Search Results (301)

Patients with implanted cardiac devices are a rapidly growing imaging population, and electrocardiogram-gated cardiac computed tomography (CT) is increasingly used to characterize device geometry, multi-device relationships, and dynamic behavior across the cardiac cycle. Cinematic rendering (CR) is a photorealistic three-dimensional (3D) visualization technique for cardiac CT whose established contribution in this population is communicative: it conveys 3D device geometry and material distinctions within a single rendered volume. We describe a demonstrative case series extending CR across the cardiac cycle—time-resolved “4D cine” CR—to depict dynamic device behavior and time-resolved multi-device interaction in a single volume; this is an illustrative technical experience rather than a systematic evaluation of diagnostic performance. Illustrative examples include an EVOQUE transcatheter tricuspid valve rendered together with concurrent surgical mitral and transcatheter aortic valves, a left atrial appendage occlusion device, a normally positioned Impella catheter, and a HeartMate 3 left ventricular assist device (LVAD). Across cases, 4D cine CR feasibility scaled inversely with metallic burden—the aggregate volume and radiodensity of metallic device components within the scan field—with renderings informative for low-metal nitinol and catheter devices but substantially degraded by streak artifact in high-metal LVAD housings. This relationship was observed qualitatively in a small selected series and is offered as an initial observation rather than an established characteristic of the technique. We discuss current limitations and emerging directions such as photon-counting detector CT, metal artifact reduction, and artificial-intelligence-assisted post-processing that may extend 4D cine CR in this population. Full article

The spatial organisation of plasma membrane proteins such as G protein-coupled receptors (GPCRs) plays a critical role in regulating cell signalling, function, and ultimately cell fate. Resolving this organisation requires techniques capable of probing dynamics at the single-molecule level with high spatial and temporal resolution. In this study, we employ the complementary fluorescence fluctuation spectroscopy approaches, Fluorescence Correlation Spectroscopy (FCS), Photon Counting Histogram Analysis (PCH), Raster Image Correlation Spectroscopy (RICS) and Number and Brightness Analysis (N&B), in conjunction with Fluorescence Recovery After Photobleaching (FRAP), to investigate the membrane organisation of the C-X-C chemokine receptor 4 (CXCR4), a GPCR known to undergo ligand-induced reorganisation. At the nanoscale, FCS highlighted opposing effects on diffusion after agonist (CXCL12) and inverse agonist (IT1t) treatment, whilst RICS also showed ligand-mediated changes in particle number. Both single-point and image-based brightness analyses (PCH and N&B) showed increased brightness after CXCL12 treatment, consistent with the pre-internalisation clustering of CXCR4. At the microscale, FRAP showed an increase in immobile CXCR4, not visible to FFS approaches, following CXCL12 stimulation. This integrated approach, performed on a single commercial confocal microscope, provides valuable insight into the reorganisation of CXCR4 in the plasma membrane over a range of temporal and spatial scales, which are not detectable using standard imaging. Full article

Background: Invasive coronary angiography has historically been the reference standard for coronary, valvular, and structural heart disease. Over the past decade, coronary computed tomography angiography (CCTA), CT-derived fractional flow reserve (CT-FFR), photon-counting detector computed tomography (PCCT), and cardiac magnetic resonance (CMR) have expanded the range of clinical questions answerable without an intra-arterial catheter, but this shift has been uneven across clinical domains. Methods: We performed a narrative review and synthesis of randomized trials, registries, society guidelines, and consensus documents (2009–2026) identified through PubMed and major cardiovascular guideline databases, written from a joint cardiology and cardiac-surgical standpoint. Results: The boundary has shifted asymmetrically, by which we mean a domain-dependent rather than uniform displacement of invasive angiography. Non-invasive imaging is now established as the first-line approach for stable chest pain at low-to-moderate pretest probability, for pre-transcatheter aortic valve replacement (TAVR) and structural procedural planning, and for aortic disease. It remains contested for stable multivessel disease and pre-coronary artery bypass grafting (CABG) planning, where CCTA- or CT-FFR-only planning is still investigational. Invasive angiography stays first-line for ST-elevation myocardial infarction (STEMI), cardiogenic shock, and complex percutaneous coronary intervention (PCI), where diagnosis and therapy are inseparable. Conclusions: Invasive and non-invasive modalities are complementary rather than competing. The appropriate first-line investigation depends on the disease domain, pretest probability, anatomical complexity, imaging quality, and whether diagnosis and treatment can be separated. We propose a complexity-stratified, heart-team framework and identify the surgical research gaps that remain. Full article

