Ex Vivo and In Vivo Monitoring and Characterization of Thermal Lesions by High-Intensity Focused Ultrasound and Microwave Ablation Using Ultrasonic Nakagami Imaging

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

Multiple ultrasonic parametric imaging for the detection and monitoring of high-intensity focused ultrasound ablation

Numerous quantitative ultrasound imaging techniques have demonstrated superior monitoring performance for thermal ablation when compared to conventional ultrasonic B-mode imaging. However, the absence of comparative studies involving various quantitative ultrasound imaging techniques hinders further clinical exploration. In this study, we simultaneously reconstructed ultrasonic Nakagami imaging, ultrasonic horizontally normalized Shannon entropy (hNSE) imaging, and ultrasonic differential attenuation coefficient intercept (DACI) imaging from ultrasound backscattered envelope data collected during high-intensity focused ultrasound ablation treatment. We comprehensively investigated their performance differences through qualitative and quantitative analyses, including the calculation of contrast-to-noise ratios (CNR) for ultrasonic images, receiver operating characteristic (ROC) analysis with corresponding indicators, the analysis of lesion area fitting relationships, and computational time consumption comparison. The mean CNR of hNSE imaging was 10.98 ± 4.48 dB, significantly surpassing the 3.82 ± 1.40 dB (p < 0.001, statistically significant) of Nakagami imaging and the 2.45 ± 0.74 dB (p < 0.001, statistically significant) of DACI imaging. This substantial difference underscores that hNSE imaging offers the highest contrast resolution for lesion recognition. Furthermore, we evaluated the ability of multiple ultrasonic parametric imaging to detect thermal ablation lesions using ROC curves. The area under the curve (AUC) for hNSE was 0.874, exceeding the values of 0.848 for Nakagami imaging and 0.832 for DACI imaging. Additionally, hNSE imaging exhibited the strongest linear correlation coefficient (R = 0.92) in the comparison of lesion area fitting, outperforming Nakagami imaging (R = 0.87) and DACI imaging (R = 0.85). hNSE imaging also performs best in real-time monitoring with each frame taking 6.38 s among multiple ultrasonic parametric imaging. Our findings unequivocally demonstrate that hNSE imaging excels in monitoring HIFU ablation treatment and holds the greatest potential for further clinical exploration.

Artificial intelligence-assisted ultrasound-guided focused ultrasound therapy: a feasibility study

OBJECTIVES

Focused ultrasound (FUS) therapy has emerged as a promising noninvasive solution for tumor ablation. Accurate monitoring and guidance of ultrasound energy is crucial for effective FUS treatment. Although ultrasound (US) imaging is a well-suited modality for FUS monitoring, US-guided FUS (USgFUS) faces challenges in achieving precise monitoring, leading to unpredictable ablation shapes and a lack of quantitative monitoring. The demand for precise FUS monitoring heightens when complete tumor ablation involves controlling multiple sonication procedures.

METHODS

To address these challenges, we propose an artificial intelligence (AI)-assisted USgFUS framework, incorporating an AI segmentation model with B-mode ultrasound imaging. This method labels the ablated regions distinguished by the hyperechogenicity effect, potentially bolstering FUS guidance. We evaluated our proposed method using the Swin-Unet AI architecture, conducting experiments with a USgFUS setup on chicken breast tissue.

RESULTS

Our results showed a 93% accuracy in identifying ablated areas marked by the hyperechogenicity effect in B-mode imaging.

CONCLUSION

Our findings suggest that AI-assisted ultrasound monitoring can significantly improve the precision and control of FUS treatments, suggesting a crucial advancement toward the development of more effective FUS treatment strategies.

Temperature Monitoring During Microwave Hyperthermia Based on BP-Nakagami Distribution

OBJECTIVE

The purpose of this study is to accurately monitor temperature during microwave hyperthermia. We propose a temperature estimation model BP-Nakagami based on neural network for Nakagami distribution.

METHODS

In this work, we designed the microwave hyperthermia experiment of fresh ex vivo pork tissue and phantom, collected ultrasonic backscatter data at different temperatures, modeled these data using Nakagami distribution, and calculated Nakagami distribution parameter m. A neural network model was built to train the relationship between Nakagami distribution parameter m and temperature, and a BP-Nakagami temperature model with good fitting was obtained. The temperature model is used to draw the two-dimensional temperature distribution map of biological tissues in microwave hyperthermia. Finally, the temperature estimated by the model is compared with the temperature measured by thermocouples.

