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We have demonstrated the capability of laboratory propagation-based microtomography (miroCT) in non-destructive 3D virtual histopathology of human blood clots without any contrast agent. The volumetric information are valuable to understand the mechanical properties of clots which are crucial in selecting the most efficient mechanical thrombectomy method for clot extraction. Different clot types retrieved by mechanical thrombectomy from patient victims of acute ischemic stroke were evaluated through propagation-based microCT. The results were correlated with high-resolution scanning electron microscopy (SEM) images, confirming detected cellular and fibrillary structures. Calcifications appeared as glassy opacity areas with relatively intense signal on microCT images, also proved by energy-dispersive spectroscopy and X-ray diffraction. Hyperintense regions on the microCT corresponded to individual or compact aggregates of red blood cells, whereas fibrin dominated volumes appeared at consistently moderate to low normalized microCT values. Red blood cell shapes and sizes are consistent with the SEM observations. Together with other potential parameters, 3D porosity distribution and volume fraction of structures can be easily measured by microCT data. Further development of automated post-processing techniques for X-ray propagation-based micro/nanoCT, also based on machine learning algorithms, can enable high throughput analysis of blood clot composition and their 3D histological features on large sample cohorts.

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PURPOSE: A modified convolution/superposition algorithm is proposed to compute dose from the kilovoltage beams used in IGRT. The algorithm uses material-specific energy deposition kernels instead of water-energy deposition kernels. METHODS: Monte Carlo simulation was used to model the Elekta XVI unit and determine dose deposition characteristics of its kilovoltage beams. The dosimetric results were compared with ion chamber measurements. The dose from the kilovoltage beams was then computed using convolution/superposition along with material-specific energy deposition kernels and compared with Monte Carlo and measurements. The material-specific energy deposition kernels were previously generated using Monte Carlo. RESULTS: The obtained gamma indices (using 2%/2mm criteria for 95% of points) were lower than 1 in almost all instances which indicates good agreement between simulated and measured depth doses and profiles. The comparisons of the algorithm with measurements in a homogeneous solid water slab phantom, and that with Monte Carlo in a head and neck CT dataset produced acceptable results. The calculated point doses were within 4.2% of measurements in the homogeneous phantom. Gamma analysis of the calculated vs. Monte Carlo simulations in the head and neck phantom resulted in 94% of points passing with a 2%/2mm criteria. CONCLUSIONS: The proposed method offers sufficient accuracy in kilovoltage beams dose calculations and has the potential to supplement the conventional megavoltage convolution/superposition algorithms for dose calculations in low energy range.

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PURPOSE: Dose calculation of kilovoltage x rays used in Image-Guided Radiotherapy has been investigated in recent years using various methods. Among these methods are model-based ones that suffer from inaccuracies in high-density materials and at interfaces when used in the kilovoltage energy range. The main reason for this is the use of water energy deposition kernels and simplifications employed such as density scaling in heterogeneous media. The purpose of this study was to produce and characterize material-specific energy deposition kernels, which could be used for dose calculations in this energy range. These kernels will also have utility in dose calculations in superficial radiation therapy and orthovoltage beams utilized in small animal irradiators. METHODS: Water energy deposition kernels with various resolutions; and high-resolution, material-specific energy deposition kernels were generated in the energy range of 10-150 kVp, using the EGSnrc Monte Carlo toolkit. The generated energy deposition kernels were further characterized by calculating the effective depth of penetration, the effective radial distance, and the effective lateral distance. A simple benchmarking of the kernels against Monte Caro calculations has also been performed. RESULTS: There was good agreement with previously reported water kernels, as well as between kernels with different resolution. The evaluation of effective depth of penetration, and radial and laterals distances, defines the relationship between energy, material density, and the shape of the material-specific kernels. The shape of these kernels becomes more forwardly scattered as the energy and material density are increased. The comparison of the dose calculated using the kernels with Monte Carlo provides acceptable results. CONCLUSIONS: Water and material-specific energy deposition kernels in the kilovoltage energy range have been generated, characterized, and compared to previous work. These kernels will have utility in dose calculations in this energy range once algorithms capable of employing them are fully developed.

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BACKGROUND & AIMS: Adipose tissue is a biologically active organ with pro-immunogenic properties. We aimed to evaluate the prognostic value of epicardial adipose tissue (EAT) in COVID-19 and its correlation with other inflammatory biomarkers. MATERIAL AND METHODS: One-hundred patients with COVID-19 were enrolled. C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-CRP ratio (LCR), and platelet-to-lymphocyte ratio (PLR) were evaluated on admission. EAT volume and density were measured by computed tomography. Patients were followed until death or discharge. Univariate and multivariate analysis was performed and ROC curve analysis was used to assess the ability of inflammatory markers in predicting survival. The relationship between EAT and other inflammatory markers was also investigated. RESULTS: The mean ± SD age of patients was 55.5 ± 15.2 years old; 68% were male. Univariate analysis revealed that increased lung involvement, blood urea nitrogen, LDH and NLR, and decreased platelet count were significantly associated with death. After adjustment, LDH was independently predictive of death (OR = 1.013, p-value = 0.03). Among inflammatory markers, LCR had the best ability for predicting survival with 79.7% sensitivity and 64.3% specificity at an optimal cut-off value of 20.8 (AUC = 0.744, 95% CI = 0.612-0.876, p-value = 0.004). EAT volume demonstrated positive correlation with NLR and PLR (p = 0.001 and 0.01), and a negative correlation with LCR (p = 0.02). EAT density was significantly different between decedents and survivors (p = 0.008). CONCLUSION: Routine laboratory tests that represent status of inflammation can be used as cost-effective prognostic markers of COVID-19. Also, the significant association between EAT volume and other inflammatory biomarkers might explain the more severe disease in obese patients.

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Edge-illumination x-ray phase contrast imaging (EI XPCI) is a non-interferometric phase-sensitive method where two absorption masks are employed. These masks are fabricated through a photolithography process followed by electroplating which is challenging in terms of yield as well as time- and cost-effectiveness. We report on the first implementation of EI XPCI with Pt-based metallic glass masks fabricated by an imprinting method. The new tested alloy exhibits good characteristics including high workability beside high x-ray attenuation. The fabrication process is easy and cheap, and can produce large-size masks for high x-ray energies within minutes. Imaging experiments show a good quality phase image, which confirms the potential of these masks to make the EI XPCI technique widely available and affordable.