Cardiovascular and vascular diseases remain major causes of morbidity and mortality worldwide, despite substantial advances in prevention, diagnosis, and treatment. In recent years, cardiovascular imaging has moved beyond the traditional assessment of anatomy and morphology toward a multidimensional evaluation of function, tissue composition, haemodynamics, inflammation, and individualized risk. This evolution has been driven by technological progress in echocardiography, cardiovascular magnetic resonance, computed tomography, nuclear imaging, intravascular imaging, and point-of-care ultrasound, together with the rapid development of artificial intelligence, radiomics, and predictive analytics. Advanced echocardiographic techniques, including contrast stress echocardiography and emerging methods for myocardial scar detection, may improve functional and prognostic assessment in patients with suspected or established coronary artery disease. Cardiac magnetic resonance, through tissue mapping, late gadolinium enhancement, and 4D flow imaging, provides unique information on myocardial fibrosis, perfusion, ventricular remodelling, and vascular haemodynamics. Computed tomography, particularly with the introduction of photon-counting technology, is expanding the non-invasive characterization of coronary plaques, vascular calcification, and thromboembolic disease. Hybrid imaging with PET/CT and PET/MR offers additional insight into vascular inflammation, myocardial metabolism, and active disease processes. At the same time, intravascular ultrasound, optical coherence tomography, and augmented-reality-supported imaging are refining interventional guidance, while point-of-care ultrasound is broadening access to rapid bedside cardiovascular and vascular assessment. The integration of imaging findings with circulating biomarkers, clinical scores, lipid profiles, coagulation parameters, and machine-learning models represents a promising strategy for personalized risk stratification, particularly in complex conditions such as coronary artery disease, venous thromboembolism, pulmonary embolism, and bleeding risk during antithrombotic therapy. This review summarizes current advances in cardiovascular imaging, discusses their translational implications, and highlights future directions for integrating imaging, artificial intelligence, and precision medicine into daily clinical practice. Full article

Cardiac computed tomography (CT) has evolved into a pivotal tool in precision cardiology, enabling comprehensive, non-invasive evaluation of coronary anatomy, plaque composition, vascular function, and inflammation. From calcium scoring to advanced physiological imaging, CT now integrates multiple layers of cardiovascular information within a unified diagnostic framework. Coronary artery calcium (CAC) quantification provides a robust, reproducible measure of atherosclerotic burden and refines risk estimation beyond traditional algorithms, particularly in asymptomatic individuals with an intermediate likelihood. Building upon this anatomical foundation, coronary CT angiography (CCTA) extends evaluation to the anatomical and morphological characterization of coronary artery disease (CAD), identifying both obstructive and non-obstructive plaques with high prognostic accuracy. The addition of CT-derived fractional flow reserve (FFR-CT) and stress perfusion CT (CTP) bridges anatomy and physiology, improving identification of flow-limiting stenoses and guiding revascularization decisions while reducing unnecessary invasive procedures. Beyond luminal assessment, CT-derived biomarkers such as the perivascular fat attenuation index (pFAI) have introduced a new dimension of vascular inflammation imaging, revealing residual risk even in patients without significant stenosis and suggesting novel pathways for individualized therapeutic targeting. Driven by advances in artificial intelligence and photon-counting detector technology, cardiac CT is transitioning from a purely diagnostic modality to an integrative platform for cardiovascular phenotyping. Taken as a whole, this integration of structural, functional, and biological data provides a genuinely holistic view of coronary health. In practical terms, it shifts clinical decision-making from population-based risk models toward precision-guided patient-specific strategies. Full article

Objectives: This study investigates the repeatability and reproducibility of radiomic features extracted from different energy levels of virtual monoenergetic reconstruction (VMER) and polyenergetic reconstruction (PER) obtained with photon-counting computed tomography (PCCT). Methods: Sixteen organic phantoms were scanned twice in a test–retest format using a 120 kV tube potential and tube currents of 10, 50, and 100 mAs. After rotating the phantoms 90° around their z-axis, additional test–retest scans were performed. A PER and 16 VMERs were generated. Segmentation and extraction of 105 original radiomic features followed. The repeatability and reproducibility of these features were assessed using the concordance correlation coefficient (CCC) for agreement and the intraclass correlation coefficient (ICC) for reliability, excluding 14 shape-based features from the analysis. Results: On average, 85 out of 91 radiomic features from VMER showed high repeatability. Approximately 30% of features demonstrated high intra-scan and inter-scan reproducibility when comparing PER and VMER. For different energy levels of VMER, around 78% showed high intra-scan reproducibility, and 74% showed high inter-scan reproducibility. Comparing the average values of test and retest scans in both the initial and rotated states revealed that 65% of features showed high agreement and 73% high reliability for PER, while for VMER, these values were 51% and 55%, respectively. Conclusions: Radiomic features from VMERs showed high test–retest repeatability, whereas reproducibility across reconstruction types and widely separated energy levels was more limited. These findings suggest that energy levels should be carefully standardized when radiomic features are extracted from PCCT-derived VMER images. Full article

