study are (1) to use the vector/edge finite element method (VFEM) to simulate the electromagnetic wave propagation to obtain the specific absorption rate, which is an input for the bio-heat equation that predicts the heat distribution in the liver tissue during MWA treatment, and (2) to compare the computational costs of VFEM and the finite element method (FEM) when different types of input powers and dielectric properties are used in the wave propagation equation. This study claims that the accuracy level increases marginally with less computation cost while using VFEM for temperature-dependent wave propagation equation.

Retrospective Study for Validation and Improvement of Numerical Treatment Planning of Irreversible Electroporation Ablation for Treatment of Liver Tumors.

OBJECTIVE: The aims of this study were to determine the electric field threshold that best fits the local response to irreversible electroporation (IRE) ablation of hepatic tumors as seen in follow-up MRI; to numerically evaluate the heat generating effect of IRE; and to demonstrate the utility of treatment planning to improve procedures in the future. METHODS: 18 cases of hepatic tumors treated with IRE ablation were numerically reconstructed and treatment outcome was computed with a numerical treatment planning framework. Simulated ablation volumes were compared to ablation volumes segmented from 6-week follow-up MRI. Two cases with a high thermal component were selected for numerical optimization. RESULTS: The best fit between segmented and simulated ablation zones was obtained at 900 V/cm threshold with the average absolute error of 5.6 ± 1.5 mm. Considerable heating was observed in 7/18 cases, where >50% of tumor volume experienced heating likely to cause thermal damage. In the selected two cases, thermal damage was eliminated with adjustment of applied voltages. CONCLUSION: Lesions visible on MRI 6 weeks post IRE represent areas that experienced an electric field of 900 V/cm or higher. This threshold is higher than previously reported for IRE of hepatic tumors. It is likely the 6-week follow-up period was too long and the ablation zone has already shrunk considerably, resulting in overestimation of the threshold. SIGNIFICANCE: We developed a sophisticated method for validation of the numerical treatment planning framework. A future prospective study can be effectively designed based on the findings of this study.

Validation of a Web-Based Planning Tool for Percutaneous Cryoablation of Renal Tumors.

PURPOSE: To validate a simulation environment for virtual planning of percutaneous cryoablation of renal tumors. MATERIALS AND METHODS: Prospectively collected data from 19 MR-guided procedures were used for validation of the simulation model. Volumetric overlap of the simulated ablation zone volume (Σ) and the segmented ablation zone volume (S; assessed on 1-month follow-up scan) was quantified. Validation metrics were DICE Similarity Coefficient (DSC; the ratio between twice the overlapping volume of both ablation zones divided by the sum of both ablation zone volumes), target overlap (the ratio between the overlapping volume of both ablation zones to the volume of S; low ratio means S is underestimated), and positive predictive value (the ratio between the overlapping volume of both ablation zones to the volume of Σ; low ratio means S is overestimated). Values were between 0 (no alignment) and 1 (perfect alignment), a value > 0.7 is considered good. RESULTS: Mean volumes of S and Σ were 14.8 cm3 (± 9.9) and 26.7 cm3 (± 15.0), respectively. Mean DSC value was 0.63 (± 0.2), and ≥ 0.7 in 9 cases (47%). Mean target overlap and positive predictive value were 0.88 (± 0.11) and 0.53 (± 0.24), respectively. In 17 cases (89%), target overlap was ≥ 0.7; positive predictive value was ≥ 0.7 in 4 cases (21%) and < 0.6 in 13 cases (68%). This indicates S is overestimated in the majority of cases. CONCLUSION: The validation results showed a tendency of the simulation model to overestimate the ablation effect. Model adjustments are necessary to make it suitable for clinical use.

Clinical evaluation of in silico planning and real-time simulation of hepatic radiofrequency ablation (ClinicIMPPACT Trial).

OBJECTIVES: To evaluate the accuracy and clinical integrability of a comprehensive simulation tool to plan and predict radiofrequency ablation (RFA) zones in liver tumors. METHODS: Forty-five patients with 51 malignant hepatic lesions of different origins were included in a prospective multicenter trial. Prior to CT-guided RFA, all patients underwent multiphase CT which included acquisitions for the assessment of liver perfusion. These data were used to generate a 3D model of the liver. The intra-procedural position of the RFA probe was determined by CT and semi-automatically registered to the 3D model. Size and shape of the simulated ablation zones were compared with those of the thermal ablation zones segmented in contrast-enhanced CT images 1 month after RFA; procedure time was compared with a historical control group. RESULTS: Simulated and segmented ablation zone volumes showed a significant correlation (ρ = 0.59, p < 0.0001) and no significant bias (Wilcoxon's Z = 0.68, p = 0.25). Representative measures of ablation zone comparison were as follows: average surface deviation (absolute average error, AAE) with 3.4 ± 1.7 mm, Dice similarity coefficient 0.62 ± 0.14, sensitivity 0.70 ± 0.21, and positive predictive value 0.66 ± 0. There was a moderate positive correlation between AAE and duration of the ablation (∆t; r = 0.37, p = 0.008). After adjustments for inter-individual differences in ∆t, liver perfusion, and prior transarterial chemoembolization procedures, ∆t was an independent predictor of AAE (ß = 0.03 mm/min, p = 0.01). Compared with a historical control group, the simulation added 3.5 ± 1.9 min to the procedure. CONCLUSION: The validated simulation tool showed acceptable speed and accuracy in predicting the size and shape of hepatic RFA ablation zones. Further randomized controlled trials are needed to evaluate to what extent this tool might improve patient outcomes. KEY POINTS: • More reliable, patient-specific intra-procedural estimation of the induced RFA ablation zones in the liver may lead to better planning of the safety margins around tumors. • Dedicated real-time simulation software to predict RFA-induced ablation zones in patients with liver malignancies has shown acceptable agreement with the follow-up results in a first prospective multicenter trial suggesting a randomized controlled clinical trial to evaluate potential outcome benefit for patients.

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.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT).

INTRODUCTION: Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available. OBJECTIVES: ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction. DISCUSSION: This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

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.