Real-time conductivity imaging of temperature and tissue property changes during radiofrequency ablation: An ex vivo model using weighted frequency difference

3-D Ultrafast Shear Wave Absolute Vibro-Elastography Using a Matrix Array Transducer

Real-time ultrasound imaging plays an important role in ultrasound-guided interventions. The 3-D imaging provides more spatial information compared to conventional 2-D frames by considering the volumes of data. One of the main bottlenecks of 3-D imaging is the long data acquisition time, which reduces practicality and can introduce artifacts from unwanted patient or sonographer motion. This article introduces the first shear wave absolute vibro-elastography (S-WAVE) method with real-time volumetric acquisition using a matrix array transducer. In S-WAVE, an external vibration source generates mechanical vibrations inside the tissue. The tissue motion is then estimated and used in solving a wave equation inverse problem to provide the tissue elasticity. A matrix array transducer is used with a Verasonics ultrasound machine and a frame rate of 2000 volumes/s to acquire 100 radio frequency (RF) volumes in 0.05 s. Using plane wave (PW) and compounded diverging wave (CDW) imaging methods, we estimate axial, lateral, and elevational displacements over 3-D volumes. The curl of the displacements is used with local frequency estimation to estimate elasticity in the acquired volumes. Ultrafast acquisition extends substantially the possible S-WAVE excitation frequency range, now up to 800 Hz, enabling new tissue modeling and characterization. The method was validated on three homogeneous liver fibrosis phantoms and on four different inclusions within a heterogeneous phantom. The homogeneous phantom results show less than 8% (PW) and 5% (CDW) difference between the manufacturer values and the corresponding estimated values over a frequency range of 80-800 Hz. The estimated elasticity values for the heterogeneous phantom at 400-Hz excitation frequency show the average errors of 9% (PW) and 6% (CDW) compared to the provided average values by magnetic resonance elastography (MRE). Furthermore, both imaging methods were able to detect the inclusions within the elasticity volumes. An ex vivo study on a bovine liver sample shows less than 11% (PW) and 9% (CDW) difference between the estimated elasticity ranges by the proposed method and the elasticity ranges provided by MRE and acoustic radiation force impulse (ARFI).

Integrated, Transparent Silicon Carbide Electronics and Sensors for Radio Frequency Biomedical Therapy

The integration of micro- and nanoelectronics into or onto biomedical devices can facilitate advanced diagnostics and treatments of digestive disorders, cardiovascular diseases, and cancers. Recent developments in gastrointestinal endoscopy and balloon catheter technologies introduce promising paths for minimally invasive surgeries to treat these diseases. However, current therapeutic endoscopy systems fail to meet requirements in multifunctionality, biocompatibility, and safety, particularly when integrated with bioelectronic devices. Here, we report materials, device designs, and assembly schemes for transparent and stable cubic silicon carbide (3C-SiC)-based bioelectronic systems that facilitate tissue ablation, with the capability for integration onto the tips of endoscopes. The excellent optical transparency of SiC-on-glass (SoG) allows for direct observation of areas of interest, with superior electronic functionalities that enable multiple biological sensing and stimulation capabilities to assist in electrical-based ablation procedures. Experimental studies on phantom, vegetable, and animal tissues demonstrated relatively short treatment times and low electric field required for effective lesion removal using our SoG bioelectronic system. In vivo experiments on an animal model were conducted to explore the versatility of SoG electrodes for peripheral nerve stimulation, showing an exciting possibility for the therapy of neural disorders through electrical excitation. The multifunctional features of SoG integrated devices indicate their high potential for minimally invasive, cost-effective, and outcome-enhanced surgical tools, across a wide range of biomedical applications.

AC measurements and simulations of hepatic radiofrequency ablation

INTRODUCTION

The radiofrequency ablation (RFA) of liver cancer is a desirable treatment option, as it is minimally invasive. An accurate numerical simulation can greatly help physicians better plan their surgical protocols. Previously, the displacement current in the RFA process was considered negligible, and therefore RFA simulation was modeled as a direct current (DC) system instead of an alternating current (AC) system. Our study investigated the hypothesis that the displacement current in the RFA process should not always be considered negligible.

