Histogram Of Number Research Articles

Quantitative characterization and analysis of pore-fractures in tar-rich coal under high-temperature pyrolysis based on micro-CT imaging

This study investigates pore distribution and permeability behavior of tar-rich coal following high-temperature pyrolysis at 500°C using X-ray computed tomography (CT) scanning. Coal samples post-pyrolysis were CT scanned, generating 1755 cross-sectional slices for three-dimensional reconstruction. An axial algorithm extracted pore distribution features, and geometric parameters were computed. An Equivalent Pore Network Model analyzed permeability characteristics. The results show that Post-pyrolysis pore distribution in tar-rich coal exhibited nonuniformity with a significant range in pore size distribution. Pores displayed concentrated spatial patterns. Total porosity was 14.24%, with 12.34% being connected. Most pores in Representative Elementary Volume (REV) regions fell within 10–50 μm in width and 20–60 μm in length, constituting over 40% of the total. Pore surface area peaked between 200–100 μm2, also comprising over 40% of the total. The Pore Network Model showed distinct characteristics in two REV regions: REV-1 demonstrated an early stage of development with poor connectivity, while REV-2 displayed a well-developed network with a bimodal coordination number histogram. The study highlights nonuniform post-pyrolysis pore distribution and significant pore size variations in tar-rich coal. This study is crucial for understanding permeability behavior in tar-rich coal after high-temperature pyrolysis.

In vivo measurements of lung function using respiratory-gated micro-computed tomography in a smoke-exposure model of chronic obstructive pulmonary disease.

We hypothesize that in vivo respiratory-gated micro computed tomography (micro-CT) imaging can noninvasively provide structural and functional information about the lungs in a cigarette-exposure model of chronic obstructive pulmonary disease in mice. Female C57BL/6 mice were exposed to cigarette smoke or ambient air for 1, 3, or 6 months. Each mouse received a respiratory-gated micro-CT scan at baseline and another scan following the exposure period, while anaesthetized and free-breathing. Images were obtained representing end-expiration and peak inspiration, and measurements were performed to characterize the lung structure and compute functional metrics. Following the final micro-CT session, the mice were euthanized and the lungs prepared for histology. Following 6 months of smoke-exposure, the mice exhibited larger increases in end-expiration lung volume and functional residual capacity, and a reduction in weight gain when compared with air-exposed mice. The histogram of CT numbers in the lung obtained during end-expiration also showed a shift to lower CT numbers following 6 months of smoke-exposure, indicating increased air content within the lungs. The metrics suggested air-trapping in the lung, which is consistent with emphysema. In the 3-month exposure group, only the reduction in weight gain was significant compared with the air-exposed group. Histological analysis confirmed that the 6-month smoke-exposed mice likely developed centrilobular emphysema as measured by the mean linear intercept. Respiratory-gated micro-CT imaging of free-breathing mice at multiple respiratory phases is noninvasive and provides additional information about lung structure and function that complements postmortem techniques and could be used to monitor changes over time.

A clinical study comparing polymer and gold fiducials for prostate cancer radiotherapy.

Image guidance with gold fiducials improves outcomes of prostate radiotherapy. However, gold produces artefact on CT imaging, interfering with contouring and verification. The purpose of this study was to compare polymer to standard gold fiducials using radiotherapy imaging modalities to assess the visibility and artefact. Twenty eight patients with locally advanced prostate cancer were enrolled, half had three polymer fiducials implanted into the prostate and half underwent insertion of gold fiducials. Patients were imaged with CT, T2 weighted MRI, cone-beam CT (CBCT) and planar KV images. Fiducials were scored for visibility and assessed for CT artefact in surrounding prostate tissue. The artefact was quantified from Hounsfield number histograms and separated into percentile ranges and proportion of voxels in HU normal tissue range of a 2cm sphere surrounding the fiducial. Gold and polymer fiducials were sufficiently visible for CT and CBCT verification. The gold fiducials could be visualized well on KV planar imaging; however, the polymer markers were obscured by pelvic bones. Neither polymer nor gold fiducials could be visualized on MRI. The polymer fiducial produced less artefact than gold on CT, having less voxel spread for the HU percentile ranges and a greater proportion of voxels in the normal tissue range. Polymer fiducials are a more suitable fiducial than gold for CT/CBCT in prostate cancer radiotherapy, demonstrating minimal artefact and good visibility on CT. However, they were not well seen on MRI or KV imaging and thus not suitable for co-registration or planar KV verification.

