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.

Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks.

Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.

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.

Correction to: Ultrasound thermal monitoring with an external ultrasound source for customized bipolar RF ablation shapes.

The original article has been published without article note.

RF-ablation pattern shaping employing switching channels of dual bipolar needle electrodes: ex vivo results.

PURPOSE: Radiofrequency (RF) ablation with mono- or bipolar electrodes is a common procedure for hepatocellular carcinoma (HCC) with a low rate of recurrence for small size tumors. For larger lesions and/or non-round/ellipsoid shapes RF ablation has some limitations and generally does not achieve comparable success rates to microwave ablation or high-intensity focused ultrasound therapies. MATERIALS AND METHODS: To shape RF ablations for matching a tumor size and geometry, we have developed an electronic channel switch box for two bipolar needles that generates multiple selectable ablation patterns. The setup can be used with commercially available mono- or bipolar RF generators. The switch box provides ten selectable ablation procedures to generate different ablation patterns without a relocation of a needle. Five patterns were exemplary generated in ex vivo tissue of porcine liver and chicken breast and visually characterized. RESULTS: Different ablation patterns, e.g., in a L- or U-shape, were achieved. In chicken breast a maximum ablation with a diameter of [Formula: see text] was obtained and in porcine liver [Formula: see text] with electrodes of [Formula: see text] length. CONCLUSION: The resulting ablations with the electronic switch box and two bipolar needles show the potential ability to manage RF therapies of complex and large tumor geometries. Next steps would be to validate the actual tissue ablation volumes in further ex vivo and preclinical studies and against simulation results.

Correction to: RF-ablation pattern shaping employing switching channels of dual bipolar needle electrodes: ex vivo results.

The original version of this article was published without funding note. The funding note is given below.

Comprehensive preclinical evaluation of a multi-physics model of liver tumor radiofrequency ablation.

PURPOSE: We aim at developing a framework for the validation of a subject-specific multi-physics model of liver tumor radiofrequency ablation (RFA). METHODS: The RFA computation becomes subject specific after several levels of personalization: geometrical and biophysical (hemodynamics, heat transfer and an extended cellular necrosis model). We present a comprehensive experimental setup combining multimodal, pre- and postoperative anatomical and functional images, as well as the interventional monitoring of intra-operative signals: the temperature and delivered power. RESULTS: To exploit this dataset, an efficient processing pipeline is introduced, which copes with image noise, variable resolution and anisotropy. The validation study includes twelve ablations from five healthy pig livers: a mean point-to-mesh error between predicted and actual ablation extent of 5.3 ± 3.6 mm is achieved. CONCLUSION: This enables an end-to-end preclinical validation framework that considers the available dataset.

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

Radiofrequency ablation (RFA) is an established treatment for liver cancer when resection is not possible. Yet, its optimal delivery is challenged by the presence of large blood vessels and the time-varying thermal conductivity of biological tissue. Incomplete treatment and an increased risk of recurrence are therefore common. A tool that would enable the accurate planning of RFA is hence necessary. This manuscript describes a new method to compute the extent of ablation required based on the Lattice Boltzmann Method (LBM) and patient-specific, pre-operative images. A detailed anatomical model of the liver is obtained from volumetric images. Then a computational model of heat diffusion, cellular necrosis, and blood flow through the vessels and liver is employed to compute the extent of ablated tissue given the probe location, ablation duration and biological parameters. The model was verified against an analytical solution, showing good fidelity. We also evaluated the predictive power of the proposed framework on ten patients who underwent RFA, for whom pre- and post-operative images were available. Comparisons between the computed ablation extent and ground truth, as observed in postoperative images, were promising (DICE index: 42%, sensitivity: 67%, positive predictive value: 38%). The importance of considering liver perfusion while simulating electrical-heating ablation was also highlighted. Implemented on graphics processing units (GPU), our method simulates 1 minute of ablation in 1.14 minutes, allowing near real-time computation.

Lattice Boltzmann method for fast patient-specific simulation of liver tumor ablation from CT images.

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 60x faster than standard finite element method.