Search Results (194)

This study presents a comprehensive computational evaluation of radiofrequency (RF) ablation efficacy and the spatial formation of thermal ablation zones within a 3D model of a liver tumor. By systematically comparing these configurations, the study aims to elucidate the physical mechanisms governing electromagnetic (EM) energy dissipation in hepatic tissue and to provide clear engineering guidelines for optimizing RF applicator selection and treatment planning in clinical practice. To reliably simulate the biophysical phenomena of the RF ablation procedure, a coupled electro-thermal model based on the finite element method and the Pennes bioheat equation was implemented. The research investigates six distinct applicator variants: conventional needle-type applicators and advanced expandable umbrella-type RF applicators equipped with four- and eight-tine electrodes, each evaluated in both monopolar and bipolar configurations. Numerical simulations were conducted for a standard 10 min ablation procedure at varying applied voltages to assess the specific absorption rate (SAR) distribution, transient heating dynamics, and the exact volumes of the resulting coagulation necrosis which were quantified using rigorous isotherms and the cumulative equivalent minutes at 43 °C (CEM₄₃) thermal dose index. Volumetric analysis of the ablation zones revealed that bipolar multi-tine electrodes induce highly localized heat concentration. Conversely, monopolar multi-tine setups strongly disperse EM energy. The results demonstrated that, for conventional needle applicators, the monopolar configuration generated significantly larger necrosis zones than the bipolar operating mode. The RF applicator geometry and its operating mode directly dictate the spatial extent of liver tissue necrosis. Moreover, advanced numerical treatment planning is essential for optimizing SAR and CEM₄₃ distributions and ensuring safe and complete hepatocellular carcinoma eradication. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article

The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits. Full article

Transitioning toward efficient and digital industrial design requires preliminary tools that support early decision-making in plant layout studies. This qualitative and exploratory study analyzes an Artificial Intelligence (AI)-assisted and Augmented Reality (AR)-supported workflow within the Systematic Layout Planning (SLP) framework for the preliminary spatial evaluation of a cosmetic emulsion production plant. The study was developed as a case study based on a previously reported layout for obtaining cosmetic emulsions from Amazonian oils. A top-view layout was examined through structured prompts aligned with SLP criteria, including product journey, activity relationships, relational diagrams, and space requirements. ChatGPT was used only as a qualitative reasoning assistant, without optimization, prediction, mathematical modeling, or algorithmic functions. After the AI-assisted review, the refined layout was represented in three dimensions and visualized through AR in a real environment. The results identified potential improvements related to operational flow, traceability, critical area relationships, and spatial organization. AR-assisted visualization provided preliminary visual evidence of compatibility between the refined layout and the selected site, supporting an early review of circulation, access, and volumetric behavior. The sequential integration of SLP, AI, and AR is proposed as an exploratory workflow for early-stage layout evaluation, pending future quantitative validation studies and expert technical review. Full article

Historic stone-built heritage is continually exposed to environmental stressors that promote material degradation and surface alteration, often in spatially heterogeneous ways. Rapid, non-destructive diagnostic tools capable of capturing both spectral and spatial information are therefore essential to support preventive conservation strategies. In this study, short-wave infrared hyperspectral imaging (SWIR-HSI), combined with chemometric analysis, three-dimensional (3D) visualisation, and complementary spectroscopic techniques, is investigated as an integrated framework for assessing the conservation state of historical stonework. A field campaign was conducted at the 15th- to 17th-century San Emeterio and San Celedonio Church (Larrabetzu, Spain), a sandstone structure exposed to environmental pollution and adverse conditions. SWIR hyperspectral images (1000–2500 nm) were acquired in situ and analysed using Principal Component Analysis (PCA) and K-Means clustering to explore spectral variability and segment the façade into spectrally homogeneous regions. The resulting chemometric outputs were projected onto a photogrammetry-based 3D RGB model, enabling volumetric visualisation of material heterogeneity and surface alteration patterns. To support the interpretation of hyperspectral features, selected regions were further analysed using X-ray fluorescence (XRF) and Raman spectroscopy. The proposed 3D-SWIR approach enhances the interpretability of hyperspectral data by embedding it within its architectural context and linking spectral variability to underlying physicochemical processes. This integrated methodology demonstrates strong potential as a non-destructive diagnostic and decision-support tool for assessing, monitoring, and conserving cultural heritage stone structures. Full article

