Search Results (15,610)

Background/Objectives: Hepatocellular carcinoma remains a major cause of death from cancer globally. While ¹⁸F-FDG PET/CT is commonly used for tumor imaging, its sensitivity is limited, especially due to high liver background uptake. Recently, ⁶⁸Ga-FAPI PET/CT, which targets fibroblast activation protein in tumor stroma, has emerged as a promising diagnostic tool. In this study, we aimed to assess the diagnostic performance of ⁶⁸Ga-FAPI-04 PET/CT in HCC patients with and without liver cirrhosis and to explore the relationship between PET metrics, tumor size, and PIVKA-II serum marker. Methods: In this prospective single-center study, 59 patients with confirmed HCC (37 with cirrhosis, 22 without) underwent ⁶⁸Ga-FAPI-04 PET/CT. The standard dose (1.5–2.0 MBq/kg) was administered intravenously, and imaging was carried out 60 min post-injection. Semi-quantitative parameters including SUVmax, SUVmean, and tumor-to-background ratio were calculated. Diagnostic performance was assessed using histopathology and multimodal imaging. Statistical analyses included the Mann–Whitney U test and Spearman correlation. Results: The overall sensitivity for HCC detection was 89.8%, with a specificity of 60% and accuracy of 87%. Sensitivity and specificity showed a tendency to be lower in cirrhotic compared with non-cirrhotic patients, with a notably higher background liver uptake in cirrhosis (SUVmax 3.60 vs. 1.3, p < 0.001), resulting in lower TBR values (3.7 vs. 7.0, p < 0.001). A strong correlation between SUVmax and tumor size was seen in non-cirrhotic HCC, while a moderate association between SUVmax and PIVKA-II levels was observed in cirrhotic patients. Conclusions:⁶⁸Ga-FAPI-04 PET/CT demonstrates high sensitivity for HCC detection and may serve as a complementary imaging modality, particularly when interpreted through conventional cross-sectional imaging. Image interpretation in cirrhotic livers may be challenging due to increased background uptake and reduced TBR. Associations between PET-derived parameters, tumor size, and serum PIVKA-II levels should be considered hypothesis-generating and require validation in larger, multicenter studies with clinical outcome data. Full article

Objectives: This study aimed to define the learning curve required for cardiac surgery residents to acquire basic technical and interpretive skills in transit-time flow measurement (TTFM) and high-resolution epicardial ultrasonography (HRUS) during coronary artery bypass grafting (CABG). Methods: Prospective, observational, single-center study evaluating performance using a novel scoring system combining functional (TTFM) and anatomical (HRUS) assessment criteria. This study was registered on ClinicalTrials.gov (Identifier: NCT06589323). Nine cardiac surgery residents without prior hands-on experience in TTFM or HRUS were enrolled. Twenty-seven elective CABG patients (67 grafts) were analyzed. Each measurement was compared with those obtained by an expert benchmark surgeon (N.T.) under standardized hemodynamic conditions. Results: Residents achieved the predefined primary endpoint (combined TTFM + HRUS score/number of grafts ≥ 11) after a median of 3 cases (IQR 2–4) and 7 anastomoses (IQR 7–10). Kaplan–Meier analysis showed a progressive increase in the probability of success, with a sharp rise after the seventh anastomosis. A shorter interval between attempts (<30 days) was significantly associated with earlier achievement of the endpoint (p < 0.05). Median acquisition time for TTFM was 25 s, with <10% inter-observer variability across all flow parameters. HRUS images of adequate quality were obtained within 60 s in >90% of cases, though slightly lower success rates were observed for lateral and inferior wall targets. No resident- or procedure-related variable was independently associated with performance improvement. Conclusions: Mastery of basic TTFM and HRUS skills requires only a few cases and anastomoses, demonstrating a short and attainable learning curve. These findings challenge the perception of a steep learning process and support the routine use of intraoperative graft verification techniques in all CABG procedures. Full article

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production. Full article

Missing seismic data in reflection seismology, which frequently arises from a variety of operational and natural limitations, immediately impairs the quality of ensuing imaging and calls into question the validity of geological interpretation. Traditional techniques for reconstructing seismic data frequently rely significantly on parameter choices and prior assumptions. Even while these methods work well for partially missing traces, reconstructing whole shot gather is still a difficult task that has not been thoroughly studied. Data-driven approaches that summarize and generalize patterns from massive amounts of data have become more and more common in seismic data reconstruction research in recent years. This work builds on earlier research by proposing an enhanced technique that can recreate whole shot gathers as well as partially missing traces. During model training, we first implement a Moveout-window selective slicing method for reconstructing missing traces. By creating training datasets inside a high signal-to-noise ratio (SNR) window, this method improves the model’s capacity for learning. Additionally, a technique is presented for the receiver domain reconstruction of missing shot data. A dual-domain reconstruction method is used to successfully recover the seismic data in order to handle situations where there is simultaneous missing data in both domains. Full article

