Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images.

OBJECTIVE: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors' puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. METHODS: The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. RESULTS: In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors' planning efficiency. CONCLUSION: After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications.

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

An Automatic Needle Puncture Path-Planning Method for Thermal Ablation of Lung Tumors.

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient's CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians' workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment.

Exploring Successful Cognitive Aging: Insights Regarding Brain Structure, Function, and Demographics.

In the realm of cognitive science, the phenomenon of "successful cognitive aging" stands as a hallmark of individuals who exhibit cognitive abilities surpassing those of their age-matched counterparts. However, it is paramount to underscore a significant gap in the current research, which is marked by a paucity of comprehensive inquiries that deploy substantial sample sizes to methodically investigate the cerebral biomarkers and contributory elements underpinning this cognitive success. It is within this context that our present study emerges, harnessing data derived from the UK Biobank. In this study, a highly selective cohort of 1060 individuals aged 65 and above was meticulously curated from a larger pool of 17,072 subjects. The selection process was guided by their striking cognitive resilience, ascertained via rigorous evaluation encompassing both generic and specific cognitive assessments, compared to their peers within the same age stratum. Notably, the cognitive abilities of the chosen participants closely aligned with the cognitive acumen commonly observed in middle-aged individuals. Our study leveraged a comprehensive array of neuroimaging-derived metrics, obtained from three Tesla MRI scans (T1-weighted images, dMRI, and resting-state fMRI). The metrics included image-derived phenotypes (IDPs) that addressed grey matter morphology, the strength of brain network connectivity, and the microstructural attributes of white matter. Statistical analyses were performed employing ANOVA, Mann-Whitney U tests, and chi-square tests to evaluate the distinctive aspects of IDPs pertinent to the domain of successful cognitive aging. Furthermore, these analyses aimed to elucidate lifestyle practices that potentially underpin the maintenance of cognitive acumen throughout the aging process. Our findings unveiled a robust and compelling association between heightened cognitive aptitude and the integrity of white matter structures within the brain. Furthermore, individuals who exhibited successful cognitive aging demonstrated markedly enhanced activity in the cerebral regions responsible for auditory perception, voluntary motor control, memory retention, and emotional regulation. These advantageous cognitive attributes were mirrored in the health-related lifestyle choices of the surveyed cohort, characterized by elevated educational attainment, a lower incidence of smoking, and a penchant for moderate alcohol consumption. Moreover, they displayed superior grip strength and enhanced walking speeds. Collectively, these findings furnish valuable insights into the multifaceted determinants of successful cognitive aging, encompassing both neurobiological constituents and lifestyle practices. Such comprehensive comprehension significantly contributes to the broader discourse on aging, thereby establishing a solid foundation for the formulation of targeted interventions aimed at fostering cognitive well-being among aging populations.

Recent research advances on simulation modeling of temperature distribution in microwave ablation of lung tumors.

Lung tumor is the first malignant tumor with the highest mortality, but only no more than one-third of patients can be treated by surgical resection. Microwave ablation (MWA) has become a new adjuvant therapeutic mean for lung tumors because of its low trauma, short treatment time, large ablation volume and wide application range. However, the treatment parameters of MWA, such as input power and ablation time, still depend on the doctors' experience, which leads to the ineffectiveness of MWA. Therefore, the accurate modeling of temperature distribution of lung tumor MWA has become a significant technical problem to be solved. Recent research was devoted to personalized characterization of lung tumor parameters, finite element analysis of temperature distribution in MWA and accurate ablation effect evaluation. In this paper, a review of the recently obtained results and data will be presented and discussed.

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.

[Multimodal high-grade glioma semantic segmentation network with multi-scale and multi-attention fusion mechanism].

Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.

Detection of microwave ablation coagulation areas using ultrasound Nakagami imaging based on Gaussian pyramid decomposition: A feasibility study.

In this paper, we explored the feasibility of using ultrasound Nakagami-m parametric imaging based on Gaussian pyramid decomposition (GPD) to detect microwave ablation coagulation areas. Monte Carlo simulation and phantom simulation results demonstrated that a 2-layer GPD model was sufficient to achieve the same m parameter estimation accuracy, smoothness and resolution as 3-layer and 4-layer. The performances of GPD, moment-based estimator (MBE) and window-modulated compounding (WMC) algorithms were compared in terms of parameter estimation, smoothness, resolution and contrast-to-noise (CNR). Results showed that the m parameter estimation obtained by GPD algorithm was better than that of MBE and WMC algorithms except the small window size (27 × 5). When using a window size of >3 pulse lengths, GPD algorithm could achieve better smoothness and CNR than MBE and WMC algorithms, but there was a certain loss of axial resolution. The computation time of GPD algorithm was less than that of WMC algorithm, while about 2.24 times that of MBE algorithm. Experimental results of porcine liver microwave ablation ex vivo (n = 20) illustrated that the average areas under the operating characteristic curve (AUCs) of Nakagami mGPD, mMBE and mWMC parametric imaging and homodyned-K (HK) α and k parametric imaging to detect coagulation areas were significantly improved by polynomial approximation (PAX). Kruskal-Wallis test showed that the accuracy of coagulation area detection obtained by PAX imaging of mGPD parameter had no significant difference with that of mMBE, mWMC, HK_α and HK_k parameters. This preliminary study suggested that Nakagami imaging based on GPD algorithm may have the potential to detect microwave ablation coagulation areas.

