Search Results (175)

Introduction: This study evaluated the performance of automated dual-energy computed tomography (DECT)-based quantification of perfusion defects (PDs) in acute pulmonary embolism and examined its correlation with clinical parameters. Methods: We retrospectively analyzed data from 171 patients treated for moderate-to-severe acute pulmonary embolism, who underwent DECT imaging at two separate time points. PDs were quantified using a fully automated AI-based segmentation method that relied exclusively on iodine perfusion maps. This was compared with a semi-automatic clinician-guided segmentation, where radiologists manually adjusted thresholds to eliminate artifacts. Clinical variables including the Miller obstruction score, right-to-left ventricular diameter ratio, oxygen saturation, and patient-reported symptoms were also collected. Results: The semiautomatic method demonstrated stronger correlations with embolic burden (Miller score; r = 0.4, p < 0.001 at follow-up) and a negative correlation with oxygen saturation (r = −0.2, p = 0.04). In contrast, the fully automated AI-based quantification consistently produced lower PD values and demonstrated weaker associations with clinical parameters. Conclusions: Semiautomatic quantification of PDs currently provides superior accuracy and clinical relevance for evaluating lung PDs in acute pulmonary embolism. Future multimodal AI models that incorporate both anatomical and clinical data may further enhance diagnostic precision. Full article

To explore the potential seed compounds from natural products as anticancer agents against small-cell lung cancer (SCLC), the underground parts of Agapanthus africanus, a plant commonly used for ornamental purposes, were investigated. Three spirostan-type steroidal glycosides (1–3) were isolated and identified by nuclear magnetic resonance spectral analysis. Compounds 1–3 exhibited cytotoxicity against SBC-3 human SCLC cells, with IC₅₀ values of 0.56, 1.4, and 7.4 µM, respectively. Compound 1, also known an agapanthussaponin A, demonstrated the most potent cytotoxicity among the isolated compounds and was evaluated for its apoptosis- and ferroptosis-inducing activities. Compound 1 arrested the cell cycle of SBC-3 cells in the G₂/M phase and induced apoptosis primarily via the mitochondrial pathway, characterized by caspases-3 and -9 activation, loss of mitochondrial membrane potential, and overproduction of reactive oxygen species. Additionally, 1 triggered ferroptosis via a dual mechanism consisting of enhanced cellular iron uptake through upregulation of transferrin and transferrin receptor 1 expression and impaired glutathione synthesis via downregulation of both xCT and glutathione peroxidase 4 expression. Compound 1 induces cell death via the apoptosis and ferroptosis pathways, suggesting its promise as a seed compound for the development of anticancer therapeutics against SCLC. Full article

Advances in plaque imaging have transformed cardiovascular diagnostics through detailed characterization of atherosclerotic plaques beyond traditional stenosis assessment. This review outlines the clinical applications of varying modalities, including dual-layer spectral CT, photon-counting CT, dual-energy CT, and CT-derived fractional flow reserve (CT-FFR). These technologies offer improved spatial resolution, tissue differentiation, and functional assessment of coronary lesions. Additionally, artificial intelligence has emerged as a powerful tool to automate plaque detection, quantify burden, and refine risk prediction. Collectively, these innovations provide a more comprehensive approach to coronary artery disease evaluation and support personalized management strategies. Full article

Background/Objectives: Although MRI with fat-suppression sequences is the gold standard for diagnosis of bone marrow edema (BME), Dual-Layer Spectral CT (DL-SCT) with electron density (ED) provides a viable alternative, particularly in situations where an MRI is not accessible. Using MRI as the reference standard, this study analyzed how DL-SCT with ED reconstructions may be a valid alternative in the detection of BME. Methods: This retrospective study included 28 patients with a suspected diagnosis of BME via MRI conducted between March and September 2024. Patients underwent DL-SCT using ED reconstructions obtained through IntelliSpace software v. 12.1. Images were evaluated by two experienced radiologists and one young radiologist in a blinded way, giving a grade from 0 to 3 to classify BME (0 absence; 1 mild; 2 moderate; 3 severe). To reduce the recall bias effect, the order of image evaluations was set differently for each reader. p-Values were considered significant when <0.05. Fleiss’ Kappa was used to assess inter-rater reliability: agreement was considered poor for k < 0; slight for k 0.01–0.20; fair for 0.21–0.40; moderate for 0.41–0.60; substantial for 0.61–0.80; and almost perfect for 0.81–1.00. Results: All the readers detected the presence or absence of BME using DL-SCT. Inter-rater reliability for grade 0 resulted in 1 (p-value < 0.001); for grade 1: 0.21 (p-value < 0.001); for grade 2: 0.197 (p-value < 0.001); and for grade 3: 0.515 (p-value < 0.001). Conclusions: ED reconstructions allowed the identification of BME presence or absence in all analyzed cases, thus suggesting DL-SCT as a potentially effective method for its detection. Full article