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology. Full article

Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including vaguely defined structural boundaries and irregular shape distributions. To tackle these constraints, we design a multi-scale feature attention network (MFA-Net), customized specifically for thyroid nodule, skin lesion, and breast lesion segmentation tasks. This network framework integrates three core components: a Bidirectional Feature Pyramid Network (Bi-FPN), a Slim-neck structure, and the Convolutional Block Attention Module (CBAM). CBAM steers the model to prioritize boundary regions while filtering out irrelevant information, which in turn enhances segmentation precision. Bi-FPN facilitates more robust fusion of multi-scale features via iterative integration of top-down and bottom-up feature maps, supported by lateral and vertical connection pathways. The Slim-neck design is constructed to simplify the network’s architecture while effectively merging multi-scale representations of both target and background areas, thus enhancing the model’s overall performance. Validation across four public datasets covering thyroid ultrasound (TNUI-2021, TN-SCUI 2020), dermoscopy (ISIC 2016), and breast ultrasound (BUSI) shows that our method outperforms state-of-the-art segmentation approaches, achieving Dice similarity coefficients of 0.955, 0.971, 0.976, and 0.846, respectively. Additionally, the model maintains a compact parameter count of just 3.05 million and delivers an extremely fast inference latency of 1.9 milliseconds—metrics that significantly outperform those of current leading segmentation techniques. In summary, the proposed framework demonstrates strong performance in thyroid, skin, and breast lesion segmentation, delivering an optimal trade-off between high accuracy and computational efficiency. Full article

Object detection for autonomous vehicles under adverse weather conditions—such as rain, fog, snow, and low light—remains a significant challenge due to severe visual distortions that degrade image quality and obscure critical features. This paper presents YOLOv11-TWCS, an enhanced object detection model that integrates TransWeather, the Convolutional Block Attention Module (CBAM), and Spatial-Channel Decoupled Downsampling (SCDown) to improve feature extraction and emphasize critical features in weather-degraded scenes while maintaining real-time performance. Our approach addresses the dual challenges of weather-induced feature degradation and computational efficiency by combining adaptive attention mechanisms with optimized network architecture. Evaluations on DAWN, KITTI, and Udacity datasets show improved accuracy over baseline YOLOv11 and competitive performance against other state-of-the-art methods, achieving mAP@0.5 of 59.1%, 81.9%, and 88.5%, respectively. The model reduces parameters and GFLOPs by approximately 19–21% while sustaining high inference speed (105 FPS), making it suitable for real-time autonomous driving in challenging weather conditions. Full article

Supercritical CO₂ (SC-CO₂) fracturing has emerged as a promising technology for the effective stimulation of unconventional tight reservoirs due to its low viscosity, high diffusivity, and environmental advantages. However, existing fracturability evaluation models often oversimplify key parameters and lack validation under realistic dual-well conditions. To address these gaps, we developed a multi-factor coupled evaluation model incorporating well spacing, stress anisotropy, and fluid viscosity and proposed a fracturability index (FI) to quantify the potential for complex fracture development. True triaxial SC-CO₂ fracturing experiments using both single- and dual-well setups were conducted, and 3D fracture networks were analyzed via CT imaging and U-Net segmentation. Results show strong agreement between FI and fracture complexity. Optimal fracturing conditions were identified, providing a practical framework for the design and optimization of SC-CO₂ fracturing in tight reservoirs. Full article

Background: Left ventricular systolic dysfunction (LVSD) is a major determinant of prognosis in patients with ischemic heart disease. Electrocardiography (ECG) is widely available, inexpensive, and may aid in identifying patients at risk. We hypothesized that a composite score derived from multiple established ECG markers could improve the detection of LVSD in patients with stable angina. Methods: In this single-center, cross-sectional study, 177 patients undergoing elective coronary angiography for stable angina were included. Patients were classified as LVSD-negative (n = 123) or LVSD-positive (n = 54) based on echocardiographic ejection fraction. ECG parameters, including fragmented QRS, pathologic Q waves, R-wave peak time, QRS duration, and frontal QRS–T angle, were assessed. Independent predictors of LVSD were identified using multivariate logistic regression. A composite ECG score was constructed by assigning one point to each abnormal parameter. Model robustness was evaluated using bootstrap resampling (1000 iterations) and 10-fold cross-validation. Results: Multivariable analysis identified prior stent implantation, fragmented QRS, pathological Q waves, R-wave peak time, frontal QRS–T angle (log-transformed), and QRS duration as independent predictors of LVSD. ROC analysis demonstrated good discriminatory performance for R-wave peak time (AUC 0.804), QRS duration (AUC 0.649), and frontal QRS–T angle (AUC 0.825) measurements. The composite ECG score showed a stepwise association with LVSD: a score of ≥2 yielded high sensitivity (88%) and negative predictive value (97%), whereas a score of ≥3 provided high specificity (100%) and positive predictive value (100%). Bootstrap resampling and cross-validation confirmed model stability and strong discriminatory performance (mean AUC, 0.964; accuracy, 0.91). Conclusions: A simple composite ECG score integrating multiple established ECG markers is associated with the robust detection of LVSD in patients with stable angina. Although not a substitute for echocardiography, this score may support early risk stratification and help identify patients who warrant further imaging evaluations. External validation in larger and more diverse populations is required before routine clinical implementation of this model. Full article

