Search Results (1,198)

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

We investigate a static and spherically symmetric Schwarzschild–Letelier Black Hole immersed in a King Dark Matter Halo and analyze how the combined effects of the cloud of strings and the dark-matter environment modify the spacetime geometry, particle dynamics, and thermodynamic behavior of the black hole. Particular attention is devoted to the motion of both massless photons and massive test particles in this black hole background. In the geodesic analysis, we derive the effective potential and study the properties of circular photon orbits, the associated black-hole shadow radius, and the innermost stable circular orbit (ISCO), highlighting the role played by the cloud of strings parameter and the King dark-matter halo parameters in shifting the orbital structure relative to the standard Schwarzschild case. To further characterize the spacetime from a topological perspective, we investigate the unstable circular null orbit using a normalized vector field constructed within the framework of Duan’s $\varphi$-Mapping Topological Current Theory. Through this method, we identify the corresponding topological charge and examine the relation between the photon sphere and the underlying topological structure of the black-hole configuration. In addition, we explore the thermodynamic properties of the system by computing the Hawking temperature, entropy, Helmholtz free energy, and heat capacity, thereby analyzing the black hole’s local and global thermodynamic stability. The influence of the surrounding dark-matter halo and cloud of strings on the phase structure and thermal behavior is discussed in detail. We further study the thermodynamic topology of the system via the off-shell free-energy formalism, which provides insight into possible thermodynamic phase transitions and the topological classification of black-hole states. Our analysis demonstrates that the combined effects of the cloud of strings and the King dark-matter halo significantly modify the horizon structure, geodesic dynamics, shadow characteristics, and thermodynamic properties of the black hole when compared with the standard Schwarzschild solution. Full article

Background/Objectives: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used for non-invasive assessment of coronary artery disease under stress and rest conditions. Although deep learning has shown promise for automated SPECT MPI interpretation, most studies focus on single-tracer datasets and do not explicitly account for tracer-dependent variability. This study developed and evaluated a multi-task deep learning framework with tracer-specific prediction heads for patient-level SPECT MPI classification. Methods: A convolutional neural network with a shared feature encoder and tracer-specific heads was implemented using polar map representations from technetium-99m (Tc-99m) and thallium-201 (Tl-201) studies. Transfer learning from ImageNet was applied. Stress-only, rest-only, and dual-input configurations were evaluated using repeated patient-stratified cross-validation and independent testing. Performance was assessed using ROC-AUC and balanced accuracy. Results: For Tc-99m normal versus abnormal perfusion classification, the stress-only model achieved the highest cross-validation AUC (0.88 ± 0.067) and test AUC of 0.88 [0.67–0.99]. For Tl-201 low-risk versus intermediate/high-risk classification, stress-based models achieved the highest cross-validation AUC (0.88 ± 0.051) and test AUC of 0.80 [0.71–0.89], comparable to dual-input models. In both tracer-specific tasks, stress-phase information showed favorable performance, but the endpoints differed and should be interpreted separately. Conclusions: Stress-phase polar maps provided strong discriminative information within this single-center cohort. These findings should be interpreted in a tracer- and task-specific manner supporting stress-phase imaging as an informative input for AI-based SPECT MPI classification while underscoring the need for external validation before broader clinical generalization. Full article

Processing-in-memory and in-memory computing (PIM/IMC) are increasingly explored to mitigate the von Neumann data-movement bottleneck that limits deep neural network (DNN) performance and energy efficiency. Progress, however, remains fragmented across device substrates, architectural prototypes, mapping and scheduling methods, compiler toolchains, and benchmarking practices, making results hard to compare and slowing deployment. This survey synthesizes developments from 2019–2025 along four coupled axes: (i) memory substrates and architectural design, (ii) mapping, partitioning, and scheduling, including learning- and graph-based strategies, (iii) compilers and end-to-end deployment flows, and (iv) benchmarking datasets, metrics, and reporting norms. Drawing on over twenty representative platforms spanning static random-access memory (SRAM) and dynamic random-access memory (DRAM), emerging non-volatile, capacitive, and photonic substrates, we clarify the trade-offs separating analog/charge-domain IMC from digital SRAM/DRAM-centric PIM, including reported peaks up to 600 TOPS/W and 1.5 TOPS/mm². We organize mapping frameworks into a unified reference taxonomy, identify recurrent evaluation pitfalls that undermine reproducibility, and highlight persistent gaps in training support, robustness under non-idealities, and coverage of large-scale GNN workloads. Finally, we outline a five-phase roadmap from benchmark standardization to industrial validation toward compiler-integrated, GNN-informed PIM/IMC systems validated on production-scale workloads. Full article

