Evaluation Of Algorithms Research Articles

A Large Model-Derived Algorithm for Complex Organoids with Internal Morphogenesis and Digital Marker Derivation.

Automated segmentation and evaluation algorithms have been demonstrated to enhance the simplicity and translational utility of organoid technology. However, there is a pressing need for the development of complex organoids that possess epithelium environmental elements, dense regional cell aggregation, and intraorganoid morphologies. Nevertheless, there has been limited progress, including both the construction of data sets and the development of algorithms, in the use of user-friendly microscopy to address such complex organoids. In this study, a data set of bright-field and living cell fluorescence images in paired forms and with temporal variance was constructed using droplet-engineered lung organoids. Additionally, a large model-based algorithm was developed. Both the organoid contours and intraorganoid morphologies were included in the data set, and their physical parameters were included and screened to form multiplex digital markers for organoid evaluation. The algorithm has been demonstrated to outperform existing methods and is therefore suitable for the evaluation of complex organoids. It is expected that the algorithm will facilitate the successful demonstration of AI in organoid evaluation and decision-making regarding their status.

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing & Evaluation (NDT & E) approach to monitor the health
of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical
analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight
to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP)
sample. The proposed approach models the observed data covariance into combination of temporal signal covariance
and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by
the presence of heterogeneity in the experimental data obtained from GFRP sample. This novel method is based on the
coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the
negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used
statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect
detection with the proposed technique.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud–edge interaction and cloud–cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.

MECOM Locus classical transcript isoforms affect tumor immune microenvironment and different targets in ovarian cancer

The MECOM locus is a gene frequently amplified in high-grade serous ovarian carcinoma (HGSOC). Nevertheless, the body of research examining the associations among MECOM transcripts, patient prognosis, and their role in modulating the tumor immune microenvironment (TIME) remains sparse, particularly in large cohorts. This study assessed the expression of MECOM transcripts in 352 HGSOC patients and 88 normal ovarian tissues from the combined GTEx/TCGA database. Using resources such as the UCSC Genome Browser, Ensembl, and NextProt, two transcripts corresponding to classical protein isoforms from MECOM were identified. Cox proportional hazards regression analysis, Kaplan-Meier survival curves, and a comprehensive TIME evaluation algorithm were employed to elucidate the connections between the expression levels of these transcripts and both patient prognosis and TIME status. Chromatin Immunoprecipitation sequencing (ChIP-seq) data for the two protein isoforms, as well as RNA sequencing data post-targeted silencing, were analyzed to identify potential regulatory targets of the different transcription factors. Elevated expression of the MECOM isoform transcripts was correlated with poorer survival in HGSOC patients, potentially through the modulation of cancer-associated fibroblasts (CAFs) and immunosuppressive cell populations. In contrast, higher levels of EVI1 isoform transcripts were linked to enhanced survival, possibly due to the regulation of CD8+ T cells, macrophages, and a reduction in the expression of JUN protein, or its DNA-binding activity on downstream genes. Diverse protein isoforms derived from MECOM were found to differentially affect the survival and tumor development in ovarian cancer patients through specific mechanisms. Investigating the molecular mechanisms underlying disease pathogenesis and identifying potential drug target proteins at the level of splice variant isoforms were deemed crucial.

Preoperative visualization of parathyroid pathology in patients with primary hyperparathyroidism using PAT/KT: a retrospective study