Molecular Mechanism of Malignant Transformation of Balb/c-3T3 Cells Induced by Long-Term Exposure to 1800 MHz Radiofrequency Electromagnetic Radiation (RF-EMR).

PURPOSE: We aimed to investigate RF-EMR-induced cell malignant transformation. METHODS: We divided Balb/c-3T3 cells into sham and expo groups. The expo groups were exposed to a 1800 MHz RF continuous wave for 40 and 60 days, for 4 h per day. The sham group was sham-exposed. Cells were harvested for a cell transformation assay, transplantation in severe combined immune deficient (SCID) mice, soft agar clone formation detection, and a transwell assay. The mRNA microarray assay was used to declare key genes and pathways. RESULTS: The exposed Balb/c-3T3 cells showed a strong increase in cell proliferation and migration. Malignant transformation was observed in expo Balb/c-3T3 cells exposed for 40 days and 60 days, which was symbolized with visible foci and clone formation. Expo Balb/c-3T3 cells that were exposed for 40 days and 60 days produced visible tumors in the SCID mice. Lipid metabolism was the key biological process and pathway involved. The mevalonate (MVA) pathway was the key metabolic pathway. The interacted miRNAs could be further research targets to examine the molecular mechanism of the carcinogenic effects of long-term exposure. CONCLUSION: Exposure for 40 and 60 days to 1800 MHz RF-EMR induced malignant transformation in Balb/c-3T3 cells at the SAR of 8.0 W/kg. We declared that lipid metabolism was the pivotal biological process and pathway. The MVA pathway was the key metabolic pathway.

Influences of blood flow parameters on temperature distribution during liver tumor microwave ablation.

Highlights: (1) A 3D simulation model of MWA (microwave ablation) based on the temperature-dependent characteristic parameters and blood flow parameters was established to realize the visual simulation of temperature distribution and coagulation zone. The internal forced convection condition was used to accurately characterize the large vessel. (2) The ex vivo MWA experimental platform was built to verify the accuracy of the simulation model. A peristaltic pump was employed for operatively controlling blood circulation and a medical soft plastic tube was introduced for appropriately simulating a blood vessel. (3) The influences of blood flow parameters of large vessels on temperature distribution and coagulation zone were systematically analyzed in order to provide reference and guidance for MWA clinicians. Purpose: Clinical MWA of liver tumor is significantly limited by the accurate prediction of vascular cooling effects. To provide reference and guidance for clinical MWA of liver tumor, the three-dimensional effects of different blood flow parameters of large vessels on MWA temperature distribution were systematically evaluated. Materials and methods: Firstly, the MWA three-dimensional finite element simulation model with blood flow parameters was established. Secondly, to verify the effectiveness of the model, MWA was performed ex vivo in porcine liver for 360 s and the temperature was measured by thermocouples. A medical soft plastic tube was placed parallel to the MWA antenna to simulate a natural liver vessel. Finally, based on this model, the influences of different vessel diameters and vessel-antenna spacings on MWA temperature distribution were analyzed. Results: Sixteen ablations were performed to verify the accuracy of the simulation model. The mean temperature errors between measured data and simulation results at six measurement points were 3.87 ℃. In the first 10 seconds of MWA, the vessel cooling effect on temperature distribution was negligible. When the vessel-antenna spacing was 5 mm and the vessel diameter varied from 3 mm to 6 mm, the temperature at the measured point near the vessel decreased by 2.11 ℃ at 360 s. When the vessel diameter was 6 mm and the vessel-antenna spacing varied from 5 mm to 7 mm, the temperature at the measured point near the vessel reduced by 14.91 ℃ at 360 s. In addition, blood diameter had little influence on the temperature distribution near the heating point. The volume of coagulation zone will not be obviously affected once the vessel lies outside the predicted coagulation zone. Conclusions: The MWA simulation model with blood flow parameters is established. Vessel-antenna spacing is the primary factor affecting the temperature distribution. A vessel with larger diameter can have a more significant effect on the temperature distribution. The large vessel will take away and block part of conduction heat, so the coagulation zone will not be formed on the lateral side of the vessel.

[Advances in Ultrasound Tissue Characterization and Its Application in Thermal Ablation of Tumors].