RESULTS

The error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 1°C in the range of 25°C-50°C for ex vivo pork tissue, and the error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 0.5°C in the range of 25°C-50°C for phantom.

CONCLUSIONS

The results show that the temperature estimation model proposed by us is an effective model for monitoring the internal temperature change of biological tissues.

Ultrasound image segmentation using Gamma combined with Bayesian model for focused-ultrasound-surgery lesion recognition

This study aims to investigate the feasibility of combined segmentation for the separation of lesions from non-ablated regions, which allows surgeons to easily distinguish, measure, and evaluate the lesion area, thereby improving the quality of high-intensity focused-ultrasound (HIFU) surgery used for the non-invasive tumor treatment. Given that the flexible shape of the Gamma mixture model (GΓMM) fits the complex statistical distribution of samples, a method combining the GΓMM and Bayes framework is constructed for the classification of samples to obtain the segmentation result. An appropriate normalization range and parameters can be used to rapidly obtain a good performance of GΓMM segmentation. The performance values of the proposed method under four metrics (Dice score: 85%, Jaccard coefficient: 75%, recall: 86%, and accuracy: 96%) are better than those of conventional approaches including Otsu and Region growing. Furthermore, the statistical result of sample intensity indicates that the finding of the GΓMM is similar to that obtained by the manual method. These results indicate the stability and reliability of the GΓMM combined with the Bayes framework for the segmentation of HIFU lesions in ultrasound images. The experimental results show the possibility of combining the GΓMM with the Bayes framework to segment lesion areas and evaluate the effect of therapeutic ultrasound.

Real-time monitoring of focused ultrasound therapy using intelligence-based thermography: A feasibility study

Focused ultrasound (FUS) therapy has been widely studied for breast cancer treatment due to its potential as a fully non-invasive method to improve cosmetic and oncologic results. However, real-time imaging and monitoring of the therapeutic ultrasound delivered to the target area remain challenges for precision breast cancer therapy. The main objective of this study is to propose and evaluate a novel intelligence-based thermography (IT) method that can monitor and control FUS treatment using thermal imaging with the fusion of artificial intelligence (AI) and advanced heat transfer modeling. In the proposed method, a thermal camera is integrated into FUS system for thermal imaging of the breast surface, and an AI model is employed for the inverse analysis of the surface thermal monitoring, thereby estimating the features of the focal region. This paper presents experimental and computational studies conducted to assess the feasibility and efficiency of IT-guided FUS (ITgFUS). Tissue phantoms, designed to mimic the properties of breast tissue, were used in the experiments to investigate detectability and the impact of temperature rise at the focal region on the tissue surface. Additionally, an AI computational analysis employing an artificial neural network (ANN) and FUS simulation was carried out to provide a quantitative estimation of the temperature rise at the focal region. This estimation was based on the observed temperature profile on the breast model's surface. The results proved that the effects of temperature rise at the focused area could be detected by the thermal images acquired with thermography. Moreover, it was demonstrated that the AI analysis of the surface temperature measurement could result in near real-time monitoring of FUS by quantitative estimation of the temporal and spatial temperature rise profiles at the focal region.

Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones

The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the α and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK α_cws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.

Monitoring of thermal lesions in ultrasound using fully convolutional neural networks: A preclinical study

Accurate monitoring of thermal ablation regions is an important guarantee for successful ablation treatment, which mainly depends on the subjective judgment of radiologists in current clinical practice. This work innovatively applied fully convolutional neural networks (FCNs) for detection and monitoring of thermal ablation regions in ultrasound (US) and comprehensively compared the performance of VGG16-FCN, U-Net, UNet++, Attention U-Net, MultiResUNet, and ResUNet, which have shown outstanding performance in medical image segmentation. The input of the models was US echo envelope data backscattered from the ablated regions. Excised porcine liver ablation dataset and clinical liver tumors ablation dataset were respectively used to evaluate the prediction ability of the models. With 1000 excised porcine liver ablation samples for training and 200 samples for testing, the UNet++ achieves both the highest Dice score (DSC) of 0.7824 ± 0.1098 and the best Hausdorff distance (HD) of 2.70 ± 1.38 mm. Additionally, considering potential clinical usage, we also tested the model generalizability by training on the excised dataset and testing on the clinical data, in which we obtained the performance with the highest DSC obtained by the ResUNet and the best HD by the UNet++. Our comparative study suggests that both UNet++ and ResUNet have relatively outstanding segmentation performance among all compared models, which are potential candidates for automatic segmentation of thermal ablation regions in US during clinical ablation treatment.