METHODS

AC measurements of ex vivo bovine liver ablation were performed, and numerical simulations were also conducted to test the hypothesis that the relative permittivity would significantly decrease after the liver tissue reached a high temperature.

RESULTS

The displacement current was observed to be a sizable fraction of the conduction current, especially before the onset of the first pause. The simulation results indicated that the relative permittivity is likely to decrease to several hundred or lower at elevated temperatures.

CONCLUSIONS

Our study results suggest that the DC model may be inadequate, especially before the first roll-off and that additional information could be available during RFA treatment by considering the AC nature of RFA, which could lead to improved numerical simulation. Additional measurements of tissue parameters are needed to reach the full potential of the AC model for further development of ablation control.

Electrical Impedance Tomography for monitoring cardiac radiofrequency ablation: a scoping review of an emerging technology

Arrhythmias are common cardiac diseases which can be treated effectively by the cardiac radiofrequency ablation (CRFA). However, information regarding the lesion growth within the myocardium is critical to the procedure's safety and efficacy but still unavailable in the current catheterisation lab (CathLab). Over the last 20 years, many efforts have been made in order to track the lesion size during the procedure. Unfortunately, all the approaches have their own limitations preventing them from the clinical translation and hence making the lesion size monitoring during a CRFA still an open issue. Electrical Impedance Tomography (EIT) is an impedance imaging modality that might be able to image the thermal-related impedance changes from which the lesion size can be measured. With the availability of the patient's CT scans, for a detailed model, and the catheter-based electrodes for the internal electrodes, EIT accuracy and sensitivity to the ablated sites can be significantly improved and is worth being explored for this application. Though EIT is still new to CRFA with no in-vivo experiments being done according to our up-to-date searching, many related EIT studies and its extensive research in Hyperthermia and other ablations can reveal many hints for a possibility of the CRFA-EIT application. In this paper, we present a review on multiple aspects of EIT in CRFA. First, the expected CRFA-EIT signal range and frequency are discussed based on various measured impedance results obtained from lesions in the past. Second, the possible noise sources that can happen in a clinical CRFA procedure, along with their signal range and frequency compared to the CRFA-EIT signal, and, third, the available current solutions to separate such noises from the CRFA-EIT signal. Finally, we review the progress of EIT in thermal applications over the last two decades in order to identify the developments that EIT can take advantage of and the current drawbacks that need to be solved for a potential CRFA-EIT application.

Radiofrequency ablation with four electrodes as a building block for matrix radiofrequency ablation: Ex vivo liver experiments and finite element method modelling. Influence of electric and activation mode on coagulation size and geometry

PURPOSE

Radiofrequency ablation (RFA) is increasingly being used to treat unresectable liver tumors. Complete ablation of the tumor and a safety margin is necessary to prevent local recurrence. With current electrodes, size and shape of the ablation zone are highly variable leading to unsatisfactory local recurrence rates, especially for tumors >3 cm. In order to improve predictability, we recently developed a system with four simple electrodes with complete ablation in between the electrodes. This rather small but reliable ablation zone is considered as a building block for matrix radiofrequency ablation (MRFA). In the current study we explored the influence of the electric mode (monopolar or bipolar) and the activation mode (consecutive, simultaneous or switching) on the size and geometry of the ablation zone.

MATERIALS AND METHODS

The four electrode system was applied in ex vivo bovine liver. The electric and the activation mode were changed one by one, using constant power of 50 W in all experiments. Size and geometry of the ablation zone were measured. Finite element method (FEM) modelling of the experiment was performed.

RESULTS

In ex vivo liver, a complete and predictable coagulation zone of a 3 × 2 × 2 cm block was obtained most efficiently in the bipolar simultaneous mode due to the combination of the higher heating efficacy of the bipolar mode and the lower impedance by the simultaneous activation of four electrodes, as supported by the FEM simulation.