A photon-number-based systematic algorithm for range image recovery of GM-APD lidar under few-frames detection

The GM-APD lidar can detect the single-photon level echo signal and obtain three-dimensional range information, but the imaging results are greatly affected by noise and statistical frame number. Considering these problems, this paper proposes a photon-number systematic (PNS) algorithm under a few statistical frames in low SNR (signal-to-noise ratio). It includes three parts: (a) We realize range image recovery based on the signal photon number histogram with a match filtering algorithm by using the proposed normalized discrete waveform functions. (b) We propose a two-dimensional double-threshold approximation denoising algorithm, which is used for image-level denoising. (c) The neighborhood threshold smoothing algorithm is proposed to supplement the target missing pixels. It is verified by Monte Carlo simulation and experimental results that the proposed algorithm has a high recovery ratio, denoising, and fidelity performance under few-frames detection in different SNRs conditions. The experimental largest target recovery ratio of the proposed algorithm is about 92%, the peak signal-to-noise ratio exceeds 16.4, and the maximum average measure of structural similarity is more than 85% under 500 statistical frames. This paper provides a new processing scheme for GM-APD lidar range image recovery under few-frames detection and low SNR, it lays the foundation for real-time and all-day monitoring.

Multi-drug-resistance efflux in cisplatin-naive and cisplatin-exposed A2780 ovarian cancer cells responds differently to cell culture dimensionality.

A primary reason for chemotherapy failure is chemoresistance, which is driven by various mechanisms. Multi-drug resistance (MDR) is one such mechanism that is responsible for drug extrusion from the intracellular space. MDR can be intrinsic and thus, may pre-exist the first application of chemotherapy. However, MDR may also be acquired during tumor exposure to chemotherapeutic agents. To understand whether cell clustering can influence intrinsic and acquired MDR, the present study assessed cultured monolayers (representing individual cells) and spheroids (representing clusters) formed by cisplatin-naïve (intrinsic MDR) and cisplatin-exposed (acquired MDR) lines of ovarian cancer A2780 cells by determining the cytometry of reaction rate constant (CRRC). MDR efflux was characterized using accurate and robust cell number vs. MDR efflux rate constant (kMDR) histograms. Both cisplatin-naïve and cisplatin-exposed monolayer cells presented unimodal histograms; the histogram of cisplatin-exposed cells was shifted towards a higher kMDR value suggesting greater MDR activity. Spheroids of cisplatin-naïve cells presented a bimodal histogram indicating the presence of two subpopulations with different MDR activity. In contrast, spheroids of cisplatin-exposed cells presented a unimodal histogram qualitatively similar to that of the monolayers of cisplatin-exposed cells but with a moderate shift towards greater MDR activity. A flow-cytometry assessment of multidrug resistance-associated protein 1 transporter levels in monolayers and dissociated spheroids revealed distributions similar to those of kMDR, thus, suggesting a plausible molecular mechanism for the observed differences in MDR activity. The observed greater effect of cell clustering on intrinsic rather than in acquired MDR can help guide the development of new therapeutic strategies targeting clusters of circulating tumor cells.