Background and Clinical Significance: Hepatic sarcoidosis is a benign lesion of unknown etiology. The gold standard for diagnosing hepatic sarcoidosis is histopathological examination. The symptoms and imaging findings of patients with hepatic sarcoidosis are often atypical, leading to misdiagnosis as hepatocellular carcinoma (HCC). Ultrasound resolution microscopy (URM) can overcome the diffraction limit, enabling fine visualization and quantitative analysis of the microvascular networks. This study aimed to provide new evidence for the differential diagnosis of these two diseases by comparing the URM parameters of hepatic sarcoidosis initially misdiagnosed as HCC with those of HCC. Case Presentation: A 67-year-old woman was admitted to the hospital due to upper abdominal pain for two weeks. Ultrasonography revealed a liver mass. The lesion was located in segment IV of the left hepatic lobe, was approximately 18 × 10 mm in size, and appeared hypoechoic. Contrast-enhanced ultrasound and enhanced magnetic resonance imaging both showed a “fast-in, fast-out” pattern, strongly suggesting HCC. The tumor markers were within the normal range. The patient underwent a laparoscopic left hepatic lobectomy. The histopathological diagnosis of the resected specimen was “hepatic sarcoidosis”. URM examination was performed during the preoperative diagnostic process. Subsequently, the URM parameters of the patient’s lesion were analyzed and compared with those of HCC. The results showed differences in multiple URM parameters, including microvascular flow velocity, diameter, microvascular density ratio, and vascular distribution, between this case of hepatic sarcoidosis and HCC. Conclusions: URM can quantitatively and multidimensionally evaluate the microvasculature of liver lesions, providing new reference data for the diagnosis and differential diagnosis of hepatic sarcoidosis. Full article

Instance segmentation in agricultural robotics requires a balance between real-time performance and accuracy. This study proposes a lightweight pomelo image segmentation method based on the YOLOv8n-seg model integrated with the RepGhost module. A pomelo dataset consisting of 5076 samples was constructed through systematic image acquisition, annotation, and data augmentation. The RepGhost architecture was incorporated into the C2f module of the YOLOv8-seg backbone network to enhance feature reuse capabilities while reducing computational complexity. Experimental results demonstrate that the YOLOv8-seg-RepGhost model enhances efficiency without compromising accuracy: parameter count is reduced by 16.5% (from 3.41 M to 2.84 M), computational load decreases by 14.8% (from 12.8 GFLOPs to 10.9 GFLOPs), and inference time is shortened by 6.3% (to 15 ms). The model maintains excellent detection performance with bounding box mAP50 at 97.75% and mask mAP50 at 97.51%. The research achieves both high segmentation efficiency and detection accuracy, offering core support for developing visual systems in harvesting robots and providing an effective solution for deep learning-based fruit target recognition and automated harvesting applications. Full article

The effective focal length is a critical determinant of optical performance and imaging quality, serving as a fundamental parameter for components ranging from ophthalmic lenses to precision microlens arrays. With the rapid advancement of complex optical systems in microscopy and smart manufacturing, there is an increasing demand for high-precision measurement techniques that can characterize these parameters with low uncertainty. In this paper, a quadri-wave lateral shearing interferometry (QWLSI) measurement system was developed. A novel precision focal length measurement method of optical lenses based on the principle of QWLSI is presented. A theoretical model for solving the focal length of the measured lens from the curvature radius of the wavefront was derived. We also proposed a novel algorithm and subsequently developed a dedicated hardware platform and a corresponding software package for its real-time implementation. Different sets of repeated measurement experiments were carried out for two convex lenses with symmetrical and asymmetrical structures, a large-scale concave lens, and a microlens array, to verify the measurement uncertainty and robustness of the QWLSI measurement system. The expanded uncertainty was also analyzed and determined as 0.31 mm (k = 1.96, normal distribution). The results show that the proposed QWLSI measuring system possesses good performance in measuring the focal lengths of different kinds of lenses and can be widely used in fields such as advanced optics manufacturing. Full article