Flash-flood and floodplain inundation simulations are highly sensitive to the spatiotemporal variability of convective rainfall, particularly during the initial runoff generation stage. However, coarse-resolution numerical weather prediction (NWP) forcing tends to smooth localized rainfall extremes, limiting its ability to accurately represent hydrological responses in low-relief floodplains. In this study, we couple a diffusion-enhanced radar nowcasting model, Diff_ConvLSTM, with a spatial resolution of 1 km and a temporal resolution of 6 min, to assess the hydrological value of high-resolution rainfall forcing over the middle Yangtze River floodplain. We introduce a monotone piecewise cubic Hermite interpolation scheme to ensure a stable transition from discrete high-frequency rainfall inputs to continuous hydrodynamic integration. Evaluation using a radar dataset from 2023 to 2024 shows that Diff_ConvLSTM better preserves intense convective echoes and rainband structures compared to the baseline ConvLSTM, increasing the Probability of Detection at the 40 dBZ threshold by 65.8%. A forcing-replacement experiment for the flood event on 30 June 2023 demonstrates that AI-based nowcasting rainfall forcing reduces peak-discharge underestimation, improves volumetric consistency, and produces inundation patterns that are closer to the observation-driven reference than those generated by low-resolution forecast forcing, although positive biases in inundation area and water depth persist. An additional event in 2024 confirms that the improvements are primarily reflected in discharge magnitude and flood volume representation, while enhancements in peak timing remain limited. Overall, the results illustrate both the added value and the remaining limitations of AI-enhanced nowcasting for hydrologically informed flood forecasting. Full article

This research presents a robust multivariate statistical framework for the non-destructive prediction of geomechanical state parameters in quartz-rich coastal sands through acoustic emission (AE) monitoring. Granular media under symmetrical compressive stress function as complex natural systems, where microscopic energy dissipation—arising from particle rearrangement and grain microcracking—radiates as transient elastic waves. To decode these stochastic processes, 24 confined uniaxial compression tests were conducted across diverse soil typologies and moisture contents (0–12%). A high-dimensional data matrix was constructed, integrating 13 geotechnical variables with 48 acoustic descriptors formulated through three distinct temporal aggregations: stage-specific, history average and weighted history average. The statistical results identify the logarithmic effective vertical stress (${\log }_{10}\left({{\mathsf{\sigma }}^{\prime }}_{\mathrm{v}}\right)$) and the cumulative axial strain ($\mathsf{\epsilon }$) as the most significant geomechanical drivers, exhibiting Pearson correlation coefficients $\left|p\right|$ ≥ 0.85 with acoustic activity. In the acoustic domain, the analysis reveals that Signal Strength (${\mathrm{S}}_{\mathrm{s}}$) and cumulative energy ($\mathrm{E}$) flux are the most reliable predictors for volumetric deformation, while the amplitude ($\mathrm{A}$), b-value ($\mathrm{b}$), and average frequency ($\mathrm{F}$) emerge as critical indicators for identifying the transition between spatial rearrangement and the onset of grain fragmentation. Furthermore, the inclusion of dimensionless parameters, particularly earliness ($\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{l}$), enhances model stability by standardising waveform symmetry across varying stress regimes. High-order polynomial regression models (up to the third degree) were derived, demonstrating that the statistical complexity of acoustic signatures allows for the high-fidelity inference of the soil matrix’s initial and state parameters. This methodology establishes a unified mathematical architecture for the in situ characterisation of granular skeletons, balancing computational efficiency with predictive power in intricate geological domains. Full article

To overcome low bioavailability and high trauma in inner ear therapies, targeted delivery across the round window membrane (RWM) via hollow microneedles (HMNs) offers a promising solution. However, the fabrication of high-aspect-ratio, small-size HMNs remains challenging. This study demonstrates the successful fabrication of small-outer-diameter HMNs using a 10 μm resolution digital light processing (DLP) system. Finite element analysis (FEA) identified a double tangent-arc transition as the optimal structural design for minimizing stress concentration. To manage the heightened parameter sensitivity at sub-feature-scale fabrication, a corrected curing index (CCI) model was established via a physics-informed regression approach incorporating polymerization kinetics and nonlinear spatial intensity distribution, achieving high fitting accuracy (R² > 0.96). Under optimized parameters, the fabricated HMNs possessed mean dimensions of 805.13 μm in height, 37.54 μm in inner diameter, and 79.36 μm in outer diameter. Compressive tests exhibited a robust structural strength of up to 141 mN per needle following post-curing. Combined in silico and in vitro experiments demonstrated excellent penetration performance. Furthermore, the HMNs achieved stable, pressure-dependent delivery with volumetric flow rates rising from 0.14 mL∙min⁻¹ to 0.39 mL∙min⁻¹ as driving pressure escalated from 50 kPa to 300 kPa, validating their functional capacity for controlled drug administration. Full article