The methods of monitoring the thermal ablation of tumor are compared and analyzed in recent years. The principle method results and insufficient of ultrasound elastography and quantitative ultrasound imaging are discussed. The results show that ultrasonic tissue signature has great development space in the field of real-time monitoring of thermal ablation, but there are still some problems such as insufficient monitoring accuracy difficulty in whole-course monitoring and insufficient in vivo experiments, so it is impossible to realize clinical application. It is necessary to further study the monitoring method which can realize accurate and real-time detection of ablation zone and transition zone and can be easily combined with the existing ultrasonic equipment.

Long-term 1800MHz electromagnetic radiation did not induce Balb/c-3T3 cells malignant transformation.

There is an increased public concern about potential health hazards of exposure to electromagnetic radiation (EMR). To declare the carcinogenic effects of 1800 MHz EMR. In this study, Balb/c-3T3 cells were exposed to 1800 MHz EMR for 80 days. The cells were harvested for cell proliferation detection, cell cycle assay, plate clone, and soft agar formation assay, transwell assay, and mRNA microarray detection. 1800 MHz EMR promoted Balb/c-3T3 proliferation. No clones were observed in both plate clone and soft agar clone formation assay. The percentage of cells in S phase in Balb/c-3T3 cells of 80d Expo was obviously higher than the percetage in 80d Sham cells. 80d Expo Balb/c-3T3 cells had stronger migration ability than Sham cells. The mRNA microarray results indicated that cell cycle, cell division, and DNA replication were the main biological processes the significant genes enriched, with higher expression of RPs and Mcms. 1800 MHz EMR promoted Balb/c-3T3 cells proliferation and migration. The mRNA microarray results indicated that cell cycle, cell division, and DNA replication were the main biological processes the significant genes enriched.

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 (αNCA and kNCA) imaging was compared with NCA-based Nakagami (mNCA) imaging and NCA-based cumulative echo decorrelation (CEDNCA) 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 αNCA, kNCA, mNCA, and CEDNCA 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 αNCA, kNCA, mNCA, and CEDNCA 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 αNCA, kNCA, mNCA, and CEDNCA 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.

Ultrasound Backscatter Envelope Statistics Parametric Imaging for Liver Fibrosis Characterization: A Review.

Early detection and diagnosis of liver fibrosis is of critical importance. Currently the gold standard for diagnosing liver fibrosis is biopsy. However, liver biopsy is invasive and associated with sampling errors and can lead to complications such as bleeding. Therefore, developing noninvasive imaging techniques for assessing liver fibrosis is of clinical value. Ultrasound has become the first-line tool for the management of chronic liver diseases. However, the commonly used B-mode ultrasound is qualitative and can cause interobserver or intraobserver difference. Ultrasound backscatter envelope statistics parametric imaging is an important group of quantitative ultrasound techniques that have been applied to characterizing different kinds of tissue. However, a state-of-the-art review of ultrasound backscatter envelope statistics parametric imaging for liver fibrosis characterization has not been conducted. In this paper, we focused on the development of ultrasound backscatter envelope statistics parametric imaging techniques for assessing liver fibrosis from 1998 to September 2019. We classified these techniques into six categories: constant false alarm rate, fiber structure extraction technique, acoustic structure quantification, quantile-quantile probability plot, the multi-Rayleigh model, and the Nakagami model. We presented the theoretical background and algorithms for liver fibrosis assessment by ultrasound backscatter envelope statistics parametric imaging. Then, the specific applications of ultrasound backscatter envelope statistics parametric imaging techniques to liver fibrosis evaluation were reviewed and analyzed. Finally, the pros and cons of each technique were discussed, and the future development was suggested.

Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging.

Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.

[Research progress on computed tomography image detection and classification of pulmonary nodule based on deep learning].

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.

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.

Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators.

