Search Results (705)

Background: Cardiac-respiratory dual gating in SPECT (DG-SPECT) is an emergent technique for alleviating motion blurring artifacts in myocardial perfusion imaging (MPI) due to both cardiac and respiratory motions. Moreover, the attenuation artifact may arise from the spatial mismatch between the sequential SPECT and CT attenuation scans due to the dual gating of SPECT data and non-gating CT images. Objectives: This study adapts a four-dimensional (4D) cardiac SPECT reconstruction with post-reconstruction respiratory motion correction (4D-RMC) for dual-gated SPECT. In theory, a respiratory motion-matched attenuation correction (MAC) method is expected to yield more accurate reconstruction results than the conventional motion-averaged attenuation correction (AAC) method. However, its potential benefit is not clear in the presence of practical imaging artifacts in DG-SPECT. In this study, we aim to quantitatively investigate these two attenuation methods for SPECT MPI: 4D-RMC (MAC) and 4D-RMC (AAC). Methods: DG-SPECT imaging (eight cardiac gates and eight respiratory gates) of the NCAT phantom was simulated using SIMIND Monte Carlo simulation, with a lesion (20% reduction in uptake) introduced at four different locations of the left ventricular wall: anterior, lateral, septal, and inferior. For each respiratory gate, a joint cardiac motion-compensated 4D reconstruction was used. Then, the respiratory motion was estimated for post-reconstruction respiratory motion-compensated smoothing for all respiratory gates. The attenuation map averaged over eight respiratory gates was used for each respiratory gate in 4D-RMC (AAC) and the matched attenuation map was used for each respiratory gate in 4D-RMC (MAC). The relative root mean squared error (RMSE), structural similarity index measurement (SSIM), and a Channelized Hotelling Observer (CHO) study were employed to quantitatively evaluate different reconstruction and attenuation correction strategies. Results: Our results show that the 4D-RMC (MAC) method improves the average relative RMSE by as high as 5.42% and the average SSIM value by as high as 1.28% compared to the 4D-RMC (AAC) method. Compared to traditional 4D reconstruction without RMC (“4D (MAC)”), these metrics were improved by as high as 11.23% and 27.96%, respectively. The 4D-RMC methods outperformed 4D (without RMC) on the CHO study with the largest improvement for the anterior lesion. However, the image intensity profiles, the CHO assessment, and reconstruction images are very similar between 4D-RMC (MAC) and 4D-RMC (AAC). Conclusions: Our results indicate that the improvement of 4D-RMC (MAC) over 4D-RMC (AAC) is marginal in terms of lesion detectability and visual quality, which may be attributed to the simple NCAT phantom simulation, but otherwise suggest that AAC may be sufficient for clinical use. However, further evaluation of the MAC technique using more physiologically realistic digital phantoms that incorporate diverse patient anatomies and irregular respiratory motion is warranted to determine its potential clinical advantages for specific patient populations undergoing dual-gated SPECT myocardial perfusion imaging. Full article

Packed granular materials absorb sound. In previous studies, granular materials sized a few millimeters and samples of grain size as a powder were studied; however, the grain sizes in between have not been addressed. In this study, the sound absorption coefficients of materials ranging from granular materials with a grain size d = 4 mm to powder materials with d = 0.05 mm were analyzed theoretically and experimentally. In addition, five packing types were studied: four types of regular packing and random packing. For these packing structures, the propagation constants and characteristic impedances were substituted within a one-dimensional transfer matrix for sound wave propagation, from which the normal-incidence sound absorption coefficient was calculated. Furthermore, our analysis accounted for particle longitudinal vibrations due to sound pressure. According to analyses of cross-sectional CT images considering tortuosity, the theoretical values for random packing tended to be close to the experimental values for d = 0.8 mm and smaller. For random packing structures with d = 0.3 mm or smaller, the experimental values were closer to the theoretical values for simple cubic lattice than the theoretical values for random packing. Full article