The spatiotemporal evolution of seepage fields and the associated hydrodynamic risk of subsequent internal erosion pose a critical threat to the structural integrity of marine and hydraulic infrastructure. To quantify these complex fluid–solid interactions, this study develops a high-fidelity numerical model—coupling the Navier–Stokes equations with the Darcy–Forchheimer resistance model and the Volume of Fluid (VOF) method—to investigate transient hydrodynamics within porous foundations under complex geometric and geological boundary conditions. Parametric analyses reveal that spatial porosity distribution fundamentally dictates the system’s seepage capacity; notably, relocating a highly permeable stratum to the shallow sub-surface eliminates upper hydraulic bottlenecks and significantly escalates total volumetric discharge. Furthermore, the study systematically evaluates the hydrodynamic efficacy of multi-dimensional seepage control structures. Results demonstrate that while increasing the vertical depth of a cutoff wall is highly efficient in restricting bulk volumetric flux, it inadvertently induces intense localized streamline convergence and flow acceleration at the structural tip. Conversely, lateral expansion of the wall base, though yielding only a moderate reduction in total seepage, successfully diffuses this concentrated flow and substantially attenuates peak pore fluid velocities. Ultimately, a combined design paradigm is proposed for practical coastal engineering applications: prioritizing vertical penetration to optimize bulk seepage reduction, concurrently integrated with moderate lateral base expansion to redistribute concentrated hydrodynamic shear stresses, thereby minimizing the hydrodynamic potential for localized piping and ensuring long-term stability against seepage-induced degradation. Full article

Understanding how built structures and urban vegetation jointly relate to population patterns is increasingly important for sustainable urban planning. This study used airborne LiDAR data to analyze three-dimensional urban morphology across Zagreb, Croatia, at neighborhood and district scales. A 1 m nDSM and classified rasters were used to derive canopy, building, height, volumetric, and built–vegetation balance metrics. Spatial clustering was assessed using Moran’s I, LISA, and Getis–Ord Gi*, while relationships with population density were evaluated using correlation and spatial regression models. At the neighborhood level, canopy cover ranged from 4.5% to 84.7%, while UMI ranged from 0.002 to 10.368. UMI showed significant spatial clustering (Moran’s I = 0.457, p = 0.001), with 24 high–high and 64 low–low clusters. Composite balance metrics outperformed individual vegetation or building indicators; logUMI provided the strongest performance for log-transformed population-density models, with SLM pseudo-R² = 0.903. Agreement assessment showed high consistency for canopy cover (R² = 0.997), while building height agreement was weaker for global datasets. Results indicate that transformed built–vegetation balance metrics provide useful complementary indicators for describing urban morphology and population density patterns. Full article

In this paper, we estimate and refine 3D human pose using the 3D point cloud or mesh model reconstructed from RGB-D cameras or volumetric capture systems. We first reconstruct the 3D model using the multi-view cameras to estimate a highly accurate skeleton. To obtain a 2D skeleton with low error, the reconstructed 3D model is projected to four virtual planes after decidi ng the direction of the 3D model. Four 2D skeletons are estimated from four images projected in the virtual plane. Afterward, the refinement process selects candidate joints based on the distribution of local vertices and the DBSCAN algorithm. It applies a sphere fitting to ensure that the final joints are located within the body volume. The joints are combined at the intersection through the back-projection of the joints, including those in the 2D skeleton on the virtual plane. The joints in the intersection are refined using the spatial distribution of the 3D information. Through the proposed method, we estimated a stable and geometrically consistent 3D human pose from reconstructed volumetric data. Using models with ground truth, we calculated the MPJPE between the skeletons of the proposed and the ground truth. The 3D pose estimation was evaluated through a visual assessment of the captured image, and the results were quantitatively compared with the 3D joint positions acquired by the motion capture device. Full article

