Chloé Audigier

Parameter Estimation for Personalization of Liver Tumor Radiofrequency Ablation

Mathematical modeling has the potential to assist radiofrequency ablation (RFA) of tumors as it enables prediction of the extent of ablation. However, the accuracy of the simulation is challenged by the material properties since they are patient-specific, temperature and space dependent. In this paper, we present a framework for patient-specific radiofrequency ablation modeling of multiple lesions in the case of metastatic diseases. The proposed forward model is based upon a computational model of heat diffusion, cellular necrosis and blood flow through vessels and liver which relies on patient images. We estimate the most sensitive material parameters, those need to be personalized from the available clinical imaging and data. The selected parameters are then estimated using inverse modeling such that the point-to-mesh distance between the computed necrotic area and observed lesions is minimized. Based on the personalized parameters, the ablation of the remaining lesions are predicted. The framework is applied to a dataset of seven lesions from three patients including pre- and post-operative CT images. In each case, the parameters were estimated on one tumor and RFA is simulated on the other tumor(s) using these personalized parameters, assuming the parameters to be spatially invariant within the same patient. Results showed significantly good correlation between predicted and actual ablation extent (average point-to-mesh errors of 4.03 mm).

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.

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

PurposeRadiofrequency (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 methodsTo 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.ResultsDifferent ablation patterns, e.g., in a L- or U-shape, were achieved. In chicken breast a maximum ablation with a diameter of

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

4.3\, \hbox {cm}

CUST: CNN for Ultrasound Thermal Image Reconstruction Using Sparse Time-of-Flight Information

4.3cm was obtained and in porcine liver

Speed of sound estimation for thermal monitoring using an active ultrasound element during liver ablation therapy (Conference Presentation)

2.8\, \hbox {cm}