Introduction: Detection of in-stent restenosis by cardiac CT is challenging due to blooming artifacts. The technological progress of CT scanners and especially the recent introduction of photon-counting detectors (PCDs) has led to an improvement in image quality. Several studies have analyzed the lumen visibility of coronary stents, but most studies used models which did not simulate cardiac movement. In this study we use a pulsatile heart model to simulate a heartbeat to analyze the effects of cardiac motion on image quality. Methods: Seventeen different coronary stents with an outer diameter of 3.0 mm were implanted into polyolefin tubes. The tubes were then filled with diluted contrast medium and attached to the pulsatile heart model. The stents were scanned in a third-generation dual-source CT with an energy-integrating detector (EID) and a first-generation PCD CT. Results: In motion, the mean visible stent lumen was reduced from 64.4% to 59.4% in EID CT, from 61.4% to 56.0% in PCD CT using the Bv60 kernel, and from 72.9% to 62.9% in PCD CT using the Bv72 kernel, each in standard resolution mode. Employing the ultra-high-resolution mode (UHR), stent lumen visibility was reduced from 61.3% to 57.9% with the Bv60 kernel and from 71.7% to 61.8% with the Bv72 kernel. The difference between static imaging and motion was significant in each instance (p < 0.001). Conclusions: While PCD CT and the use of sharper kernels improves the image quality in comparison with EID CT and smoother kernels, the impact of cardiac motion on the reduction in stent lumen visibility is substantial. Hence, the best image quality is achieved in patients with a normal and regular heart rate. If this is not possible to achieve, a retrospective acquisition mode should be considered. Full article

Dicarbadodecaboranes (12) (carboranes) are versatile molecular building blocks with unique properties, which allow the expansion of classical medicinal-chemical space. To enable single-photon emission computed tomography (SPECT) imaging of cyclooxygenase-2 (COX-2), we investigated the feasibility of introducing iodine-123 into nido-indoborin 1, a nido-carborane analog of indomethacin with potent and selective cyclooxygenase-2 inhibitory activity. An electrophilic iodination strategy afforded two regioisomers, 2a and 2b, bearing the iodine at the carborane cluster. Compared to nido-indoborin, a reduced COX-2 inhibition potency and selectivity were observed, with 2b exhibiting the more favorable inhibition profile. Radiosynthesis of [¹²³I]2b was achieved by N-chlorosuccinimide–mediated electrophilic substitution of 1, and conditions were optimized, leading to an isolated radiochemical yield of 4%. While the radiotracer displayed high stability in phosphate buffer, ester hydrolysis was observed in human plasma and murine liver microsomes with no significant deiodination in vitro. Cell uptake studies indicated partial COX-2–dependent accumulation but also revealed substantial non-specific uptake and unexpected enhancement of radiotracer uptake in the presence of carborane-based blocking agents. In vivo pilot imaging studies in mice bearing U87 xenografts showed renal and hepatobiliary clearance without measurable tumor accumulation but evidence of deiodination over time. Overall, iodination was feasible, but the resulting compounds lacked the required COX-2-selective tumor accumulation for further radiotracer development. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

Introduction: Progressive supranuclear palsy (PSP) is an atypical Parkinsonian disorder characterized by a range of clinical phenotypes, reflecting its multiple subtypes. As a result, accurate diagnosis during life remains challenging, underscoring the need for reliable biomarkers. The present narrative review aims to evaluate whether dopamine transporter single-photon emission computed tomography (DaT-SPECT) can serve as a biomarker in the assessment of PSP. Methods: The database search identified 31 original research articles relevant to our study objective. Of these, 17 studies included PSP patients and utilized DaT-SPECT as the sole molecular imaging modality; 9 studies combined DaT-SPECT with at least one additional molecular imaging technique; and 5 studies integrated DaT-SPECT with a laboratory-based biomarker of neurodegenerative disease. Results: DaT-SPECT appears to demonstrate low specificity and variable sensitivity for PSP across studies. Discussion: Combining DaT-SPECT with other diagnostic biomarkers, especially brain magnetic resonance imaging and other nuclear imaging modalities, may improve diagnostic accuracy, especially given its relatively low specificity for PSP. Nevertheless, these initially promising findings need to be validated in large, multicenter studies that include and clearly define multiple, autopsy-confirmed PSP subtypes. Full article