Objectives: This study aimed to investigate the association between optical coherence tomography angiography (OCTA) parameters and cardiovascular (CV) risk scores in individuals with type 1 diabetes (T1D). Methods: A cross-sectional analysis of a large-scale prospective OCTA trial cohort (ClinicalTrials.gov NCT03422965) was performed. Demographic, systemic, and ocular data—including OCTA imaging—were collected. T1D participants were stratified into three CV risk categories: moderate (MR), high (HR), and very high risk (VHR). Individualized predictions for fatal and non-fatal CV events at 5 and 10 years were calculated using the STENO T1 Risk Engine calculator. Results: A total of 501 individuals (1 eye/patient; 397 T1D, 104 controls) were included. Subjects with MR (n = 37), HR (n = 152) and VHR (n = 208) exhibited significantly reduced vessel density (VD) (20.9 ± 1.3 vs. 20.2 ± 1.6 vs. 19.3 ± 1.8 mm⁻¹, p < 0.05), perfusion density (PD) (0.37 ± 0.02 vs. 0.36 ± 0.02 vs. 0.35 ± 0.02%, p < 0.05) and foveal avascular zone circularity (0.69 ± 0.06 vs. 0.65 ± 0.07 vs. 0.63 ± 0.09, p < 0.05). Statistically significant negative correlations were observed between CV risk and OCTA parameters including VD, PD, and retinal nerve fiber layer thickness, while central macular thickness (CMT) showed a positive correlation (p < 0.05). Notably, CMT was significantly associated with 5-year CV risk. Conclusions: OCTA-derived metrics, particularly reduced retinal VD and PD, are associated with elevated CV risk scores in T1D patients. These findings suggest that OCTA may serve as a valuable non-invasive tool for identifying individuals with increased CV risk scores. Full article

As a core node of the air transportation network, airports rely on aircraft model identification as a key link to support the development of smart aviation. Synthetic Aperture Radar (SAR), with its strong-penetration imaging capabilities, provides high-quality data support for this task. However, the field of SAR image interpretation faces numerous challenges. To address the core challenges in SAR image-based aircraft recognition, including insufficient dataset samples, single-dimensional target features, significant variations in target sizes, and high missed-detection rates for small targets, this study proposed an improved network architecture, SAR-YOLOv8l-ADE. Four modules achieve collaborative optimization: SAR-ACGAN integrates a self-attention mechanism to expand the dataset; SAR-DFE, a parameter-learnable dual-residual module, extracts multidimensional, detailed features; SAR-C2f, a residual module with multi-receptive field fusion, adapts to multi-scale targets; and 4SDC, a four-branch module with adaptive weights, enhances small-target recognition. Experimental results on the fused dataset SAR-Aircraft-EXT show that the mAP₅₀ of the SAR-YOLOv8l-ADE network is 6.1% higher than that of the baseline network YOLOv8l, reaching 96.5%. Notably, its recognition accuracy for small aircraft targets shows a greater improvement, reaching 95.2%. The proposed network outperforms existing methods in terms of recognition accuracy and generalization under complex scenarios, providing technical support for airport management and control, as well as for emergency rescue in smart aviation. Full article

Background/Objectives: Sex-specific differences in metabolic dysfunction-associated fatty liver disease (MAFLD)-related hepatocellular carcinoma (HCC) remain poorly understood. This study aimed to clarify sex-associated disparities in disease progression and recovery using a diethylnitrosamine (DEN) plus Western diet/fructose-induced murine model combined with artificial intelligence (AI)-assisted histological analysis. Methods: Male and female C57BL/6J mice received a single diethylnitrosamine injection and were fed a Western diet/fructose regimen for 38 weeks, followed by an 8-week recovery period on standard chow. Serum biochemical parameters were measured, and liver histology was assessed using second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy. Steatosis and fibrosis were quantified within tumor and adjacent non-tumor regions using AI-based image analysis. Results: Male mice developed more severe disease phenotypes, including greater tumor burden and higher serum alanine aminotransferase levels, compared with females. Following dietary recovery, female mice showed substantial reductions in tumor number and hepatic steatosis, particularly in non-tumor regions; in contrast, male mice demonstrated only minimal improvement. AI-assisted quantification confirmed considerable regression of both steatosis and fibrosis in females and moderate fibrosis improvement in both sexes. Conclusions: These findings indicate sexual dimorphism in the progression and regression of MAFLD-related HCC, with females exhibiting enhanced metabolic and histological recovery. The results underscore the importance of considering sex as a biological variable in preclinical metabolic dysfunction–associated fatty liver disease-related hepatocellular carcinoma research and highlight the value of AI-enhanced imaging for precise, objective evaluation of liver histology. Full article