The evaluation of stents in coronary computed tomography angiography (CCTA) is still a major topic in cardiovascular imaging. Using Photon-Counting Detector CT (PCCT) may improve the assessment of coronary stents and make on-site CT-FFR analysis feasible for ruling out in-stent restenosis (ISR). In this study, patients with previous coronary stent implantation who underwent CCTA using PCCT and subsequent invasive catheter angiography (ICA) were included. Stent characteristics such as location and length were reported. CT-FFR measurements were taken 1.8 cm before and after the stent, with a value of ≤0.80 defined as hemodynamically significant under respecting the diagnostic accuracy drop in the gray zone between 0.76 and 0.80. Delta CT-FFR with a cut-off value of ≥0.06, indicating hemodynamic significance, was determined. Any ISR and interventional treatment during the following ICA was recorded. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated for post-stent CT-FFR and Delta CT-FFR in detecting ISR. Patients were followed up to evaluate the rate of major adverse cardiovascular events (MACE) 6 months after CCTA. A total of 19 patients (5 female, 14 male, median age 69 years) were enrolled in this study. In most cases, coronary stents were located in the proximal LAD with a median stent length of 70.2 mm. Pathological CT-FFR < 0.76 distal to the stent was detected in 6 cases (31.6%), while pathological Delta CT-FFR ≥ 0.06 occurred in 14 cases (73.7%). ICA was performed in three of these patients, with ISR confirmed in two cases. These findings yield sensitivity and NPV of 100% for both post-stent CT-FFR and Delta CT-FFR for excluding ISR with a superior specificity (76.5% vs. 29.4%) and overall diagnostic accuracy (78.9% vs. 36.8%) for post-stent CT-FFR. Two patients reported a myocardial infarction in follow-up; however, neither of them was located in the territory of the stented coronary artery. This study outlines the feasibility of on-site CT-FFR analysis using PCCT in excluding ISR in coronary stents with a high diagnostic accuracy. These findings highlight the need to extend the benefits of CT-FFR analysis for non-invasive assessment of possible ISR regarding personalized risk stratification and therapy planning. Full article

Single-photon Light Detection and Ranging (LiDAR), which is capable of detecting single-photon signals, has developed rapidly in the field of long-range imaging. Due to the long detection range and limited laser power, the accumulated signal photons of single-photon LiDAR are extremely sparse. Meanwhile, the dark current counts, backscattering noise, and background noise of the single-photon detector are significant, resulting in an extremely low signal-background ratio of the detection data. However, existing algorithms struggle to accomplish the depth reconstruction on data with extremely low signal-to-background ratio (SBR). To address the challenges of complex spatiotemporal correlation and feature sparsity in long-range single-photon imaging depth reconstruction, we design a deep reconstruction algorithm based on a classification formulation, specifically tailored for single-echo detection scenarios. We propose a wavelet denoising preprocessing module and a four-dimensional attention module to learn the spatiotemporal correlations of the photon-counting cube data. Sawtooth-arranged dilated convolutions are utilized during the pixel-wise denoising process to extract sparse features, and non-local total variation regularization combined with cross-entropy is introduced as a joint loss function. For depth reconstruction of data with an SBR of 1:100, the root-mean-square error is less than 0.022 m, which is 66.72% lower than that of the best baseline algorithm. It also achieves promising depth reconstruction results on data with an SBR of 1:300. 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

Bone densitometry in osteoporosis diagnosis via dual-energy X-ray absorptiometry (DXA) can benefit from advances in imaging detector technology. We devised a compact imaging scanner—DXA4A—using a photon-counting and energy-sensitive Timepix4 hybrid pixel detector (512 × 448 pixels, 55 µm pitch), for areal bone mineral density (aBMD) assessments in the distal radius and tibia in the clinic and for future in-flight astronauts’ bone health assessment. We present the design and Monte Carlo simulations of the scanner. A Timepix4 detector with a 1 mm thick CdTe sensor was tested in the laboratory with X-ray tube sources, acquiring first images of test samples. Monte Carlo simulations were implemented for scanner design and performance prediction, using 50 kVp unfiltered and 100 kVp Sm K-edge filtered spectra. With a digital twin of the scanner and patient wrist, we set up a virtual imaging study and determined the aBMD in the forearm of a patient (0.515 ± 0.048 g/cm²), in agreement with the clinical DXA value (0.571 g/cm² for the total forearm). This study highlights the feasibility of realizing a compact DXA scanner for the distal tibia and radius with spectral capabilities, exploiting Timepix4 hybrid detectors for its peculiar energy sensitivity and photon event timing properties for tissue identification. Full article