The evaluation of the fracturing effect of coalbed methane (CBM) wells is crucial for the efficient development of CBM reservoirs. Currently, studies focusing on the evaluation of the hydraulic fracture stimulation effect of coal seams and the integrated analysis of “drilling-fracturing-monitoring” are relatively insufficient. Therefore, this paper takes three drainage and production wells in the coalbed methane block on the northwest wing of the Xiangxia anticline in the Bijie Experimental Zone of Guizhou Province as the research objects. In view of the complex geological characteristics of this area, such as multiple and thin coal seams, high gas content, and high stress and low permeability, the paper systematically summarizes the results of drilling and fracturing engineering practices of the three drainage and production wells in the area, including the application of key technologies such as a two-stage wellbore structure and the “bentonite slurry + low-solid-phase polymer drilling fluid” system to ensure wellbore stability, low-solid-phase polymer drilling fluid for wellbore protection, and staged temporary plugging fracturing. On this basis, a study on microseismic signal acquisition and tomographic energy inversion based on a ground dense array was carried out, achieving four-dimensional dynamic imaging and quantitative interpretation of the fracturing fractures. The results show that the fracturing fractures of the three drainage and production wells all extend along the direction of the maximum horizontal principal stress, with azimuths concentrated between 88° and 91°, which is highly consistent with the results of the in situ stress calculation from the previous drilling engineering. The overall heterogeneity of the reservoir leads to the asymmetric distribution of fractures, with the transformation intensity on the east side generally higher than that on the west side, and the maximum stress deformation influence radius reaching 150 m. The overall transformation effect of each well is good, with the effective transformation volume ratio of fracturing all exceeding 75%, and most of the target coal seams are covered by the fracture network, significantly improving the fracture connectivity. From the perspective of the transformed planar area per unit fluid volume, although there are numerical differences among the three wells, they are all within the effective transformation range. This study shows that microseismic fracture monitoring technology can provide a key basis for the optimization of fracturing technology and the evaluation of the production increase effect, and offers a solution to the problem of evaluating the hydraulic fracture stimulation effect of coal seams. Full article

The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data. Full article

Three-dimensional surface topography offers a noninvasive alternative to radiographic imaging for evaluating trunk morphology and posture. This study aimed to establish normative values of trunk cross-sectional geometry and to examine their associations with spinal alignment in healthy young adults. A cross-sectional sample of 108 participants (62 women, 46 men; 19–23 years, normal BMI) underwent assessment using two complementary techniques: TorsoScan, which reconstructed 360° trunk cross-sections at thoracic levels T1, T4, T8, and T12, and DIERS Formetric 4D, which quantified spinal posture parameters. For each cross-section, sagittal and coronal diameters and cross-sectional areas were calculated; sex differences were analyzed using Welch’s t tests and effect sizes, and associations with posture were examined by Pearson correlations with false discovery rate correction and regression modeling. Trunk geometry followed a regular thoracic profile, with the largest coronal diameter at T4 and the maximal area at T8. Men showed consistently larger diameters and areas than women, with large effect sizes at T4–T12. Four associations remained significant: reduced mid-thoracic breadth and area were linked to greater lumbar lordosis, while upper thoracic depth correlated positively with thoracic kyphosis. Predictive models based on posture explained limited variance (R² ≤ 0.19). These findings provide sex-specific reference values and demonstrate that TorsoScan and DIERS Formetric yield complementary, partly convergent measures that may support radiation-free evaluation of trunk morphology in clinical and rehabilitative settings. Full article