CONCLUSIONS

In ex vivo liver, the four electrode system used in a bipolar simultaneous mode offers the best perspectives as building block for MRFA. These results should be confirmed by in vivo experiments.

Cardiac radiofrequency ablation tracking using electrical impedance tomography

There is a need for accessible high speed imaging of Radiofrequency (RF) cardiac electrosurgery to improve safety and efficacy of the ablation time course, where lesion information is critical to safety and efficacy but currently lacking in real time. In this paper, Electrical Impedance Tomography (EIT) using existing cardiac EP electrodes was optimised to confirm (1) that removal of measurements with low signal sensitivity leads to improved images and (2) that multiple signal thresholds are needed to track the lesion accurately over time. A novel ventricle-shaped gel phantom with realistic fluid flow to mimic blood flow, lung ventilation and myocardium conductivity was developed to study the capability and motivate transition to in-vivo measurements. When using 8 external (ECG) electrodes, 4 internal coronary sinus electrodes and 4 RF catheter-based electrodes, the optimal setup for sensitivity and dynamic tracking was 77 measurements within an error of 20%. Higher thresholds were more suitable for the earlier phase of the ablation when lesions are small while lower thresholds suited later phases. Patient-specific thresholds could be optimised in pre-surgical planning where detailed anatomical images are available. While the error reported in this initial study appears large, it is a major advance over the current situation for the cardiologist where no real-time lesion visualization is accessible in a regular EP suite/cath lab.

Early and Late Fusion Machine Learning on Multi-Frequency Electrical Impedance Data to Improve Radiofrequency Ablation Monitoring

Radiofrequency ablation (RFA) is a popular modality for tumor treatment. However, inexpensive real-time monitoring of RFA within multiple tissue types is still an ongoing research topic. The objective of this study is to utilize multi-frequency electrical impedance data within real-time RFA depth estimation through data fusion schemes that include non-linear machine learning (ML) models. Multi-frequency tissue complex electrical impedance measurements are used to provide input data to the data fusion schemes. Our results show that the fusion schemes significantly decrease both the spread of residuals and the mean of the residuals for depth estimation. Thus, data fusion can be a significant tool for use in improving the performance of ML-based monitoring for RFA.

Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles

OBJECTIVE

Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance.

METHODS

Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression.

RESULTS

Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models. The root mean square errors were 2 mm for NN and 0.5 mm for TEs for previous statistical models and the root mean square errors were 0.4 mm for NN and 0.04 mm for TEs for the statistical models presented in this paper. Simulation ablation performance showed a mean difference against physical measurements of 0.5 ±0.2 mm for the NN-based depth estimation method and 0.7 ±0.4 mm for the TE-based depth estimation method.

CONCLUSION

The results show that multi-frequency data significantly improves the depth estimation performance of the statistical models.

SIGNIFICANCE

The RFA lesion depth estimation methods presented in this work achieve millimeter-resolution accuracy with soft real-time performance on an ARMv7-based embedded system for potential translation to clinical RFA technologies.

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

BACKGROUND

Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz-800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time monitoring the ablation process is a must in order to maintain the reliability of the treatment. Common methods for this monitoring task have proven to be accurate, however, they are all time-consuming or require expensive equipment, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure.

METHODS

A machine learning (ML) approach is presented that aims to reduce the monitoring time while keeping the accuracy of the conventional methods. Two different hardware setups are used to perform the ablation and collect impedance data at the same time and different ML algorithms are tested to predict the ablation depth in 3 dimensions, based on the collected data.

RESULTS

Both the random forest and adaptive boosting (adaboost) models had over 98% R² on the data collected with the embedded system-based hardware instrumentation setup, outperforming Neural Network-based models.

CONCLUSIONS

It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a ×86-64 workstation.

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network

BACKGROUND

Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes.

METHODS

Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue.

RESULTS

The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor.