A Review of Quantitative Ultrasound-Based Approaches to Thermometry and Ablation Zone Identification Over the Past Decade

Percutaneous thermal therapy is an important clinical treatment method for some solid tumors. It is critical to use effective image visualization techniques to monitor the therapy process in real time because precise control of the therapeutic zone directly affects the prognosis of tumor treatment. Ultrasound is used in thermal therapy monitoring because of its real-time, non-invasive, non-ionizing radiation, and low-cost characteristics. This paper presents a review of nine quantitative ultrasound-based methods for thermal therapy monitoring and their advances over the last decade since 2011. These methods were analyzed and compared with respect to two applications: ultrasonic thermometry and ablation zone identification. The advantages and limitations of these methods were compared and discussed, and future developments were suggested.

Ultrasound Entropy Imaging for Detection and Monitoring of Thermal Lesion During Microwave Ablation of Liver

Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0.895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0.90 against ground truth, AUROC of 0.868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5.86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.

Methods of monitoring thermal ablation of soft tissue tumors - A comprehensive review

Thermal ablation is a form of hyperthermia in which oncologic control can be achieved by briefly inducing elevated temperatures, typically in the range 50-80°C, within a target tissue. Ablation modalities include high intensity focused ultrasound, radiofrequency ablation, microwave ablation, and laser interstitial thermal therapy which are all capable of generating confined zones of tissue destruction, resulting in fewer complications than conventional cancer therapies. Oncologic control is contingent upon achieving predefined coagulation zones; therefore, intraoperative assessment of treatment progress is highly desirable. Consequently, there is a growing interest in the development of ablation monitoring modalities. The first section of this review presents the mechanism of action and common applications of the primary ablation modalities. The following section outlines the state-of-the-art in thermal dosimetry which includes interstitial thermal probes and radiologic imaging. Both the physical mechanism of measurement and clinical or pre-clinical performance are discussed for each ablation modality. Thermal dosimetry must be coupled with a thermal damage model as outlined in Section 4. These models estimate cell death based on temperature-time history and are inherently tissue specific. In the absence of a reliable thermal model, the utility of thermal monitoring is greatly reduced. The final section of this review paper covers technologies that have been developed to directly assess tissue conditions. These approaches include visualization of non-perfused tissue with contrast-enhanced imaging, assessment of tissue mechanical properties using ultrasound and magnetic resonance elastography, and finally interrogation of tissue optical properties with interstitial probes. In summary, monitoring thermal ablation is critical for consistent clinical success and many promising technologies are under development but an optimal solution has yet to achieve widespread adoption.

High contrast ultrasonic method with multi-spatiotemporal compounding for monitoring catheter-based ultrasound thermal therapy: Development and Ex Vivo Evaluations

OBJECTIVE

Changes in ultrasound backscatter energy (CBE) imaging can monitor thermal therapy. Catheter-based ultrasound (CBUS) can treat deep tumors with precise spatial control of energy deposition and ablation zones, of which CBE estimation can be limited by low contrast and robustness due to small or inconsistent changes in ultrasound data. This study develops a multi-spatiotemporal compounding CBE (MST-CBE) imaging approach for monitoring specific to CBUS thermal therapy.

METHODS

Ex vivo thermal ablations were performed with stereotactic positioning of a 180 directional CBUS applicator, temperature monitoring probes, endorectal US probe, and subsequent lesion sectioning and measurement. Five frames of raw radiofrequency data were acquired throughout in 15s intervals. Using window-by-window estimation methods, absolute and positive components of MST-CBE images at each point were obtained by the compounding ratio of squared envelope data within an increasing spatial size in each short-time window.

RESULTS

Compared with conventional US, Nakagami, and CBE imaging, the detection contrast and robustness quantified by tissue-modification-ratio improved by 37.24.7 (p<0.001), 37.55.2 (p<0.001), and 6.44.0 dB (p<0.05) in the MST-CBE imaging, respectively. Correlation coefficient and bias between cross-sectional dimensions of the ablation zones measured in tissue sections and estimated from MST-CBE were up to 0.91 (p<0.001) and -0.02 mm2, respectively.

CONCLUSION

The MST-CBE approach can monitor the detailed changes within target tissues and effectively characterize the dimensions of the ablation zone during CBUS energy deposition.

SIGNIFICANCE

The MST-CBE approach could be practical for improved accuracy and contrast of monitoring and evaluation for CBUS thermal therapy.