Mathematical model of COVID-19 spread in Turkey and South Africa: theory, methods, and applications

A comprehensive study about the spread of COVID-19 cases in Turkey and South Africa has been presented in this paper. An exhaustive statistical analysis was performed using data collected from Turkey and South Africa within the period of 11 March 2020 to 3 May 2020 and 05 March and 3 of May, respectively. It was observed that in the case of Turkey, a negative Spearman correlation for the number of infected class and a positive Spearman correlation for both the number of deaths and recoveries were obtained. This implied that the daily infections could decrease, while the daily deaths and number of recovered people could increase under current conditions. In the case of South Africa, a negative Spearman correlation for both daily deaths and daily infected people were obtained, indicating that these numbers may decrease if the current conditions are maintained. The utilization of a statistical technique predicted the daily number of infected, recovered, and dead people for each country; and three results were obtained for Turkey, namely an upper boundary, a prediction from current situation and lower boundary. The histograms of the daily number of newly infected, recovered and death showed a sign of lognormal and normal distribution, which is presented using the Bell curving method parameters estimation. A new mathematical model COVID-19 comprised of nine classes was suggested; of which a formula of the reproductive number, well-poseness of the solutions and the stability analysis were presented in detail. The suggested model was further extended to the scope of nonlocal operators for each case; whereby a numerical method was used to provide numerical solutions, and simulations were performed for different non-integer numbers. Additionally, sections devoted to control optimal and others dedicated to compare cases between Turkey and South Africa with the aim to comprehend why there are less numbers of deaths and infected people in South Africa than Turkey were presented in detail.

Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks

Routing and spectrum assignment (RSA) in elastic optical network (EON) is a challenging task as an inefficient RSA may result in spectral and spatial fragmentation, leading to under-utilization of spectrum and increased connection blocking. Previous fragmentation-aware RSA algorithms, based on the assumption of an ideal physical layer, are not suitable for the performance evaluation of practical networks, where quality of the optical signal degrades while travelling long distances through the optical fiber links. Therefore, while running a RSA algorithm, it is necessary to consider the impact of all noise components to evaluate receiver bit-error rate (BER) on the fly, so that each connection is setup only when the estimated BER remains below an acceptable limit. This problem, hitherto unexplored in EONs though more significant and complex as compared to the conventional optical networks, is taken into consideration in this paper while framing fragmentation-aware RSA algorithm. In particular, we present a novel BER-cum-fragmentation-aware RSA algorithm, by using online estimates of BER values while executing fragmentation-aware RSA. The simulation results show that awareness of BER increases the connection blocking, which can however be addressed favorably by increasing transmit power, as much as possible from the available transmitter modules. Finally, we present a histogram of number of established connections versus delay-bandwidth product of the connections, leading to an assessment of the proposed algorithm in respect of the ability to allow long-haul connections with large bandwidth requests.

Synergistic Modern Global 1 Km Cropland Dataset Derived from Multi-Sets of Land Cover Products

The quality of global cropland products could affect our understanding of the impacts of cropland reclamation on global changes. With the advancement of remote sensing technology, several global land cover products and synergistic datasets have been developed in recent decades. However, there are still some disagreements among the global cropland datasets. In this paper, we proposed a new synergistic method that integrates the reliability of spatial distribution and cropland fraction on a pixel scale, and developed a modern (around 2000 C.E.) fractional cropland dataset with a 1 km × 1 km spatial resolution on the basis of the spatial consistency of cropland reclamation intensity derived from multi-sets of global land cover products. The main conclusions are shown as follows: (1) The accuracy of spatial distribution assessed by validation samples in this synergistic dataset reaches 87.6%, and the dataset also has a moderate amount of cropland pixels when compared with other products. (2) The reliability of cropland fraction on the pixel scale had been highly improved, and most cropland pixel has a higher fraction (over 90%) in this dataset. The “L” shape of the histogram of pixel numbers with different reclamation intensities is reasonable because it is consistent with the up-scaling results derived from satellite-derived products with high spatial resolutions and the expert knowledge on cultivation. (3) The cropland areas in this non-calibrated result are generally closer to that of FAOSTAT on scales from global to national when compared to other non-calibrated synergistic datasets and original satellite-derived products. (4) The reliability of the synergistic result developed by this method might be decreased to some degree in the regions with high discrepancies among the original multi-sets of cropland datasets.