Background/Objectives: Photon-counting computed tomography (PCCT) combined with radiomics enables advanced myocardial tissue characterization beyond conventional imaging. This study investigated whether myocardial radiomic features derived from PCCT are associated with nicotine status in patients without coronary artery disease. Methods: In this retrospective, single-center study, 104 patients (38 men, 66 women; median age 54 years) without coronary calcification (Agatston score = 0) underwent cardiac PCCT. Myocardial septal thickness was measured at three points during the 65–70% cardiac phase. Myocardial tissue was manually segmented, and 105 radiomic features were extracted. After correlation-based feature reduction, 45 independent features were used for analysis. Patients were categorized based on nicotine status. Machine learning models, including logistic regression, random forest, and gradient boosting, were trained and evaluated using stratified five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC) and additional classification metrics. Results: No significant differences in myocardial septal thickness were observed between smokers and non-smokers (p > 0.05). However, radiomic features enabled moderate discrimination between smokers and non-smokers. Logistic regression with L2 regularization achieved the best performance (ROC-AUC 0.66, balanced accuracy 0.67), outperforming random forest and gradient boosting models. The most relevant radiomic features primarily comprised higher-order texture and shape-based parameters associated with spatial gray-level heterogeneity and subtle variations in myocardial tissue architecture. Conclusions: PCCT-based radiomics may capture subtle myocardial imaging signatures associated with smoking status, even in the absence of structural changes detectable by conventional metrics. These findings highlight the potential of cardiac radiomics as a non-invasive imaging biomarker for early cardiovascular risk assessment and support its integration into advanced cardiac imaging workflows. Future multicenter studies with larger cohorts, external validation, and multimodal correlation are warranted to improve robustness and facilitate clinical translation. Full article

Background/Objectives: Revascularization of chronic total occlusions (CTO) in patients with heart failure and reduced ejection fraction (HFrEF) remains controversial, as randomized trials have not demonstrated a clear prognostic benefit. Methods: We present an imaging-guided case series of patients with ischemic HFrEF who underwent CTO percutaneous coronary intervention (PCI) following myocardial viability assessment using single-photon emission computed tomography (SPECT). Contemporary antegrade and retrograde techniques were employed. Results: At 6- and 12-month follow-ups, all patients demonstrated marked improvement in NYHA (New York Heart Association) functional class, significant reductions in NT-proBNP (N-terminal pro-brain natriuretic peptide) levels, and substantial improvement in quality of life assessed by the Minnesota Living with Heart Failure Questionnaire (MLHFQ). These benefits occurred despite only modest improvement in left ventricular (LV) ejection fraction (EF) and limited reverse remodeling. SPECT enabled identification of viable but ischemic myocardium, supporting individualized revascularization decisions. Conclusions: In selected high-risk patients with ischemic HFrEF, CTO-PCI was associated with meaningful clinical and biomarker improvement independent of substantial EF recovery. Careful patient selection, incorporating myocardial viability assessment, may refine individualized clinical decision-making in selected patients. These findings support an imaging-guided approach and warrant further prospective evaluation. Full article

Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes providing measurable biomarkers for longitudinal and translational studies. This review systematically analyzes 60 studies published between 2015 and 2025, covering major imaging modalities including Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems across murine and zebrafish models. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key comparative findings indicate that variability sources can be broadly categorized into technical (instrumentation, reconstruction, calibration) and biological (physiological heterogeneity, model-specific factors), with their interaction governing overall measurement uncertainty. Emerging computational approaches, including parametric modeling and artificial intelligence–assisted pipelines, show potential in reducing variance and improving parameter stability, although they introduce additional dependencies requiring validation. Collectively, this review frames quantitative preclinical imaging as a metrological discipline, emphasizing that reproducibility, bias control, and cross-modality harmonization are critical for generating robust and translationally relevant imaging biomarkers. Full article

Metasurfaces exploit subwavelength scattering elements to manipulate light with a level of flexibility that is difficult to achieve using conventional optical platforms, making them promising building blocks for next-generation photonic systems. Yet the increasing dimensionality of metasurface design spaces and the demand for multifunctional responses have exposed the limitations of traditional intuition-led design approaches. In this Review, we survey the emergence of artificial intelligence (AI)-empowered metasurfaces across three major themes: inverse design, multidimensional optical-field control, and free-space optical computing. We first summarize the fundamental principle of optical field manipulation and the algorithmic approaches to metasurface design, including stochastic optimization, deep neural networks, and generative models, with emphasis on their capabilities in rapid performance prediction and inverse structural discovery. We next discuss artificial intelligence-assisted strategies for engineering multiple spatial, spectral, and polarization degrees of freedom in free space. We then highlight the role of AI-empowered metasurface architectures in optical information processing and computation. Together, these developments point to a powerful framework for integrating machine intelligence with meta-optics, with implications for autonomous photonic systems and high-capacity optical computing. Full article