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment. Full article

Background/Objectives: Lung cancer is one of the most common and lethal malignancies worldwide. Early detection remains challenging due to its variable biological behavior. Computed tomography (CT) is the primary imaging method used for early detection. However, the manual interpretation of CT scans is constrained by several challenges such as reliance on expert experience, increasing clinical workload, and considerable variability among observers. Methods: This study introduces an explainable transformer-based framework capable of distinguishing among the three principal clinical categories of lung cancer (small-cell lung cancer, non-small-cell lung cancer, and normal) while simultaneously generating automated radiology reports from CT images. In contrast to conventional single-slice methodologies, the proposed model employs a multi-slice volumetric encoding strategy that captures spatial continuity and anatomical relationships across the CT slices. Visual features extracted by a ViT-based encoder are transformed into a compact patient-level representation through a Learnable Query Attention Pooling (LQAP) mechanism, and this unified representation is subsequently used for both three-class prediction and report generation with a GPT-2-based decoder. To enhance explainability, slice-wise Grad-CAM maps are produced, visually highlighting the anatomical cues that guide the model’s decisions. Results: Experiments conducted on the newly curated LungCA dataset comprising 767 patients demonstrate that the model achieves 97.40% accuracy in the Turkish (TR) reporting scenario and 94.81% accuracy in the English (EN) scenario, alongside strong alignment with human-written reports in BLEU, ROUGE, METEOR, and CIDEr metrics. Conclusions: The findings demonstrate that the proposed multi-slice transformer framework achieves robust performance in both classification and radiology report generation, enhances transparency throughout the decision-making process, and provides a robust artificial intelligence solution capable of effectively supporting clinical workflows in lung cancer assessment. Full article

The development of a 3D digital cadastre is a key objective of Australia’s Cadastre 2034 strategy for modernising land information infrastructure. Jurisdictions across Australia are progressively transitioning from conventional 2D cadastral systems towards 3D cadastral models to better represent complex land and property rights, particularly in dense urban environments. In New South Wales (NSW), LandXML is currently the standard for digital cadastral lodgement. However, its limitations in supporting 3D spatial data representation have prompted investigation of alternative standards such as LandInfra and its InfraGML encoding. The aim of this study is to investigate how LandInfra handles existing cadastral information in New South Wales, Australia. In particular, this study is a technical and structural comparison of LandXML and InfraGML, examining data modelling workflows and geometric encoding. A hybrid research methodology integrating Design Science Research (DSR) and Case Study Research (CSR) was applied. Two representative cadastral plans—a standard deposited plan and a strata plan—were digitised using LISCAD 2025 v25.9.23.1 and AutoCAD Civil 3D 2026 V1 and subsequently modelled in both LandXML and InfraGML formats. Validation was conducted using KITModelViewer and schema validators, with comparative analysis of development cycle, modelling structure, usability, and workflow. This study demonstrates that InfraGML offers semantic richness and structural flexibility compared to LandXML within the scope of the examined case studies, although its practical adoption is constrained by limited commercial software support and may present adoption challenges for practitioners. The findings of this research suggest that LandInfra offers considerable potential for advancing the future development of 3D cadastre in Australia. In this context, InfraGML is positioned as a promising data standard for ongoing investigation and future research, rather than an immediate substitute for LandXML. Within the scope of this study, a fully operational 3D cadastral implementation is neither developed nor validated within existing legal or institutional frameworks, and complex 3D scenarios are not addressed. Future research should explore integration with CAD platforms, legislative implications of 3D survey features, complex volumetric cases, and formal 3D topological validation, and alternative modelling approaches, such as using Nested Parcels method and InfraJSON encoding. Full article

Background: Cone Beam Computed Tomography (CBCT) is widely used in image-guided radiotherapy because it provides on-board volumetric imaging at relatively low doses, but its clinical utility for synthetic CT (sCT) generation remains limited by noise, scatter, artifacts, and reduced Hounsfield Unit (HU) fidelity. Conditional diffusion models (CDMs) have recently emerged as a promising alternative to earlier deep learning approaches because their iterative denoising process may better preserve anatomical structure and model uncertainty. Objective: This systematic review evaluates the use of conditional diffusion models for CBCT-to-CT synthesis, with particular attention to architectural strategies, reported quantitative outcomes, and potential clinical relevance. A systematic search was conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2013 and 2024. Eleven studies met the eligibility criteria and were analyzed to address three questions: (1) Which conditional diffusion strategies have been used? (2) What outcomes have been reported? and (3) What clinical implications have been discussed? Results: Across the included studies, CDMs frequently showed promising image quality performance, especially when incorporating anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations. However, the evidence base remains small and highly heterogeneous with respect to anatomy, dimensionality, supervision strategy, and evaluation metrics, limiting the strength of direct comparative claims. The reviewed literature suggests that conditional diffusion models are a promising direction for CBCT-to-CT synthesis, but stronger dose-aware validation, standardized reporting, and broader multicenter evaluation are still needed before routine clinical deployment. This review has been registered with the International Prospective Register of Systematic Reviews (PROSPERO), under registration number CRD42024619240. Full article