INTRODUCTION: Topical diagnosis of pathologically altered parathyroid glands in patients with primary hyperparathyroidism is a challenge both for specialists of radial diagnostics and for planning the scope of surgical intervention. The choice of diagnostic modality for localization of parathyroid glands depends on the availability of the method for a particular medical center. PET/CT is a promising method to determine the localization of parathyroid adenomaOBJECTIVE: To analyze the experience of using PET/CT with 11C-methionine and PET/CT with 11C-choline for preoperative diagnosis of parathyroid pathology in patients with primary hyperparathyroidism.MATERIALS AND METHODS: 72 patients diagnosed with PHPT and underwent surgical treatment were included in the study. Before surgical intervention all patients underwent laboratory and instrumental investigations in the scope of: blood tests for PTH, TTH, ionized calcium, calcitonin, 25(OH)D, creatinine, phosphorus, ultrasound of the neck, computer tomography of the neck with intravenous bolus contrasting.RESULTS: PET/CT showed the highest rates of DA in the patient group before primary surgery — 90.9%, compared with ultrasound (89.6%) and CT with IV contrast (84.9%). In the group of solitary adenomas of ectopic MTCS, PET/CT also had a higher DA — 92.8% than the other modalities. In patients with relapsed or persistent disease, the informativity rates of CT with intravenous contrast and PET/CT were similar (Se — 100%, Sp — 94.1%, DT — 96.2%).DISCUSSION: The algorithm for preoperative evaluation of patients to identify the location of pathologic parathyroid glands now includes imaging with two modalities: Neck ultrasound and radionuclide imaging: planar scintigraphy (washout/subtraction), SPECT, SPECT-CT. If the results are questionable, a CT scan of the neck with contrast or PET/CT is performed as a second step. None of the modalities has 100% diagnostic accuracy. Our study demonstrated the superiority of PET/CT over ultrasound and CT w/in contrast in a group of patients scheduled for primary surgical treatment. In a small group of patients, high informative rates of PET/CT and CT with contrast were demonstrated before reoperation, confirming the great potential of these techniques in these patients.CONCLUSION: PET/CT with 11C-methionine and PET/CT with 11C-choline have high diagnostic accuracy in patients with primary hyperparathyroidism before reoperation. PET/CT and CT with contrast showed similarly high diagnostic accuracy for the diagnosis of parathyroid adenoma with persistence or recurrence of the disease in patients with primary hyperparathyroidism.

An evaluation model for an optimal decarbonisation process in the built environment

PurposeThe goal is to deliver a decision-support framework to both public and private entities engaged in energy retrofit investments in the property market.Design/methodology/approachThe evaluation algorithm that is being offered takes an innovative approach to financial and economic analysis. Its foundation is a market-driven/cost-driven method, drawing logic from operational research and goal programming.FindingsThe algorithm is tested to a real estate portfolio yielding an optimal asset retrofitting schedule. The ranking list is determined by taking into consideration a variety of parameters, including investment costs and total CO2 emissions from energy retrofit initiatives. The Carbon Risk Real Estate Monitor’s (CRREM) emission targets for 2030 are employed as a reference point in the process of creating a ranking list of the assets that compose the real estate portfolio under examination.Practical implicationsThe evaluation algorithm will allow to determine, in a real estate portfolio, a priority list of assets to be enhanced. This is accomplished by taking into account the client’s financial resources, the overall cost of the intervention programmes for each asset, and the effects that each asset would have on the environment and the energy once the suggested retrofit programme is put into place.Originality/valueThe study proposes a methodological approach that seeks to balance the optimisation of energy performance, the reduction of environmental effect, the promotion of social well-being and economic sustainability in the context of managing the current property sector.

The Effectiveness of using Artificial Intelligence for Building Professional Competencies of Future Specialists in Art Majors in Higher Education Institutions

The research aims to study the impact of the use of artificial intelligence (AI) tools in higher education institutions (HEIs) on building professional competencies of future art specialists. The research employed quantitative and qualitative methods (in particular, modeling methods, pedagogical experiments, and survey of respondents to assess the impact of AI tools on building professional competencies). The author’s definition of the concept of “professional competencies of art specialists” is proposed. Targeted tools were selected and used for building components of professional competencies. For example, VocalAnalysis AI tools were used to form the perceptual component — for students majoring in Musical Art; Art Vision AI — for students majoring in Fine Arts; ChoreoVision AI — for students majoring in Choreography. The results of the study show that students rated their level of ability to use AI as higher than medium. The questionnaire designed to study the impact of the use of AI on building professional competencies of future specialists in art majors, demonstrated a high level of agreement between the assessment of the impact of the use of AI tools on the formation of various components of professional competencies. Further research can be aimed at the development and testing of an algorithm for objective expert evaluation of specific AI tools for the implementation of art projects by students of the specified art majors.