BACKGROUND: The homodyned-K (HK) distribution is an important statistical model for describing ultrasound backscatter envelope statistics. HK parametric imaging has shown potential for characterizing hepatic steatosis. However, the feasibility of HK parametric imaging in assessing human hepatic steatosis in vivo remains unclear. METHODS: In this paper, ultrasound HK µ parametric imaging was proposed for assessing human hepatic steatosis in vivo. Two recent estimators for the HK model, RSK (the level-curve method that uses the signal-to-noise ratio (SNR), skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics), were investigated. Liver donors (n=72) and patients (n=204) were recruited to evaluate hepatic fat fractions (HFFs) using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate, and severe) using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct µ RSK and µ XU images to correlate with HFF analyses and fatty liver stages. The µ RSK and µ XU parametric images were constructed using the sliding window technique with the window side length (WSL) =1-9 pulse lengths (PLs). The diagnostic values of the µ RSK and µ XU parametric imaging methods were evaluated using receiver operating characteristic (ROC) curves. RESULTS: For the 72 participants in Group A, the µ RSK parametric imaging with WSL =2-9 PLs exhibited similar correlation with log10(HFF), and the µ RSK parametric imaging with WSL = 3 PLs had the highest correlation with log10(HFF) (r=0.592); the µ XU parametric imaging with WSL =1-9 PLs exhibited similar correlation with log10(HFF), and the µ XU parametric imaging with WSL =1 PL had the highest correlation with log10(HFF) (r=0.628). For the 204 patients in Group B, the areas under the ROC (AUROCs) obtained using µ RSK for fatty stages ≥ mild (AUROC1), ≥ moderate (AUROC2), and ≥ severe (AUROC3) were (AUROC1, AUROC2, AUROC3) = (0.56, 0.57, 0.53), (0.68, 0.72, 0.75), (0.73, 0.78, 0.80), (0.74, 0.77, 0.79), (0.74, 0.78, 0.79), (0.75, 0.80, 0.82), (0.74, 0.77, 0.83), (0.74, 0.78, 0.84) and (0.73, 0.76, 0.83) for WSL =1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. The AUROCs obtained using µ XU for fatty stages ≥ mild, ≥ moderate, and ≥ severe were (AUROC1, AUROC2, AUROC3) = (0.75, 0.83, 0.81), (0.74, 0.80, 0.80), (0.76, 0.82, 0.82), (0.74, 0.80, 0.84), (0.76, 0.80, 0.83), (0.75, 0.80, 0.84), (0.75, 0.79, 0.85), (0.75, 0.80, 0.85) and (0.73, 0.77, 0.83) for WSL = 1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. CONCLUSIONS: Both the µ RSK and µ XU parametric images are feasible for evaluating human hepatic steatosis. The WSL exhibits little impact on the diagnosing performance of the µ RSK and µ XU parametric imaging. The µ XU parametric imaging provided improved performance compared to the µ RSK parametric imaging in characterizing human hepatic steatosis in vivo.

RF ablation thermal simulation model: Parameter sensitivity analysis.

OBJECTIVE: The aim of the research is to obtain the relative influences of some critical electro-thermal parameters on the ablation temperature and lesion volume during temperature-controlled radiofrequency ablation (RFA) of liver tumor by parameter sensitivity analysis. METHODS: The finite element method (FEM) has been used to establish the simulation model of RFA temperature field, and the sensitivity of the tissue parameters has been analyzed. The effects of six parameters have been taken into account, including the thermal specific capacity (Cp), the thermal conductivity (k), the electrical conductivity (Sigma), the density (rho), the dielectric constant (Epsilon) and the resistance (R). The simulation processes based on different parameter values have been accomplished with Comsol Multiphysics software, and the sensitivity parameters have been obtained utilizing the variance contribution rate (SS%) or the main effects. RESULTS: It was found that the ablation temperature and lesion volume increased with increasing the values of Rand Sigma, but was a reverse situation for Cp and rho. Besides, the influence of k on ablation volume was relatively small and Epsilon had a negligible effect on ablation temperature. CONCLUSIONS: It is concluded that these parameter sensitivity results can provide scientific and reliable reference for the specificity analysis of the RF ablation models.

Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging.

Nonalcoholic fatty liver disease is a type of hepatic steatosis that is not only associated with critical metabolic risk factors but can also result in advanced liver diseases. Ultrasound parametric imaging, which is based on statistical models, assesses fatty liver changes, using quantitative visualization of hepatic-steatosis-caused variations in the statistical properties of backscattered signals. One constraint with using statistical models in ultrasound imaging is that ultrasound data must conform to the distribution employed. Small-window entropy imaging was recently proposed as a non-model-based parametric imaging technique with physical meanings of backscattered statistics. In this study, we explored the feasibility of using small-window entropy imaging in the assessment of fatty liver disease and evaluated its performance through comparisons with parametric imaging based on the Nakagami distribution model (currently the most frequently used statistical model). Liver donors (n = 53) and patients (n = 142) were recruited to evaluate hepatic fat fractions (HFFs), using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate and severe), using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct B-mode, small-window entropy and Nakagami images to correlate with HFF analyses and fatty liver stages. The diagnostic values of the imaging methods were evaluated using receiver operating characteristic curves. The results demonstrated that the entropy value obtained using small-window entropy imaging correlated well with log10(HFF), with a correlation coefficient r = 0.74, which was higher than those obtained for the B-scan and Nakagami images. Moreover, small-window entropy imaging also resulted in the highest area under the receiver operating characteristic curve (0.80 for stages equal to or more severe than mild; 0.90 for equal to or more severe than moderate; 0.89 for severe), which indicated that non-model-based entropy imaging-using the small-window technique-performs more favorably than other techniques in fatty liver assessment.