Probability distributions of statistical characteristics of mesoscale eddies in the global ocean

ABSTRACTThree independent global observations of nonlinear mesoscale eddy datasets derived from satellite altimeter are analysed to investigate the statistical characteristics of mesoscale eddies. Histograms of amplitude, radius, rotation speed, and Rossby number of mesoscale eddy in the global ocean were fitted by four probability density functions called normal, Rayleigh, gamma, and lognormal distribution. The histograms of these characteristics were best approximated by the lognormal distribution, regardless of the choice of eddy datasets. Further comparison studies between eddies of several large ocean basins and eddy-active regions showed that the lognormal distribution performed better in eddy-active regions. This study improves the understanding of eddy characteristics distributions, and may provide a basis for eddy parameterization in the numerical model.

Object Contour Tracking Algorithm of Infrared Image Under Complex Background

Traditional image contour tracking algorithm has low tracking accuracy. To solve this problem, an object contour tracking algorithm based on local significant edge features in complex background is proposed. In the algorithm, the projective invariant is firstly introduced, to construct the geometric information descriptor between the edge positions of the infrared image, and set up the histogram of the feature number of each target contour. The geometric similarity between the features is measured by the pasteurized coefficient, the edge features of the neighbourhood around the object contour are established, and the object contour with significant features in the edge of the image is searched. Combining Shape-context operator with edge feature, the feature description vector can be formed, and Euclidean distance is defined to track measurement function. Using this function, the selected object contour is tracked preliminarily. The random consistency checking algorithm is used to eliminate the false tracking feature points and obtain the best tracking value of the object contour, thus the infrared image’s object contour tracking is carried out in the complex background. Experimental simulation shows that the proposed algorithm has high tracking accuracy and effectively improves the quality of infrared image analysis.

Research on establishing numerical model of geo material based on CT image analysis

Geotechnical engineering material is a kind of heterogeneous composite material. The stone, mineral, gravel, hole, and crack contained in it have different physical and mechanical properties, and their responses to external forces are very different. For example, stress distribution, crack propagation, and failure mode are closely related to material heterogeneity and microstructure. This paper presents a finite element model method for processing CT images of geotechnical materials by using digital image technology. This paper presents a finite element model method for processing CT images of geotechnical materials by using digital image technology. The theory of digital image processing is applied to geotechnical CT image processing to realize pseudo-color enhancement of CT image, and the histograms of different geotechnical CT numbers are obtained. Canny operator is used to detect the edge of geotechnical CT image, and the binary image of geotechnical microstructure is obtained. According to the color change of pseudo-color enhanced image and CT histogram, the meso-structure and variation of rock and soil can be quantitatively analyzed. The finite element model established by this method can fully consider the heterogeneity of geotechnical materials, especially the effect of void distribution on the mechanical properties of geotechnical materials.

Three-dimensional substructure measurements for the differential diagnosis of ground glass nodules

BackgroundWe analyzed the differences between maximum and peak computed tomography (CT) numbers (M-P), respectively representing the densities of the solid center and the main periphery of ground-glass nodules (GGNs), and the average change in M-P velocity (V(M-P)) during follow-up to differentiate between pre-invasive (PIA) and invasive adenocarcinoma (IAC).MethodsData of 102 patients were retrospectively collected and analyzed in our study including 43 PIAs and 59 IACs. Diameters, total volumes, and the maximum and peak CT numbers in CT number histograms were measured and followed for at least 3 months. This study was registered retrospectively.ResultsThe M-P values for IACs were higher than those for PIAs (p = 0.001), with an area under the curve (AUC) of 0.810 and a threshold of 489.5 Hounsfield units (HU) in ROC analysis. The V(M-P) values for IACs were smaller than those for PIAs (p = 0.04), with an AUC of 0.805 and a threshold of 11.01 HU/day.ConclusionsM-P and V(M-P) values may help distinguish IACs from PIAs by representing the changes in the sub-structural densities of GGNs during follow-up.

Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study.

PurposeIn an effort for early assessment of treatment response, we investigate radiation induced changes in quantitative CT features of tumor during the delivery of chemoradiation therapy (CRT) for pancreatic cancer.MethodsDiagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. On each daily CT, the pancreatic head, the spinal cord and the aorta were delineated and the histograms of CT number (CTN) in these contours were extracted. Eight histogram-based radiomic metrics including the mean CTN (MCTN), peak position, volume, standard deviation (SD), skewness, kurtosis, energy and entropy were calculated for each fraction. Paired t-test was used to check the significance of the change of specific metric at specific time. GEE model was used to test the association between changes of metrics over time for different pathology responses.ResultsIn general, CTN histogram in the pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the 1st to the 26th fraction in MCTN ranged from -15.8 to 3.9 HU with an average of -4.7 HU (p<0.001). Meanwhile the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less peaked). The changes of MCTN, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response is associated with the changes of MCTN, SD, and skewness. In cases of good response, patients tend to have large reductions in MCTN and skewness, and large increases in SD and kurtosis.ConclusionsSignificant changes in CT radiomic features, such as the MCTN, skewness, and kurtosis in tumor were observed during the course of CRT for pancreas cancer based on quantitative analysis of daily CTs. These changes may be potentially used for early assessment of treatment response and stratification for therapeutic intensification.

Standardizing CT lung density measure across scanner manufacturers.

Computed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty. Data were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization. We show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [-862.0 HU, -851.3 HU] before standardization, and [-859.0 HU, -853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of -855.9 HU with 95% CI of [-857.4 HU, -854.5 HU] based on the calibration and the uncertainty in the SRM certified density. This study provides a quantitative assessment of the variations expected in CT lung density measures attributed to non-biological sources such as scanner calibration and scanner x-ray spectrum and filtration. By removing scanner-protocol dependence from the measured CT numbers, higher accuracy and reproducibility of quantitative CT measures were attainable. The standardization procedures developed in study may be explored for possible application in CT lung density clinical data.

Quantitative CT analysis of pulmonary pure ground-glass nodule predicts histological invasiveness

ObjectiveTo assess whether quantitative computed tomography (CT) can help predict histological invasiveness of pulmonary adenocarcinoma appearing as pure ground glass nodules (pGGNs). MethodsA total of 110 pulmonary pGGNs were retrospectively evaluated, and pathologically classified as pre-invasive lesions, minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA). Maximum nodule diameters, largest cross-sectional areas, volumes, mean CT values, weights, and CT attenuation values at the 0th,2th,5th, 25th, 50th,75th, 95th, 98th and100th percentiles on histogram, as well as 2th to 98th, 5th to 95th, 25th to 75th,and 0th to 100thslopes, respectively, were compared among the three groups. ResultsOf the 110 pGGNs, 50, 28, and 32 were pre-invasive lesions, MIA, and IPA, respectively. Maximum nodule diameters, largest cross-sectional areas, andmass weights were significantly larger in the IPA group than in pre-invasive lesions. The 95th, 98th, 100th percentiles, and 2th to 98th, 25th to 75th, and 0th to 100thslopes were significantly different between pre-invasive lesions and MIA or IPA. Logistic regression analysis showed that the maximum nodule diameter (OR=1.21, 95%CI: 1.071–1.366, p<0.01) and 100th percentile on histogram (OR=1.02, 95%CI: 1.009–1.032, p<0.001) independently predicted histological invasiveness. ConclusionsQuantitative analysis of CT imaging can predict histological invasiveness of pGGNs, especiallythe maximum nodule diameter and 100th percentile on CT number histogram; this can instruct the long-term follow-up and selective surgical management.