Spectral computed tomography (SCT) enables material decomposition, artifact reduction, and contrast enhancement, leveraging symmetry principles across its technical framework to enhance material differentiation and image quality. However, its nonlinear data acquisition process involving noise and scatter leads to a highly ill-posed inverse problem. To address this, we propose a dual-domain iterative reconstruction network that combines joint learning reconstruction with physical process modeling, which also uses the symmetric complementary properties of the two domains for optimization. A dedicated physical module models the SCT forward process to ensure stability and accuracy, while a residual-to-residual strategy reduces the computational burden of model-based iterative reconstruction (MBIR). Our method, which won the AAPM DL-Spectral CT Challenge, achieves high-accuracy material decomposition. Extensive evaluations also demonstrate its robustness under varying noise levels, confirming the method’s generalizability. This integrated approach effectively combines the strengths of physical modeling, MBIR, and deep learning. Full article

Beyond clinical areas, photon-counting detector (PCD) CT is innovatively applied to study paleontological specimens. This study presents a preliminary investigation into the application of PCD-CT for imaging large dinosaur fossils, comparing it with standard energy-integrating detector (EID) CT. The left dentary of Tyrannosaurus and the skull of Camarasaurus were imaged using PCD-CT in ultra-high-resolution mode and EID-CT. The PCD-CT and EID-CT image quality of the dinosaurs were visually assessed. Compared with EID-CT, PCD-CT yielded higher-resolution anatomical images free of image deterioration, achieving a better definition of the Tyrannosaurus mandibular canal and the three semicircular canals of Camarasaurus. PCD-CT clearly depicts the internal structure and morphology of large dinosaur fossils without damaging them and also provides spectral information, thus allowing researchers to gain insights into fossil mineral composition and the preservation state in the future. Full article

Objective: This study aimed to investigate the structural, vascular, and biochemical alterations in patients with pseudoexfoliation syndrome (PES) and pseudoexfoliative glaucoma (PXG) and to evaluate the associations between serum biomarkers, the retinal nerve fiber layer (RNFL), choroidal thickness (CT), and vessel density (VD) in these groups. Methods: All subjects underwent spectral-domain optical coherence tomography (SD-OCT) and OCT angiography (OCTA) to assess RNFL thickness, CT, and VD. Serum levels of inflammatory and oxidative stress biomarkers—including malondialdehyde (MDA), glutathione (GSH), interleukin-6 (IL-6), nitric oxide (NO), inducible NO synthase (iNOS), galectin-3, and SCUBE-1—were analyzed, and regression and ROC curve analyses were performed to evaluate predictive value and diagnostic performance. Results: A total of 80 patients were included and are listed as follows: 25 controls, 30 with PES, and 25 with PXG. There were no significant differences among groups in terms of age or gender. RNFL thickness, CT, and VD were significantly reduced in the PXG group compared to the PES and control groups (p < 0.001). PXG patients showed the most pronounced reductions in both peripapillary and macular CT, as well as superficial and deep VD. Serum iNOS, SCUBE-1, galectin-3, and MDA levels were significantly elevated in PXG, while GSH levels were lower (p < 0.001); NO levels showed no significant differences. In the PES and PXG groups, several ocular parameters correlated significantly with serum biomarkers, particularly iNOS, MDA, and GSH. Regression analysis in PXG patients identified iNOS and MDA as significant predictors of RNFL thickness and VD. ROC analysis demonstrated that MDA and GSH exhibited the highest diagnostic accuracy among the tested biomarkers for distinguishing PXG patients from controls. Conclusions: PXG is associated with significant structural, vascular, and biochemical alterations, including reduced RNFL thickness, choroidal thinning, and decreased VD. Altered serum levels of MDA and GSH were significantly associated with these ocular changes and demonstrated the highest diagnostic accuracy among the biomarkers evaluated. These findings support their potential utility as non-invasive biomarkers for distinguishing PXG from PES and healthy controls and for monitoring disease progression. Full article