Background: The automated analysis of intestinal organoids in microscopy images are essential for high-throughput morphological studies, enabling precision and scalability. Traditional manual analysis is time-consuming and subject to observer bias, whereas Machine Learning (ML) approaches have recently demonstrated superior performance. Purpose: This study aims to evaluate YOLO (You Only Look Once) for organoid segmentation and classification, comparing its standalone performance with a hybrid pipeline that integrates DL-based feature extraction and ML classifiers. Methods: The dataset, consisting of 840 light microscopy images and over 23,000 annotated intestinal organoids, was divided into training (756 images) and validation (84 images) sets. Organoids were categorized into four morphological classes: cystic non-budding organoids (Org0), early organoids (Org1), late organoids (Org3), and Spheroids (Sph). YOLO version 10 (YOLOv10) was trained as a segmenter-classifier for the detection and classification of organoids. Performance metrics for YOLOv10 as a standalone model included Average Precision (AP), mean AP at 50% overlap (mAP50), and confusion matrix evaluated on the validation set. In the hybrid pipeline, trained YOLOv10 segmented bounding boxes, and features extracted from these regions using YOLOv10 and ResNet50 were classified with ML algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Random Forest, eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptrons (MLP). The performance of these classifiers was assessed using the Receiver Operating Characteristic (ROC) curve and its corresponding Area Under the Curve (AUC), precision, F1 score, and confusion matrix metrics. Principal Component Analysis (PCA) was applied to reduce feature dimensionality while retaining 95% of cumulative variance. To optimize the classification results, an ensemble approach based on AUC-weighted probability fusion was implemented to combine predictions across classifiers. Results: YOLOv10 as a standalone model achieved an overall mAP50 of 0.845, with high AP across all four classes (range 0.797–0.901). In the hybrid pipeline, features extracted with ResNet50 outperformed those extracted with YOLO, with multiple classifiers achieving AUC scores ranging from 0.71 to 0.98 on the validation set. Among all classifiers, Logistic Regression emerged as the best-performing model, achieving the highest AUC scores across multiple classes (range 0.93–0.98). Feature selection using PCA did not improve classification performance. The AUC-weighted ensemble method further enhanced performance, leveraging the strengths of multiple classifiers to optimize prediction, as demonstrated by improved ROC-AUC scores across all organoid classes (range 0.92–0.98). Conclusions: This study demonstrates the effectiveness of YOLOv10 as a standalone model and the robustness of hybrid pipelines combining ResNet50 feature extraction and ML classifiers. Logistic Regression emerged as the best-performing classifier, achieving the highest ROC-AUC across multiple classes. This approach ensures reproducible, automated, and precise morphological analysis, with significant potential for high-throughput organoid studies and live imaging applications. Full article

This study examined the impact of fluid type and grain diameter on the interfacial area between different pairs of non-wetting and wetting fluids in natural porous media. Synchrotron X-ray microtomography was used to obtain high-resolution, three-dimensional images of multi-phase porous media systems. Multiple porous media, comprising a range of physical and geochemical properties, were used in this study. The four pairs of non-wetting/wetting fluids used were dense OIL/water, light OIL/water, air/dense OIL, and air/water. Images were obtained over a broad range of wetting phase saturation and for both wetting phase drainage and imbibition conditions. The results showed that for each fluid pair, the total (capillary + film) interfacial area increased with decreasing wetting fluid saturation. Interfacial areas were similar among all fluid pairs for a given porous medium. They were also similar for drainage and imbibition conditions. The maximum specific interfacial area (A_m) was shown to correlate well with inverse median grain diameter. The physical properties of the porous medium appear to have a greater influence on the magnitude of specific total interfacial area for a given saturation than fluid properties or wetting phase history. Full article

Bridges are critical to national mobility and economic flow, making dependable structural health monitoring (SHM) systems essential for safety and durability. However, the SHM data quality is often affected by sensor faults, transmission noise, and environmental interference. To address these issues, anomaly detection methods are widely adopted. Despite their wide use and variety, there is a lack of systematic evaluation that comprehensively compares these techniques. Existing reviews are often constrained by limited scope, minimal comparative synthesis, and insufficient focus on real-time performance and multivariate analysis. Consequently, this systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, sourced from eight reputable databases. Unlike prior reviews, this work presents a novel four-dimensional taxonomy covering real-time capability, multivariate support, analysis domain, and detection methods. Moreover, detection methods are further classified into three categories: distance-based, predictive, and image processing. A comparative evaluation of the reviewed detection methods is performed across five key dimensions: robustness, scalability, real-world deployment feasibility, interpretability, and data dependency. Findings reveal that image-processing methods are the most frequently applied (22 studies), providing high detection accuracy but facing scalability challenges due to computational intensity. Predictive models offer a trade-off between interpretability and performance, whereas distance-based methods remain less common due to their sensitivity to dimensionality and environmental factors. Notably, only 11 studies support real-time anomaly detection, and multivariate analysis is often overlooked. Moreover, time-domain signal processing dominates the field, while frequency and time-frequency domain methods remain rare despite their potential. Finally, this review highlights key challenges such as scalability, interpretability, robustness, and practicality of current models. Further research should focus on developing adaptive and interpretable anomaly detection frameworks that are efficient enough for real-world SHM deployment. These models should combine multi-modal strategies, handle uncertainty, and follow standardized evaluation protocols across varied monitoring environments. Full article