Fluorescence intensity and bright spot analyses using a confocal microscope for photodynamic diagnosis of brain tumors

BackgroundIn photodynamic diagnosis using 5-aminolevulinic acid (5-ALA), discrimination between the tumor and normal tissue is very important for a precise resection. However, it is difficult to distinguish between infiltrating tumor and normal regions in the boundary area. In this study, fluorescent intensity and bright spot analyses using a confocal microscope is proposed for the precise discrimination between infiltrating tumor and normal regions. MethodsFrom the 5-ALA-resected brain tumor tissue, the red fluorescent and marginal regions were sliced for observation under a confocal microscope. Hematoxylin and eosin (H&E) staining were performed on serial slices of the same tissue. According to the pathological inspection of the H&E slides, the tumor and infiltrating and normal regions on confocal microscopy images were investigated. From the fluorescent intensity of the image pixels, a histogram of pixel number with the same fluorescent intensity was obtained. The fluorescent bright spot sizes and total number were compared between the marginal and normal regions. ResultsThe fluorescence intensity distribution and average intensity in the tumor were different from those in the normal region. The probability of a difference from the dark enhanced the difference between the tumor and the normal region. The bright spot size and number in the infiltrating tumor were different from those in the normal region. ConclusionsFluorescence intensity analysis is useful to distinguish a tumor region, and a bright spot analysis is useful to distinguish between infiltrating tumor and normal regions. These methods will be important for the precise resection or photodynamic therapy of brain tumors.

Numerical modelling of soot formation and oxidation using phenomenological soot modelling approach in a dual-fueled compression ignition engine

Modelling soot formation and oxidation in diesel engines has been a long-standing challenge. In this study, soot particle characteristics in terms of particle dynamics, particle size and number density were modelled by integrating a multi-step phenomenological soot model into the KIVA-CHEMKIN CFD code for compression ignition engine combustion simulations. This semi-detailed soot model is dedicated to solving rate equations using sub-models to account for precursor formation, soot particle inception and coagulation as well as soot surface growth and oxidation. Soot growth and oxidation is inherently derived from the concentration of certain species found in the chemical reaction mechanism used, namely H, O2, C2H2 and the nucleating polycyclic aromatic hydrocarbon (PAH). Acetylene is taken as the core precursor species in nucleating PAH, soot inception as well as acetylene-assisted soot surface growth. The integrated multi-step phenomenological soot model has been validated under gasoline and diesel dual-fuel engine conditions.The predicted histograms of soot particle number along with size distribution contribute towards the understanding of dominant factors that affect soot formation. The factors affecting soot particle under varying engine loads and fuel conditions were extensively investigated. Generally, the predicted soot particle size was larger for heavy-sooting conditions as compared to low-sooting conditions. For port-injected gasoline-dominated combustion, there is less soot being discharged. This might be attributed to two reasons. First, a more homogenous mixture is realized with less diesel fuel, thus effectively reducing the formation of soot precursors. The other reason is the intensive heat release which enhances the depletion of soot precursors, thereby impeding the growth of soot particles in terms of size and mass.

Dual-Energy Spectral CT: Various Clinical Vascular Applications

Single-source dual-energy (DE) computed tomography (CT) with fast switching of tube voltage allows projection-based image reconstruction, substantial reduction of beam-hardening effects, reconstruction of accurate monochromatic images and material decomposition images (MDIs), and detailing of material composition by using x-ray spectral information. In vascular applications, DE CT is expected to overcome limitations of standard single-energy CT angiography, including patient exposure to nephrotoxic contrast medium and carcinogenic radiation, insufficient contrast vascular enhancement, interference from metallic and beam-hardening artifacts and severe vessel calcification, and limited tissue characterization and perfusion assessment. Acquisition of low-energy monochromatic images and iodine/water MDIs can reasonably reduce contrast agent dose and improve vessel enhancement. Acquisition of virtual noncontrast images, such as water/iodine MDIs, can reduce overall radiation exposure by replacing true noncontrast CT in each examination. Acquisition of monochromatic images by using metal artifact reduction software or acquisition of iodine/water MDIs can reduce metal artifacts with preserved or increased vessel contrast, and subtraction of monochromatic images between two energy levels can subtract coils composed of dense metallic materials. Acquisition of iodine/calcium (ie, hydroxyapatite) MDIs permits subtraction of vessel calcification and improves vessel lumen delineation. Sensitive detection of lipid-rich plaque can be achieved by using fat/water MDIs, the spectral Hounsfield unit curve (energy level vs CT attenuation), and a histogram of effective atomic numbers included in an image. Various MDIs are useful for accurate differentiation among materials with high attenuation values, including contrast medium, calcification, and fresh hematoma. Iodine/water MDIs are used to assess organ perfusion, such as in the lungs and myocardium. Understanding these DE CT techniques enhances the value of CT for vascular applications. (©)RSNA, 2016.