Monitoring microwave ablation using ultrasound homodyned K imaging based on the noise-assisted correlation algorithm: An ex vivo study

In this paper, we proposed ultrasound homodyned K (HK) imaging based on the noise-assisted correlation algorithm (NCA) for monitoring microwave ablation of porcine liver ex vivo. The NCA-based HK (α_NCA and k_NCA) imaging was compared with NCA-based Nakagami (m_NCA) imaging and NCA-based cumulative echo decorrelation (CED_NCA) imaging. Backscattered ultrasound radiofrequency signals of porcine liver ex vivo during and after the heating of microwave ablation were collected (n = 15), which were processed for constructing B-mode imaging, NCA-based HK imaging, NCA-based Nakagami imaging, and NCA-based CED imaging. To quantitatively evaluate the final coagulation zone, the polynomial approximation (PAX) technique was applied. The accuracy of detecting coagulation area with α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging was evaluated by comparing the PAX imaging with the gross pathology. The receiver operating characteristic (ROC) curve was used to further evaluate the performance of the three quantitative ultrasound imaging methods for detecting the coagulation zone. Experimental results showed that the average accuracies of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging combined with PAX imaging were 89.6%, 83.25%, 89.23%, and 91.6%, respectively. The average areas under the ROC curve (AUROCs) of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging were 0.83, 0.77, 0.83, and 0.86, respectively. The proposed NCA-based HK imaging may be used as a new method for monitoring microwave ablation.

Nakagami imaging with related distributions for advanced thermogram pseudocolorization

Pseudocoloring algorithms embedded in the software of thermal cameras gradually colorize original intensity thermograms generated by detecting temperatures and contrast. Maximum and minimum based algorithms, however, executed by thresholding, applied to intensity thermograms for revealing and coloring the outliers instead. Although the common pseudocoloring protocols employed for general purposes may provide crucial information on the superficial contrast between radiation emitted by various sources; their common kernel is not sufficient for detecting and differentiating high radiated regions from surrounding areas, which is mandatory for recognition of abnormalities. Therefore, we propose novel imaging methodology based on Nakagami and related distributions, including gamma, Rayleigh, Weibull, chi-square and exponential, for enhancing thermal images and also for creating adequate discrimination. We initially define the boundaries of tumor and surrounding area in a synthetically generated breast thermogram already diagnosed as retroareolar tumor. Using Nakagami and transformations supported by mathematical foundations, we conducted several experiments to find the discrimination factor of the pseudocoloring techniques by calculating difference of average contrast between the tumor and the surrounding area. The performance is greatly encouraging that we achieved considerably better discrimination factor, designated for this study, up to 106.80 compared to the results of existing built-in pseudocolorization methods computed as 11.56.

A review of conventional and newer generation microwave ablation systems for hepatocellular carcinoma

Although microwave ablation (MWA) exhibits a high thermal efficiency, the major limitation of conventional MWA systems is the lack of predictability of the ablation zone size and shape. Therefore, a specific newer generation MWA system, The Emprint™ Ablation System with Thermosphere™ Technology, was designed to create predictable large spherical zones of ablation that are not impacted by varying tissue environments. The time required for ablation with MWA systems is short, and the shape of the necrosis is elliptical with the older systems and spherical with the new system. In addition, because MWA has no heat-sink effect, it can be used to ablate tumors adjacent to major vessels. Although these factors yield a large ablation volume and result in good local control, excessive ablation of liver tissue and unexpected ablation of surrounding organs are possible. Therefore, MWA should be carefully performed. This review highlights the efficacy and complications of MWA performed with conventional systems and the newer generation system in patients with hepatocellular carcinoma (HCC). MWA with the newer generation system seems to be a promising treatment option for large HCCs and secondary hepatic malignancies, with several advantages over other available ablation techniques, including conventional MWA. However, further randomized controlled trials are necessary to fully clarify the benefits and pitfalls of this new system.

Ultrasound-Based Regularized Log Spectral Difference Method For Monitoring Microwave Hyperthermia

The feasibility of using normalized cumulative difference attenuation (NCDA) map for tracking the spatial and temporal evolution of temperature during microwave hyperthermia experiment on in-vitro phantoms is explored in this study. The NCDA maps were estimated from the beamformed ultrasound radio frequency (RF) data using a regularized log spectral difference (RLSD) technique. The NCDA maps were estimated at different time instants for the entire period of the experiment. The contour maps of the NCDA and the ground truth temperature map, obtained using an infra-red(IR) thermal camera corresponding to the ultrasound imaging plane, showed that NCDA was able to locate the axial and lateral co-ordinates of the hotspot with the error of <; 1.5 mm axially and <; 0.1 mm laterally. The error in the estimated hotspot area was less than 8 %. This preliminary in-vitro study suggests that NCDA maps estimated using RLSD may have potential in evaluating the spatio-temporal evolution of temperature and may help in the development of ultrasound-based image-guided temperature monitoring system for microwave hyperthermia.