The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m⁻¹ and 2.0 m⁻¹), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization. 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

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

Background: Coronary computed tomography angiography (cCTA) is now indicated as a non-invasive tool for ruling out obstructive coronary artery disease (O-CAD) in patients who are candidates for transcatheter aortic valve implantation (TAVI) showing low-intermediate pre-test probability of O-CAD. In elderly and comorbid TAVI candidates, the safety and accuracy of cCTA as an alternative to invasive coronary angiography (ICA) for ruling out O-CAD remain to be established. Aim: To assess the feasibility, diagnostic accuracy, and clinical safety of cCTA for ruling out proximal O-CAD in elderly, comorbid, high-risk patients undergoing TAVI. Methods: We conducted a retrospective, single-center study including all consecutive patients with severe symptomatic aortic stenosis who underwent TAVI between January 2019 and December 2020. All patients underwent pre-TAVI cCTA. Patients with positive or non-diagnostic cCTA underwent ICA selectively (ICA group). In patients with no-O-CAD, ICA was omitted and proceeded directly to TAVI (no-ICA group). Accordingly, patients were divided into two groups: no-ICA and ICA group. Clinical follow-up was extended up to 5 years, with assessment of major adverse cardiovascular events (MACEs), mortality, heart failure hospitalizations, and unplanned revascularization. Results: Among 355 patients enrolled, 210 were included in the study. Among them, 140 (66.7%) had negative cCTA for O-CAD, and ICA was safely omitted in 132 patients (62.8%). cCTA was inconclusive in 43 patients (20.5%) and positive in 27 (12.9%). ICA confirmed O-CAD in 53 of 78 patients (67.9%) and PCI was performed in 35 of 53 (66.0%). The accuracy of cCTA for ruling in O-CAD was low (66.28%). During the follow-up period (1513 ± 508 days), the no-ICA group showed comparable outcomes to the ICA group in terms of periprocedural complications and long-term results—at both 1 and 5 years—for MACEs, heart failure hospitalizations, mortality and unplanned revascularization. Outcomes remain comparable between the two groups after performing matched-pair analyses. Conclusions: Our data show that cCTA may provide a reliable, safe, and effective alternative to ICA for ruling out obstructive CAD in elderly patients undergoing TAVI when image quality is diagnostic. A cCTA-based strategy allows deferral of ICA in most cases without compromising procedural safety or long-term clinical outcomes, enabling a personalized and tailored clinical pathway. Whether advanced CT techniques, such as CT-FFR and photon-counting CT, may help refine patient selection for invasive coronary assessment remains to be demonstrated. Full article

Coronary artery calcification, present in 20–30% of percutaneous coronary interventions (PCI), significantly impairs procedural success. Conventional angiography detects calcification in fewer than half of affected cases, while intravascular imaging—though precise—requires lesion crossability that cannot be guaranteed in up to 20% of severely calcified lesions. Cardiac CT (CCT) addresses both constraints by providing comprehensive, three-dimensional calcium characterization before the procedure begins, independent of wire crossability. This review details how specific CCT-derived parameters translate into procedural decisions. Calcium arc, depth, density, and longitudinal distribution each carry distinct implications for device selection: superficial high-density calcium favors atherectomy, while deep concentric patterns are better addressed by intravascular lithotripsy. Validated scoring systems—including the ABCD score—enable objective pre-procedural risk stratification. For chronic total occlusions, bifurcation lesions, ostial stenoses, and very long calcified segments, CCT provides lesion-specific information that supports stepwise strategy selection, equipment preparation, and anticipation of combined modification approaches. Importantly, CCT also identifies anatomical configurations—such as left main bifurcations or tortuous calcified segments—where specific device-related risks warrant particular caution. CCT and intravascular imaging serve complementary roles: CCT defines the strategic framework before the procedure, while intravascular imaging guides real-time execution and optimization. Limitations include operator-dependent interpretation, the absence of standardized protocols for translating calcium morphology into device selection, and the need to validate established Hounsfield unit thresholds in emerging photon-counting CT systems. Prospective randomized evidence comparing CCT-guided and intravascular imaging-guided strategies remains limited but is anticipated from ongoing trials. Full article