Radiomics involves extracting quantitative features from medical images, resulting in high-dimensional data. Unsupervised clustering has been used to discover patterns in radiomic features, potentially yielding hidden biological insights. However, its effectiveness depends on the selection of dimensionality reduction techniques, clustering methods, and hyperparameter optimization, an area with limited exploration in the literature. We present a novel bootstrapping-based hyperparameter search approach to optimize clustering efficacy, treating dimensionality reduction and clustering as a connected process chain. The hyperparameter search was guided by the Adjusted Rand Index (ARI) and Davies–Bouldin Index (DBI) within a bootstrapping framework of 100 iterations. The cluster assignments were generated through 10-fold cross-validation, and a grid search strategy was used to explore hyperparameter combinations. We evaluated ten unsupervised learning pipelines using both simulation studies and real-world radiomics data derived from multiphase CT images of renal cell carcinoma. In simulations, we found that Non-negative Matrix Factorization (NMF) and Spectral Clustering outperformed the traditional Principal Component Analysis (PCA)-based approach. The best-performing pipeline (NMF followed by K-means clustering) successfully identified all three simulated clusters, achieving a Cramér’s V of 0.9. The simulation also established a reference framework for understanding the concordance patterns among different pipelines under varying strengths of clustering effects. High concordance reflects strong clustering. In the real-world data application, we observed a moderate clustering effect, which aligned with the weak associations to clinical outcomes, as indicated by the highest AUROC of 0.63. Full article

Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. To address this issue, an algorithm for spectral CT reconstruction based on photon-counting detectors is proposed, which combines Kronecker-Basis-Representation (KBR) tensor decomposition and total variational (TV) regularization (namely KBR-TV). The proposed algorithm uses KBR tensor decomposition to unify the sparse measurements of traditional tensor spaces, and constructs a third-order tensor cube through non-local image similarity matching. At the same time, the TV regularization term is introduced into the independent energy spectrum image domain to enhance the sparsity constraint of single-channel images, effectively reduce artifacts, and improve the accuracy of image reconstruction. The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. The ultimate findings indicate that the KBR-TV method offers superior enhancement in the quality of spectral CT images in comparison to several existing methods. Full article

Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral decomposition, whereas UNet excels in the segmentation detail restoration and complexity boundary processing by virtue of its coding-decoding structure and the hopping connection mechanism. In this paper, an undisturbed soil column was collected in Hainan Province, China, which was classified as Ferralsols (FAO/UNESCO), and CT scans were utilized to acquire high-resolution images and generate high-quality datasets suitable for deep learning through preprocessing operations such as fixed-layer sampling, cropping, and enhancement. The results show that MMLFR-UNet outperforms UNet and traditional methods (e.g., Otsu and Fuzzy C-Means (FCM)) in terms of Intersection over Union (IoU), Dice Similarity Coefficients (DSC), Pixel Accuracy (PA), and boundary similarity. Notably, this model exhibits exceptional robustness and precision in segmentation tasks involving complex pore structures and low-contrast images. Full article