Making Formulog Fast: An Argument for Unconventional Datalog Evaluation

With its combination of Datalog, SMT solving, and functional programming, the language Formulog provides an appealing mix of features for implementing SMT-based static analyses (e.g., refinement type checking, symbolic execution) in a natural, declarative way. At the same time, the performance of its custom Datalog solver can be an impediment to using Formulog beyond prototyping—a common problem for Datalog variants that aspire to solve large problem instances. In this work we speed up Formulog evaluation, with some surprising results: while 2.2× speedups can be obtained by using the conventional techniques for high-performance Datalog (e.g., compilation, specialized data structures), the big wins come by abandoning the central assumption in modern performant Datalog engines, semi-naive Datalog evaluation. In the place of semi-naive evaluation, we develop eager evaluation, a concurrent Datalog evaluation algorithm that explores the logical inference space via a depth-first traversal order. In practice, eager evaluation leads to an advantageous distribution of Formulog’s SMT workload to external SMT solvers and improved SMT solving times: our eager evaluation extensions to the Formulog interpreter and Soufflé’s code generator achieve mean 5.2× and 7.6× speedups, respectively, over the optimized code generated by off-the-shelf Soufflé on SMT-heavy Formulog benchmarks. All in all, using compilation and eager evaluation (as appropriate), Formulog implementations of refinement type checking, bottom-up pointer analysis, and symbolic execution achieve speedups on 20 out of 23 benchmarks over previously published, hand-tuned analyses written in F ♯ , Java, and C++, providing strong evidence that Formulog can be the basis of a realistic platform for SMT-based static analysis. Moreover, our experience adds nuance to the conventional wisdom that traditional semi-naive evaluation is the one-size-fits-all best Datalog evaluation algorithm for static analysis workloads.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.

Optimization of surgical evaluation algorithms for living donor liver transplantation

BackgroundLiving donor liver transplantation (LDLT) is an established and endorsed alternative for deceased donor liver transplantation with better recipient outcomes. Nevertheless, while extensive evaluation of potential donors is crucial, evaluation algorithms differ between transplant centres and guidelines. MethodsWe included 317 individuals evaluated for LDLT between 07/2007–07/2022 in a retrospective analysis. The evaluation process was analysed to identify the key reasons for declining 77 potential donors. Additionally, 146 donors that underwent LDLT were analysed regarding risk factors for complications. ResultsThe main reasons for donor refusal were liver volumetry (40.3 %) and metabolic factors including obesity or steatotic liver disease (20.8 %). Contrast-enhanced computed tomography (CECT) identified 63.6 % of all declined donors; CECT combined with assessment of medical history, physical examination, blood testing and ultrasonography, identified 87.0 % of declined potential donors. Associated with this selection, complication rates in donors were low (≥II in 17.1 %; none with ≥IVb). Notably, higher age was a risk factor for developing a complication ≥II after hemi-hepatectomy (p = 0.0373). ConclusionsWe propose a progressive 4-step evaluation algorithm that begins with a very basic assessment combined with up-front CECT. This early phase of testing is expected to identify nearly 90 % of ineligible donors, thereby conserving critical resources, time and money, as well as minimising burden for potential donors. FundingJ.M.W. received funding by grant We-4675/6–1 from the Deutsche Forschungsgemeinschaft (DFG) in Bonn, Germany.

Construction Method of Information Security Detection based on Clustering Algorithm

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.

Developing a real-time compaction quality assessment methodology for subgrade based on semi-supervised co-training

The current intelligent methodologies for real-time evaluation of subgrade compaction quality predominantly rely on supervised machine learning algorithms. However, the scarcity of sand cone test significantly impedes model performance, thereby severely limiting the applicability of intelligent subgrade compaction. This paper proposes a semi-supervised co-training algorithm for real-time evaluation of subgrade compactness during the construction procedure, leveraging unlabeled data to enhance the model performance. Based on the compaction datasets from various subgrade scenarios, the proposed PSO-XGB-Co-training KNN-PLS (PSO-XGB-CoKP) algorithm is utilized to train the unlabeled data, boasting a 20.4% reduction in Mean Squared Error (MSE). The semi-supervised co-training algorithm is modified by employing different regressors as co-trained sub-models and increasing the number of regressors. The model is optimized by levering the accuracy and computational cost, and an optimal data augmentation volume is recommended through the sensitivity study. This study provides an alternative approach for leveraging unbalanced and small-sample datasets to develop a reliable intelligent methodology for evaluating subgrade compaction quality in engineering practice.