Background/Objectives: The recent development of four-dimensional X-ray velocimetry (4DXV) technology (three-dimensional space and time) provides a unique opportunity to obtain preclinical quantitative functional lung images. Only single-scan measurements in non-survival studies have been obtained to date; thus, methodologies enabling animal survival for repeated imaging to be accomplished over weeks or months from the same animal would establish new opportunities for the assessment of pathophysiology drivers and treatment response in advanced preclinical drug-screening efforts. Methods: An anesthesia protocol developed for animal recovery to allow for repetitive, longitudinal scanning of individual animals over time. Test–retest imaging scans from the lungs of healthy mice were performed over 8 weeks to assess the repeatability of scanner-derived quantitative imaging metrics and variability. Results: Using a murine model of fibroproliferative lung disease, this longitudinal scanning approach captured heterogeneous progressive changes in pulmonary function, enabling the visualization and quantitative measurement of averaged whole lung metrics and spatial/regional change. Radiation dosimetry studies evaluated the effects of imaging acquisition protocols on X-ray dosage to further adapt protocols for the minimization of radiation exposure during repeat imaging sessions using these newly developed image acquisition protocols. Conclusions: Overall, we have demonstrated that the 4DXV advanced imaging scanner allows for repeat measurements from the same animal over time to enable the high-resolution, noninvasive mapping of quantitative lung airflow dysfunction in mouse models with heterogeneous pulmonary disease. The animal anesthesia and image acquisition protocols described will serve as the foundation on which further applications of the 4DXV technology can be used to study a diverse array of murine pulmonary disease models. Together, 4DXV provides a novel and significant advancement for the longitudinal, noninvasive interrogation of pulmonary disease to assess spatial/regional disease initiation, progression, and response to therapeutic interventions. Full article

Head and neck cancer (HNC) is highly heterogeneous and difficult to treat, underscoring the need for rapid, patient-specific models. Standard three-dimensional (3D) cultures often lose stromal partners that influence therapy response. We developed a patient-derived system maintaining tumor cells, cancer-associated fibroblasts (CAFs), and cells undergoing partial epithelial–mesenchymal transition (pEMT) for drug sensitivity testing. Biopsies from four HNC patients were enzymatically dissociated. CAFs were directly cultured, and their conditioned medium (CAF-CM) was collected. Cryopreserved primary tumor cell suspensions were later revived, screened in five different growth media under 2D conditions, and the most heterogeneous cultures were re-embedded in 3D hydrogels with varied gel mixtures, media, and seeding geometries. Tumoroid morphology was quantified using a perimeter-based complexity index. Viability after treatment with cisplatin or Notch modulators (RIN-1, recombination signal-binding protein for immunoglobulin κ J region (RBPJ) inhibitor; FLI-06, inhibitor) was assessed by live imaging and the water-soluble tetrazolium-8 (WST-8) assay. Endothelial Cell Growth Medium 2 (ECM-2) medium alone produced compact CAF-free spheroids, whereas ECM-2 supplemented with CAF-CM generated invasive aggregates that deposited endogenous matrix. Matrigel with this medium and single-point seeding gave the highest complexity scores. Two of the three patient tumoroids were cisplatin-sensitive, and all showed significant growth inhibition with the FLI-06 Notch inhibitor, while the RBPJ inhibitor RIN-1 induced minimal change. The optimized scaffold retains tumor–stroma crosstalk and provides patient-specific drug response data within days after operation, supporting personalized treatment selection in HNC. Full article