WE-FG-206-02: Brief Variations of BOLD Signal in Resting State FMRI Leading to Functional Connectivity Pattern Identification

Purpose: To assess the role of spontaneous brain activity in identifying functional connectivity patterns in the brain during resting-state using both high and low blood-oxygen level dependent (BOLD) signals from functional magnetic resonance imaging (fMRI). Methods: fMRI BOLD signals within seed regions from known functional connectivity networks were extracted to find an average seed-region time profile. A threshold was applied to the BOLD signal and and time points were selected where the BOLD signal satisfied the threshold requirements. The same threshold was also applied to the time profile of each voxel in the brain, i.e., including voxels outside of the seed region. The signal profile of seed region time points above the threshold was compared to the signal profile of whole brain voxel time points over the threshold. Any time point that occurred on both signal profiles was considered a match. We then evaluated whether or not a voxel was part of the functional connectivity network based on the percentage of matching time points. Results: These findings suggest that functional connectivity patterns can be identified only using a small number of time points. Using only 10% of the time points during a functional MRI functional connectivity patterns can be reproduced with varying degrees of accuracy. Using this same method, with a 33% threshold a histogram of numbers of matches per matches possible within the region where we expect to see the functional connectivity pattern, shows no significant separation between using the top third, the middle third, or the bottom third of data. Conclusion: This work indicates that functional connectivity patterns can be reproduced not only with spontaneous activity of high signals, but also from brief instances of low BOLD signal. As the potential role of resting-state fMRI for clinical diagnosis evolves, understanding the fundamentals of functional connectivity patterns is critical. NIH R01-NS074045

SU‐F‐R‐50: Radiation‐Induced Changes in CT Number Histogram During Chemoradiation Therapy for Pancreatic Cancer

Purpose:In an effort of early assessment of treatment response, we investigate radiation induced changes in CT number histogram of GTV during the delivery of chemoradiation therapy (CRT) for pancreatic cancer.Methods:Diagnostic‐quality CT data acquired daily during routine CT‐guided CRT using a CT‐on‐rails for 20 pancreatic head cancer patients were analyzed. All patients were treated with a radiation dose of 50.4 in 28 fractions. On each daily CT set, the contours of the pancreatic head and the spinal cord were delineated. The Hounsfiled Units (HU) histogram in these contourswere extracted and processed using MATLAB. Eight parameters of the histogram including the mean HU over all the voxels, peak position, volume, standard deviation (SD), skewness, kurtosis, energy, and entropy were calculated for each fraction. The significances were inspected using paired two‐tailed t‐test and the correlations were analyzed using Spearman rank correlation tests.Results:In general, HU histogram in pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the first to the last fraction in mean HU in pancreatic head ranged from −13.4 to 3.7 HU with an average of −4.4 HU, which was significant (P&lt;0.001). Among other quantities, the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less sharp) during the CRT delivery. The changes of mean HU, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response status is associated with the changes of SD (ΔSD), i.e., ΔSD= 1.85 (average of 7 patients) for good reponse, −0.08 (average of 6 patients) for moderate and poor response.Conclusion:Significant changes in HU histogram and the histogram‐based metrics (e.g., meam HU, skewness, and kurtosis) in tumor were observed during the course of chemoradiation therapy for pancreas cancer. These changes may be potentially used for early assessment of treatment response.