Algorithms for evaluation of minimal cut sets

Objective:We propose a way to enhance the evaluation of minimal cut sets (MCSs) in biological systems modeled by Petri nets, by providing criteria and methodology for determining their optimality in disabling specific processes without affecting critical system components. Methods:This study concerns Petri nets to model biological systems and utilizes two primary approaches for MCS evaluation. First is the analyzing impact on t-invariants to identify structural dependencies. Second is assessing the impact on potentially starved transitions caused by the inactivity of specific MCSs. This approach deal with net dynamics. These methodologies aim to offer practical tools for assessing the quality and effectiveness of MCSs. Results:The proposed methodologies were applied to two case studies. In the first case, a cholesterol metabolism network was analyzed to investigate how local inflammation and oxidative stress, in conjunction with cholesterol imbalances, influence the progression of atherosclerosis. The MCSs were ranked, with the top sets presented, focusing on those that disabled the fewest number of t-invariants. In the second case, a carbohydrate metabolism disorder model was examined to understand its impact on atherosclerosis progression. The analysis aimed to identify MCSs that could inhibit the atherosclerosis process by targeting specific transitions. Both studies utilized the Holmes software for calculations, demonstrating the effectiveness of the proposed evaluation methodologies in ranking MCSs for practical biological applications. Conclusion:The algorithms proposed in this paper offer an analytical approach for evaluating the quality of MCSs in biological systems. By providing criteria for MCS optimality, these approaches have potential to enhance the utility of MCS analysis in systems biology, aiding in the understanding and manipulation of complex biological networks.Algorithm are implemented within Holmes software, an open-source project available at https://github.com/bszawulak/HolmesPN.

Algorithms and Data Structures for Cs-smooth RMB-splines of Degree 2s + 1

The simple mesh refinement algorithm of Groiss et al. (2023) generates T-meshes admitting Reachable Minimally supported (RM) B-splines that possess the property of local linear independence and form a non-negative partition of unity. The construction was first presented for the bilinear case and has later been extended to Cs-smooth splines of degree p=2s+1. The present paper is devoted to algorithms and data structures for RMB-splines. We prove that the memory consumption of the data structures for representing a T-mesh and the associated RMB-splines is linear with respect to the mesh size, and we describe the details of the underlying refinement algorithm. Moreover, we introduce a novel evaluation algorithm for RMB-spline surfaces, which is based solely on repeated convex combinations of the control points, thereby generalizing de Boor's algorithm for tensor-product splines. Numerical experiments are included to demonstrate the advantageous behaviour of the proposed data structures and algorithms with respect to their efficiency. We observe that the total computational time (which includes also error estimation and spline coefficient computation) scales roughly linearly with the number of degrees of freedom for the meshes considered.

A Novel Fractional Model and Its Application in Network Security Situation Assessment

The evaluation process of the Fractional Order Model is as follows. To address the commonly observed issue of low accuracy in traditional situational assessment methods, a novel evaluation algorithm model, the fractional-order BP neural network optimized by the chaotic sparrow search algorithm (TESA-FBP), is proposed. The fractional-order BP neural network, by incorporating fractional calculus, demonstrates enhanced dynamic response characteristics and historical dependency, showing exceptional potential for handling complex nonlinear problems, particularly in the field of network security situational awareness. However, the performance of this network is highly dependent on the precise selection of network parameters, including the fractional order and initial values of the weights. Traditional optimization methods often suffer from slow convergence, a tendency to be trapped in local optima, and insufficient optimization accuracy, which significantly limits the practical effectiveness of the fractional-order BP neural network. By introducing cubic chaotic mapping to generate an initial population with high randomness and global coverage capability, the exploration ability of the sparrow search algorithm in the search space is effectively enhanced, reducing the risk of falling into local optima. Additionally, the Estimation of Distribution Algorithm (EDA) constructs a probabilistic model to guide the population toward the globally optimal region, further improving the efficiency and accuracy of the search process. The organic combination of these three approaches not only leverages their respective strengths, but also significantly improves the training performance of the fractional-order BP neural network in complex environments, enhancing its generalization ability and stability. Ultimately, in the network security situational awareness system, this integration markedly enhances the prediction accuracy and response speed.

Multi-Trajectory Planning Control Strategy for Hydropower Plant Bridge Crane Based on Evaluation Algorithm

Currently, the research on crane trajectory planning mostly aims to, first, plan the trajectories of the crane and the trolley, and then to use a trial-and-error method or optimization algorithm to iteratively calculate the optimal trajectory parameters under the control of the optimal trajectory parameters to achieve the suppression of the swing angle. However, research on the fusion application of multi-trajectory planning algorithms is very rare. In addition, the existing methods are not suitable for the special operation control of hydropower plant bridge cranes. Based on the application scenario of hydropower plant bridge cranes, this paper proposes a comprehensive multi-trajectory control strategy based on the entropy weight technique for order preference, similarly to the ideal solution (TOPSIS) evaluation method. Specifically, the kinematic analysis of the crane is carried out and the trajectory evaluation index system is established. Secondly, under the walking constraint condition, four different trajectory planning algorithms are used to obtain the crane trajectory curve. In order to ensure the accuracy and comprehensiveness of the evaluation, the evaluation data are obtained through the Adams motion simulation platform. Finally, based on the entropy weight TOPSIS evaluation method, the optimal walking trajectory for each displacement is selected. The simulation and experimental results show that the evaluation method can select the optimal trajectory based on the motion characteristics of the trajectory algorithm in different displacement conditions, effectively reducing the load swing during the walking process of the crane and improving the positioning accuracy.

Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models

Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features—the mode-parameterized Conway–Maxwell–Poisson (COMP) distribution and the generalized Poisson model. However both require the use of either constraints on the parameter space or a parameterization that leads to challenges in interpretability. We propose spatial mean-parameterized COMP models that retain the flexibility of these models while resolving the above issues. We use a Bayesian spatial filtering approach in order to efficiently handle high-dimensional spatial data and we use reversible-jump MCMC to automatically choose the basis vectors for spatial filtering. The COMP distribution poses two additional computational challenges—an intractable normalizing function in the likelihood and no closed-form expression for the mean. We propose a fast computational approach that addresses these challenges by, respectively, introducing an efficient auxiliary variable algorithm and pre-computing key approximations for fast likelihood evaluation. We illustrate the application of our methodology to simulated and real datasets, including Texas HPV-cancer data and US vaccine refusal data. Supplementary materials for this article are available online.

A novel semi-analytic algorithm for evaluation of nearly singular integrals in boundary element analysis

A novel semi-analytic method is proposed to evaluate various nearly singular integrals accurately. Different from the traditional semi-analytical method, the Taylor expansion for shape functions, Jacobian, and so on in our method is based on the nearest point from a source point to an integral element rather than the projection point from the source point to the integral element. Therefore, it can more accurately approximate the distance from the source point to Gaussian integration points. Then, approximate expressions for the integrand functions of two-dimensional potential problems are derived. The effectiveness of the proposed method is verified by evaluating the calculation accuracy of physical quantities at points in different geometric models, and comparing it with the traditional semi-analytical method and distance transform method.

Scatterer spacing based on Gabor atoms matched from harmonic ultrasound echoes to improve assessment of microwave‐induced thermal lesions

AbstractBackgroundMicrowave ablation (MWA) is a minimally invasive alternative for the treatment of unresectable liver tumors. To verify the effectiveness and safety of MWA, it is critical to measure the temperature variation and assess the regions of the microwave‐induced thermal lesions.PurposeRecent studies have indicated that the locations of optimally matched Gabor atoms (LOMGA) from ultrasound radiofrequency (RF) echo signals allow accurate and stable scatterer spacing estimation. Herein, a harmonic‐based LOMGA method is proposed to estimate the scatterer spacing for improving the assessment of microwave‐induced thermal lesions.MethodsThe mean scatterer spacing (MSS) is estimated via the LOMGA method incorporating the selection of concise atoms from separated second‐harmonic RF echo signals with the pulse‐inversion algorithm for thermal lesion evaluation. In vitro experiments, 10 fresh porcine liver samples were ablated at different time nodes during the ablation period, and 200 sets of second‐harmonic and fundamental RF echo signals were randomly selected from the regions of interest in the coagulated liver samples for MSS estimation. The means and standard deviations of the MSSs, as well as the linear regression for the mean MSSs, were calculated from fundamental and second‐harmonic signals for comparison and evaluation, the receiver operating characteristic (ROC) curves for the 200 sets of fundamental‐based and harmonic‐based MSS estimates from the 10 liver samples at five pairs of adjacent time nodes were calculated, and one‐way analysis of variance (ANOVA) tests were performed for the five pairs of adjacent time nodes. The fundamental and harmonic‐based p‐values in the ANOVA tests and the areas under the ROC curves (AUCs) were calculated to statistically analyze the differences in the MSSs between adjacent time nodes.ResultsThe harmonic‐based increments in the intensity variation and coherent components were larger than the fundamental‐based increments with the increasing ablation time. The harmonic‐based MSSs from the 10 liver samples at five pairs of adjacent time nodes were found to be highly statistically significant (p < 0.01). Thus, the harmonic‐based MSSs had greater variations. Compared with the fundamental‐based results, for the five preset ST values, the average increment in the harmonic‐based mean slopes was 69.22% and the average decrement in the mean standard deviations was 11.67% for the linear‐fitting MSS results, and the results were statistically significant (p < 0.05).ConclusionHarmonic‐based MSSs are more sensitive and robust to variations in coagulated tissues, which is advantageous for the assessment of microwave‐induced thermal lesions.