The modern power system is characterized by large-scale networks, diverse types of sources and loads, and complex grid structures. Virtual Power Plants (VPPs) are proposed to address the operation problem after the integration of Distributed Energy Resources (DERs). Optimization problems in the VPP operation are predominantly mixed-integer programming (MIP) problems belonging to the class of NP-hard problems, motivating the application of quantum computers. Focusing on the VPP optimal dispatch problem under wind and solar uncertainty, we employ the Model Predictive Control (MPC) framework to conduct the VPP intraday rolling dispatch. The classical model and the Quadratic Unconstrained Binary Optimization (QUBO) model for the MPC-based intraday rolling dispatch problem are formulated, respectively. The QUBO formulation of the VPP dispatch problem renders it directly solvable by a specialized quantum computer based on dissipative optical systems: the Coherent Ising Machine (CIM). Compared with the benchmark classical solvers, the experimental results demonstrate the significant computational time reduction capability of CIM. Specifically, compared to Gurobi, Simulated Annealing and Tabu Search, the CIM achieves relative computational time reductions of $75.25\%$, $99.95\%$ and $99.96\%$, respectively, while maintaining competitive solution quality. Our work demonstrates the applicability of CIM and its acceleration potential in VPP intraday rolling dispatch, paving the way for the practical application of specialized photonic quantum computers in smart grids. Full article

To address the challenge of accurately reconstructing multi-depth targets using single-photon Light Detection and Ranging (LiDAR) under few-frame conditions in high-precision applications such as autonomous driving perception, remote sensing, and military reconnaissance, this paper proposes a computational imaging method named the Jensen–Shannon Divergence Weighted Pixel Fusion Constant False Alarm Rate (JSWPF-CFAR) approach. First, the proposed method utilizes the Jensen–Shannon (JS) divergence to characterize the statistical similarity between adjacent pixels, thereby constructing adaptive weights to achieve the effective fusion of echo signals. The key innovation lies in the formulation of a JS divergence-based weighting factor, which fully exploits the inherent spatial correlation within 3D target structures to optimize the pixel fusion process and enhance the signal statistics of target echoes. Subsequently, a CFAR detection model tailored for Geiger-mode Avalanche Photodiode (GM-APD) multi-depth echo signals is constructed to estimate the noise photon count within a local sliding window; this estimate is then used to calculate a photon counting threshold for identifying and extracting high-confidence target intervals. Finally, a peak-picking method is employed to perform the 3D reconstruction of multi-depth targets. Compared with existing techniques such as matched filtering and Reversible Jump Markov Chain Monte Carlo (RJMCMC), the proposed method exhibits superior reconstruction quality under few-frame and low Signal-to-Background Ratio (SBR) conditions. The experimental results demonstrate that the proposed method achieves an improvement in target restoration degree (RD) of at least 21.16% and a relative variance (Var) optimization of at least 62.90% over the matched filtering and RJMCMC baselines. These results indicate that the proposed approach effectively enhances the multi-depth estimation performance of single-photon LiDAR in complex scenes. Full article

To address the low qubit efficiency and high user-side operational complexity in existing quantum key agreement schemes, this paper proposes a high-efficiency and lightweight quantum key agreement scheme based on Bell states. The scheme is constructed upon a time-reversed EPR architecture, in which a quantum server performs entangled-state preparation and Bell state measurement. Furthermore, a bidirectional decoy photon mechanism is incorporated into the architecture to achieve eavesdropping detection. By exploiting the completeness and orthogonality of the Bell basis, the scheme introduces an encoding mechanism based on local Pauli operations, enabling a single Bell state to carry 2 bits of key information and thereby realizing dense coding, which improves qubit efficiency. Meanwhile, users are only required to perform single-qubit operations, which reduces the quantum operational requirements on the user side. Based on the properties of Bell states, this paper derives the mapping relationship between local Pauli operations and Bell state measurement outcomes. Experimental results on the SpinQ Gemini quantum computing platform are consistent with the theoretical analysis, verifying the feasibility of the proposed scheme. In addition, security analysis shows that, owing to the bidirectional decoy photon mechanism, the scheme can resist various quantum attacks. The proposed scheme combines high efficiency with a lightweight implementation, reducing quantum hardware requirements on the user side and network deployment costs, thereby providing a cost-effective solution for practical quantum key agreement. Full article