CONCLUSIONS

The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

Radiofrequency Thermal Ablation Heat Energy Transfer in an Ex-Vivo Model

Little work has been done to consider the temperature changes and energy transfer that occur in the tissue outside the vein with ultrasound-guided vein ablation therapy. In this experiment, a ex-vivo model of the human calf was used to analyze heat transfer and energy degradation in tissue surrounding the vein during endovascular radiofrequency ablation (RFA). A clinical vein ablation protocol was used to determine the tissue temperature distribution in 10 per cent agar gel. Heat energy from the radiofrequency catheter was measured for 140 seconds at fixed points by four thermometer probes placed equidistant radially at 0.0025, 0.005, and 0.01 m away from the RFA catheter. The temperature rose 1.5°C at 0.0025 m, 0.6°C at 0.005 m, and 0.0°C at 0.01 m from the RFA catheter. There was a clinically insignificant heat transfer at the distances evaluated, 1.4 ± 0.2 J/s at 0.0025 m, 0.7 ± 0.3 J/s at 0.0050 m, and 0.3 ± 0.0 J/s at 0.01 m. Heat degradation occurred rapidly: 4.5 ± 0.5 J (at 0.0025 m), 4.0 ± 1.6 J (at 0.0050 m), and 3.9 ± 3.6 J (at 0.01 m). Tumescent anesthesia injected one centimeter around the vein would act as a heat sink to absorb the energy transferred outside the vein to minimize tissue and nerve damage and will help phlebologists strategize options for minimizing damage.

Determination of the electrical conductivity of human liver metastases: impact on therapy planning in the radiofrequency ablation of liver tumors

Background Radiofrequency ablation is used to induce thermal necrosis in the treatment of liver metastases. The specific electrical conductivity of a liver metastasis has a distinct influence on the heat formation and resulting tumor ablation within the tissue. Purpose To examine the electrical conductivity σ of human colorectal liver metastases and of tumor-free liver tissue in surgical specimens. Material and Methods Surgical specimens from patients with resectable colorectal liver metastases were used for measurements (size of metastases <30 mm). A four-needle measuring probe was used to determine the electrical conductivity σ of human colorectal liver metastasis (n = 8) and tumor-free liver tissue (n = 5) in a total of five patients. All measurements were performed at 470 kHz, which is the relevant frequency for radiofrequency ablation. The tissue temperature was also measured. Hepatic resections were performed in accordance with common surgical standards. Measurements were performed in the operating theater immediately after resection. Results The median electrical conductivity σ was 0.57 S/m in human colorectal liver metastases at a median temperature of 35.1℃ and 0.35 S/m in tumor-free liver tissue at a median temperature of 34.9℃. The electrical conductivity was significantly higher in tumor tissue than in tumor-free liver tissue ( P = 0.005). There were no differences in tissue temperature between the two groups ( P = 0.883). Conclusion The electrical conductivity is significantly higher in human colorectal liver metastases than in tumor-free liver tissue at a frequency of 470 kHz.

Three-dimensional imaging technologies: a priority for the advancement of tissue engineering and a challenge for the imaging community

Tissue engineering/regenerative medicine (TERM) is an interdisciplinary field that applies the principle of engineering and life sciences to restore/replace damaged tissues/organs with in vitro artificially-created ones. Research on TERM quickly moves forward. Today newest technologies and discoveries, such as 3D-/bio-printing, allow in vitro fabrication of ex-novo made tissues/organs, opening the door to wide and probably never-ending application possibilities, from organ transplant to drug discovery, high content screening and replacement of laboratory animals. Imaging techniques are fundamental tools for the characterization of tissue engineering (TE) products at any stage, from biomaterial/scaffold to construct/organ analysis. Indeed, tissue engineers need versatile imaging methods capable of monitoring not only morphological but also functional and molecular features, allowing three-dimensional (3D) and time-lapse in vivo analysis, in a non-destructive, quantitative, multidimensional analysis of TE constructs, to analyze their pre-implantation quality assessment and their fate after implantation. This review focuses on the newest developments in imaging technologies and applications in the context of requirements of the different steps of the TERM field, describing strengths and weaknesses of the current imaging approaches.