Background: The implant–abutment interface has been thoroughly examined due to its impact on the success of implant healing and longevity. Removing the abutment is advantageous, but it changes the biomechanics of the implant fixture and restoration. This in vitro three-dimensional finite element analytical (FEA) study aims to evaluate the distribution of von Mises stress (VMS) in abutment-free and three additional implant abutment connections composed of various titanium alloys. Materials and methods: A three-dimensional implant-supported single-crown prosthesis model was digitally generated on the mandibular section using a combination of microcomputed tomography imaging (microCT), a computer-assisted designing (CAD) program (SolidWorks), Analysis of Systems (ANSYS), and a 3D digital scan (Visual Computing Lab). Four digital models [A (BioHorizons), B (Straumann AG), C abutment-free (Matrix), and D (TRI)] representing three different functional biomaterials [wrought Ti-6Al-4Va ELI, Roxolid (85% Ti, 15% Zr), and Ti-6Al-4V ELI] were subjected to simulated static/cyclic static loading in axial/oblique directions after being restored with highly translucent monolithic zirconia restoration. The stresses generated on the implant fixture, abutment, crown, screw, cortical, and cancellous bones were measured. Results: The highest VMSs were generated by the abutment-free (Model C, Matrix) implant system on the implant fixture [static (32.36 Mpa), cyclic static (83.34 Mpa)], screw [static (16.85 Mpa), cyclic static (30.33 Mpa), oblique (57.46 Mpa)], and cortical bone [static (26.55), cyclic static (108.99 Mpa), oblique (47.8 Mpa)]. The lowest VMSs in the implant fixture, abutment, screw, and crown were associated with the binary alloy Roxolid [83–87% Ti and 13–17% Zr]. Conclusions: Abutment-free implant systems generate twice the stress on cortical bone than other abutment implant systems while producing the highest stresses on the fixture and screw, therefore demanding further clinical investigations. Roxolid, a binary alloy of titanium and zirconia, showed the least overall stresses in different loadings and directions. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most common and effective methods for early detection of AD. With the rapid development of deep learning (DL) algorithms, AD detection based on deep learning has wide applications. Methods: In this research, we have developed an AD detection method based on three-dimensional (3D) convolutional neural networks (CNNs) for 3D MRI images, which can achieve strong accuracy when compared with traditional 3D CNN models. The proposed model has four main blocks, and the multi-layer fusion functionality of each block was used to improve the efficiency of the proposed model. The performance of the proposed model was compared with three different pre-trained 3D CNN architectures (i.e., 3D ResNet-18, 3D InceptionResNet-v2, and 3D Efficientnet-b2) in both tasks of multi-/binary-class classification of AD. Results: Our model achieved impressive classification results of 91.4% for binary-class as well as 80.6% for multi-class classification on the Open Access Series of Imaging Studies (OASIS) database. Conclusions: Such results serve to demonstrate that multi-stage feature fusion of 3D CNN is an effective solution to improve the accuracy of diagnosis of AD with 3D MRI, thus enabling earlier and more accurate diagnosis. Full article

Malay songket motifs are a vital component of Malaysia’s intangible cultural heritage, characterized by intricate visual designs and deep cultural symbolism. However, the practical digital preservation and retrieval of these motifs present challenges, particularly due to the rotational variations typical in textile imagery. This study introduces a novel Content-Based Image Retrieval (CBIR) model that integrates Principal Component Analysis (PCA) for feature extraction and Quadratic Geometric Distance (QGD) for measuring similarity. To evaluate the model’s performance, a curated dataset comprising 413 original images and 4956 synthetically rotated songket motif images was utilized. The retrieval system featured metadata-driven preprocessing, dimensionality reduction, and multi-angle similarity assessment to address the issue of rotational invariance comprehensively. Quantitative evaluations using precision, recall, and F-measure metrics demonstrated that the proposed PCAQGD + Rotation technique achieved a mean F-measure of 59.72%, surpassing four benchmark retrieval methods. These findings confirm the model’s capability to accurately retrieve relevant motifs across varying orientations, thus supporting cultural heritage preservation efforts. The integration of PCA and QGD techniques effectively narrows the semantic gap between machine perception and human interpretation of motif designs. Future research should focus on expanding motif datasets and incorporating deep learning approaches to enhance retrieval precision, scalability, and applicability within larger national heritage repositories. Full article

The three-dimensional (3D) structure of clouds is a key factor in atmospheric processes, profoundly influencing solar radiation transfer, weather patterns, and climate dynamics. However, accurately representing this complex structure in radiative transfer models remains a significant challenge. As part of the Deep 3D Scattering of Solar Radiation in the Atmosphere due to Clouds (D3D) project, we conducted a comprehensive study on the role of all-sky imagers (ASIs) in reconstructing observational 3D cloud fields and integrating them into advanced 3D cloud modeling. Since November 2022, a network of four ASIs has been operating across the broader Patras region in Greece, continuously capturing atmospheric measurements over an area of approximately 50 km². Using simultaneously captured images from the ASIs within the network, a 3D cloud reconstruction was performed utilizing advanced image processing techniques, with a primary focus on cumulus cloud scenarios. The Structure from Motion (SfM) technique was employed to reconstruct the 3D structural characteristics of clouds from two-dimensional images. The resulting 3D cloud fields were then integrated into the MYSTIC three-dimensional radiative transfer model to simulate and reconstruct solar irradiance fields. Full article