Simulation of temperature field for temperature-controlled radio frequency ablation using a hyperbolic bioheat equation and temperature-varied voltage calibration: a liver-mimicking phantom study

Predicting spatio-temporal radiofrequency ablation temperature using deep neural networks

Radiofrequency ablation (RFA) of the medial branch nerve is a widely used therapeutic intervention for facet joint pain. However, denervation of the multifidus muscle is an inevitable consequence of RFA. New ablation techniques with the potential to prevent muscle denervation can be designed using computational simulations. However, depending on the complexity of the model, they could be computationally expensive. As an alternative approach, deep neural networks (DNNs) can be used to predict tissue temperature during RFA procedure. The objective of this paper is to predict the tissue spatial and temporal temperature distributions during RFA using DNNs. First, finite element (FE) models with a range of distances between the probes were run to obtain the temperature readings. The measured temperatures were then used to train the DNNs that predict the spatio-temporal temperature distribution within the tissue. Finally, a separate data obtained from FE simulations were used to test the efficacy of the network. The results presented in this paper demonstrate that the network can achieve an error rate as low as 0.05%, accompanied by a 92% reduction in time compared to FE simulations. The approach proposed in this study will play a major role in the design of new RFA treatments for facet joint pain.

Robotic laser position versus freehand in CT-guided percutaneous microwave ablation for single hepatocellular carcinoma (diameter < 3 cm): a preliminary study

PURPOSE

To compare the accuracy and safety of robotic laser position (RLP) versus freehand for antenna CT-guided microwave ablation (MWA) of single hepatocellular carcinoma (HCC) (diameter < 3 cm).

MATERIALS AND METHODS

This retrospective study was conducted between May 2020 and June 2021. A total of 40 patients with early HCC who underwent CT-guided MWA were divided into two groups: a freehand group (n = 20) and a RLP group (n = 20). Based on in-plane and out-of-plane data, the actual puncture point error (APPE), number of repositioning procedures, and operative duration were compared using the Mann-Whitney U test. Ablation-related complications were compared using the Chi-squared test.

RESULTS

The mean diameter of HCC patients who received MWA was 2.4 ± 0.5 cm. For in-plane APPE, APPE was comparable between the two groups (p = 0.299). However, for the out-of-plane position, the APPE in the freehand group was higher than that in the RLP group (p = 0.027). The number of repositioning procedures was 0 (range, 0-0) for RLP-guided procedures and 3 (range, 2-5) for freehand procedures, showing a statistically significant difference between the two groups (p < 0.001). The mean operative duration for freehand procedures was 39 min, compared with 26 min for RLP-guided procedures, showing a significant difference (p = 0.013). No deaths or major complications were directly related to MWA. Minor complications in the freehand group were comparable with those in the RLP group (p = 0.313).

CONCLUSION

RLP guidance significantly reduces the number of antenna repositioning procedures in MWA and improves puncture accuracy for target HCC out-of-plane. In addition, the operative duration of robotic guidance was shorter than that of freehand guidance.

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.

Optimization of an Endoscopic Radiofrequency Ablation Electrode

Radiofrequency ablation (RFA) is an increasingly used, minimally invasive, cancer treatment modality for patients who are unwilling or unable to undergo a major resective surgery. There is a need for RFA electrodes that generate thermal ablation zones that closely match the geometry of typical tumors, especially for endoscopic ultrasound-guided (EUS) RFA. In this paper, the procedure for optimization of an RFA electrode is presented. First, a novel compliant electrode design is proposed. Next, a thermal ablation model is developed to predict the ablation zone produced by an RFA electrode in biological tissue. Then, a multi-objective genetic algorithm is used to optimize two cases of the electrode geometry to match the region of destructed tissue to a spherical tumor of a specified diameter. This optimization procedure is then applied to EUS-RFA ablation of pancreatic tissue. For a target 2.5 cm spherical tumor, the optimal design parameters of the compliant electrode design are found for two cases. Cases 1 and 2 optimal solutions filled 70.9% and 87.0% of the target volume as compared to only 25.1% for a standard straight electrode. The results of the optimization demonstrate how computational models combined with optimization can be used for systematic design of ablation electrodes. The optimization procedure may be applied to RFA of various tissue types for systematic design of electrodes for a specific target shape.