Conformal coverage of liver tumors by the thermal coagulation zone in 2450-MHz microwave ablation

Purpose: To optimize treatment schemes using 2450-MHz microwave ablation (MWA), a novel conformal coverage method based on bipolar-angle mapping is proposed that determines whether a liver tumor is completely encompassed by thermal coagulation zones. Materials and methods: Firstly, three-dimensional (3-D) triangular mesh data of liver tumors were reconstructed from clinical computed tomography (CT) slices using the Marching cubes (MC) algorithm. Secondly, characterization models of thermal coagulation zones were established based on finite element simulation results of 40, 45, 50, 55, and 60 W ablations. Finally, coagulation zone models and tumor surface data were mapped and fused on a two-dimensional (2-D) plane to achieve conformal coverage of liver tumors by comparing the corresponding polar radii. Results: Optimal parameters for ablation treatment of liver tumors were efficiently obtained with the proposed conformal coverage method. Fifteen liver tumors were obtained with maximal diameters of 12.329-78.612 mm (mean ± standard deviation, 39.094 ± 19.447 mm). The insertion positions and orientations of the MWA antenna were determined based on 3-D reconstruction results of these tumors. The ablation patterns and durations of tumors were planned according to the minimum mean standard deviations between the ablative margin and tumor surface. Conclusion: The proposed method can be applied to computer-assisted MWA treatment planning of liver tumors, and is expected to guide clinical procedures in future.

Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution

PURPOSE

The Nakagami statistical model and Nakagami shape parameter m have been widely used in linear tissue characterization and preliminarily characterized the envelope distributions of nonlinear encapsulated microbubbles (EMBs). However, the Nakagami distribution of nonlinear scattering EMBs lacked a systematical investigation. Thus, this study aimed to investigate the Nakagami distribution of EMBs and illustrate the impact of EMBs' nonlinearity on the Nakagami model.

METHOD

A group of simulated EMB phantoms and in vitro EMB dilutions with an increasing concentration distribution under various EMB nonlinearities, as regulated by acoustic parameters, were characterized by using the window-modulated compounding Greenwood-Durand estimator.

RESULTS

Raw envelope histograms of simulated and in vitro EMBs were well matched with the Nakagami distribution with a high correlation coefficient of 0.965 ± 0.021 (P < 0.005). The mean values and gradients of m parameters of simulated and in vitro EMBs were smaller than those of linear scatterers due to the stronger nonlinearity. These m values exhibited a quasi-linear improvement with the increase in second harmonic nonlinear-to-linear component ratio regulated by pulse lengths and excitation frequencies at low- and high-concentration conditions.

CONCLUSIONS

The Nakagami distribution was suitable for the EMBs characterization but the corresponding m parameter was affected by the EMBs' nonlinearity. These validations provided support and nonlinear impact assessment for the EMBs' characterization using the Nakagami statistical model in the future.

Monitoring Microwave Ablation Using Ultrasound Echo Decorrelation Imaging: An ex vivo Study

In this study, a microwave-induced ablation zone (thermal lesion) monitoring method based on ultrasound echo decorrelation imaging was proposed. A total of 15 cases of ex vivo porcine liver microwave ablation (MWA) experiments were carried out. Ultrasound radiofrequency (RF) signals at different times during MWA were acquired using a commercial clinical ultrasound scanner with a 7.5-MHz linear-array transducer. Instantaneous and cumulative echo decorrelation images of two adjacent frames of RF data were calculated. Polynomial approximation images were obtained on the basis of the thresholded cumulative echo decorrelation images. Experimental results showed that the instantaneous echo decorrelation images outperformed conventional B-mode images in monitoring microwave-induced thermal lesions. Using gross pathology measurements as the reference standard, the estimation of thermal lesions using the polynomial approximation images yielded an average accuracy of 88.60%. We concluded that instantaneous ultrasound echo decorrelation imaging is capable of monitoring the change of thermal lesions during MWA, and cumulative ultrasound echo decorrelation imaging and polynomial approximation imaging are feasible for quantitatively depicting thermal lesions.