We report the synthesis and characterization of five novel metal complexes. Three of them are vanadium complexes with the general formula [VO(Lⁿ)₂], where Lⁿ are Schiff bases derived from the condensation of 2-carbaldehyde-8-hydroxyquinoline with either 4-(2-aminoethyl)morpholine (L¹), 3-morpholinopropylamine (L²) or 1-(2-aminoethyl)piperidine (L³). The two other metal complexes are [Ni(L¹)₂] and [Fe(L¹)₂]Cl. They were characterized by analytical, spectroscopic (Fourier transform infrared, UV-visible absorption), and mass spectrometric techniques as well as by single-crystal X-ray diffraction (for all [VO(Lⁿ)₂] complexes and [Ni(L¹)₂]). While, in the crystal structure, the V(IV)O complexes show distorted square–pyramidal geometry with the ligands bound as bidentate through quinolate NO donors, the Ni(II) complex shows octahedral geometry with two ligand molecules coordinated through NNO donors. Stability studies in aqueous media revealed that the vanadium complexes are not stable, undergoing oxidation to VO₂(L), which was corroborated by ⁵¹V NMR and MS. This behavior is also observed in organic media, though at a significantly slower rate. The Ni complex exhibited small spectral changes over time in aqueous media. Nonetheless, all compounds show enhanced stability in the presence of bovine serum albumin (BSA). Fluorescence studies carried out for the Ni(II) and Fe(III) complexes indicate reversible binding to albumin. The cytotoxicity of the L¹ metal complexes was assessed on melanoma (B16F10 and A375) and colon cancer (CT-26 and HCT-116) cell lines, with 5-fluorouracil (5-FU) as a reference drug. The V- and Ni complexes showed the lowest IC₅₀ values (<10 μM) in either A375 or HCT-116 cells after 48 h of incubation, while the Fe(III) complex presented minimal antiproliferative effects. The complexes were generally more cytotoxic to human than murine cancer cells. Synergistic in vitro studies with 5-FU revealed antagonism in most cases, except in A375 cells, where an additive effect was observed for the combination with the V-complex. Overall, these compounds show promising potential for cancer treatment, mostly for melanoma. Full article

Shadow in remote sensing images can obscure important details of land features, making shadow detection crucial for enhancing the accuracy of subsequent analyses and applications. Current shadow detection methods primarily rely on the spectral information of images, which can often result in shadow misdetection due to the phenomenon of spectral confusion of different objects. To mitigate this issue, we propose a method that combines topography and spectra (CTS). Firstly, we introduce a new DEM-based shadow coarse detection method to obtain the DEM rough shadow mask, which uses a relationship between the magnitude of terrain height angle and solar elevation angle to determine shadow properties. Then, we use the MC3 (modified C3 component) index-based shadow fine detection method to obtain an MC3 mean map, which includes image enhancement with a stretching process and multi-scale superpixel segmentation. We then derive the Shadow pixel Proportion Map (SPM) by counting the DEM rough shadow mask in terms of superpixels. The Joint Shadow probability Map (JSM) is obtained by combining the SPM and the MC3 mean map with specific weights. Finally, a multi-level Otsu threshold method is applied to the JSM to generate the shadow mask. We compare the proposed CTS method against several state-of-the-art algorithms through both qualitative assessments and quantitative metrics. The results show that the CTS method demonstrates superior accuracy and consistency in detecting true shadows, achieving an average overall accuracy of 95.81% on mountainous remote sensing images. Full article

Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity. Full article

Background: Pulmonary hypertension (PH) is characterized by elevated pressure in the pulmonary artery. Currently, most dual-energy CT (DECT) research focuses on the application of iodine mapping in pulmonary embolism. However, little attention is paid to the parametric mapping of the lung parenchyma of PH. Methods: In total, 156 cases undergoing thoracic DECT from 2021 August to 2023 February were surveyed. For each case, the iodine density (Iod) and effective atomic number (Zeff) of four different levels of the lung, along with the iodine density of the pulmonary artery and aorta, were measured. The measured parameters and their derivatives were compared between PH cases and normal controls and between chronic thromboembolic PH (CTEPH) and non-CTEPH cases. Results: Region of interest (ROI)-Zeff was statistically lower in the PH group as compared to the normal controls on each level. The ratio of PA-iod/ROI-iod was significantly higher in the PH group than in the normal controls. ROI-iod was statistically lower in the CTEPH cases as compared with the non-CTEPH cases on each level. The CTEPH cases demonstrated a higher PA-iod/ROI-iod value as compared with the non-CTEPH cases. Conclusions: The PA-iodine density and effective Z of spectrum CT could serve as valuable imaging parameters for the diagnosis and characterization of PH and CTEPH. Full article

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis. Full article