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PURPOSE: Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver. METHODS: Intra-organ contrast perfusion depends on the organ’s vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood–contrast mixture was compared to clinical data. RESULTS: The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves (TCC). Measurements of simulated CT ROIs were found to agree with clinically-observed values of early arterial phase contrast enhancement of the parenchyma ([Formula: see text] HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement [Formula: see text] HU). CONCLUSIONS: The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.

Agroforestry systems play an important role in sustainable agroecosystems. However, accurately and adequately quantifying the relationships between environmental factors and tree growth in these systems are still lacking. Objectives of this study were to quantify environmental factors affecting growth of four tree species and to develop functional soil maps (FSM) for each species in an agroforestry site. The diameter at breast height, absolute growth rate (AGR), and neighborhood competition index of 259 trees from four species (northern red oak [Quercus rubra], pecan [Carya illinoinensis], cottonwood [Populus deltoides], and sycamore [Platanus occidentalis]) were determined. A total of 51 topsoil samples were collected and analyzed, and 12 terrain attributes were derived from the digital elevation model. The relationships between AGR, soil, topography, and tree size were analyzed using Spearman correlation. Based on correlation analysis, FSM for each species were generated using the k-means cluster method by overlaying correlated soil and terrain attribute maps. Results showed tree size and terrain attributes were driving factors affecting tree growth rate relative to soil properties. The spatial variations in AGR among functional units were statistically compared within tree species and the areas with larger AGR were identified by the FSM. This study demonstrated that FSM could delineate areas with different AGR for the oak, cottonwood, and sycamore trees. The AGR of pecan trees did not vary among functional units. The generated FSM may allow land managers to more precisely establish and manage agroforestry systems.

PURPOSE Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4-10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative four-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation. METHODS IPEN uses two input datasets: (a) the contours and locations of three-dimensional regions-of-interest (ROIs) such as arteries and sub-sections of cancerous lesions, and (b) a series of x-ray projection data obtained from an intra-arterial contrast injection to contrast enhancement to wash-out. IPEN then estimates a time-enhancement curve (TEC) for each ROI directly from projections without reconstructing cross-sectional images by maximizing the agreement between synthesized and measured projections with a temporal roughness penalty. When path lengths through ROIs are known for each x-ray beam, the ROI-specific enhancement can be accurately estimated from projections. Computer simulations are performed to assess the performance of the IPEN algorithm. Intra-arterial contrast-enhanced liver scans over 25 s were simulated using XCAT phantom version 2.0 with heterogeneous tissue textures and cancerous lesions. The following four sub-studies were performed: (a) The accuracy of the estimated TECs with overlapped lesions was evaluated at various noise (dose) levels with either homogeneous or heterogeneous lesion enhancement patterns; (b) the accuracy of IPEN with inaccurate ROI contours was assessed; (c) we investigated how overlapping ROIs and noise in projections affected the accuracy of the IPEN algorithm; and (d) the accuracy of the perfusion indices was assessed. RESULTS The TECs estimated by IPEN were sufficiently accurate at a reference dose level with the root-mean-square deviation (RMSD) of 0.0027 ± 0.0001 cm-1 or 13 ± 1 Hounsfield unit (mean ± standard deviation) for the homogeneous lesion enhancement and 0.0032 ± 0.0005 cm-1 for the heterogeneous enhancement (N = 20 each). The accuracy was degraded with decreasing doses: The RMSD with homogeneous enhancement was 0.0220 ± 0.0003 cm-1 for 20% of the reference dose level. Performing 3 × 3 pixel averaging on projection data improved the RMSDs to 0.0051 ± 0.0002 cm-1 for 20% dose. When the ROI contours were inaccurate, smaller ROI contours resulted in positive biases in TECs, whereas larger ROI contours produced negative biases. The bias remained small, within ± 0.0070 cm-1 , when the Sorenson-Dice coefficients (SDCs) were larger than 0.81. The RMSD of the TEC estimation was strongly associated with the condition of the problem, which can be empirically quantified using the condition number of a matrix Az that maps a vector of ROI enhancement values z to projection data and a weighted variance of projection data: a linear correlation coefficient (R) was 0.794 (P

Harvesting corn (Zea mays L.) stover for use as bioenergy feedstock may provide short‐term economic benefits and perhaps improve grain yield in continuous corn systems, but excessive stover removal may lead to long‐term depletion of soil C stocks. To better quantify the impacts of stover harvest on the soil C balance, we investigated CO2 fluxes under three harvest treatments (none, moderate, and high) in three continuous corn systems: (a) no tillage and no biochar applications, (b) chisel plowing with biochar amendments, and (c) chisel plowing without biochar amendments were quantified. We sampled static chambers 14, 13, and 15 times in 2010, 2011, and 2012, respectively, and the measurements were used to calibrate the Agricultural Production Systems sIMulator model. Although CO2‐C emissions did not differ among the three systems, both moderate (approximately 30%) and high (approximately 60%) stover removal rates reduced simulated CO2‐C emissions by nearly 10% and more than 22%, respectively. Despite these reductions in CO2 flux, the sum of CO2‐C and stover C exceeded CO2‐C losses in those plots without stover removal. This finding suggested soil C depletion was occurring for the soil and crop management practices used, even though depletion was not evident after 3 years. Application of biochar increased soil C levels, suggesting it may be able to offset some C losses. We conclude that for sustainable, highly productive agroecosystems that include stover harvest, all agronomic practices must be optimized to minimize C losses and maintain soil quality.

The results of a comprehensive study of soils under a 30-m-wide five-row oak shelterbelt of meridional orientation and on the adjacent arable fields of the agroforestry landscape in typical forest-steppe of Belgorod oblast are discussed. Background soils are medium-deep light clayey leached chernozems (Luvic Chernozems) on calcareous loesslike heavy loam. As a result of tree growth during the past 50 years (age of the shelterbelt), certain changes in the intensity and direction of soil-forming processes have taken place in soils of both the shelterbelt and the adjacent arable land. Data on the contents and stocks of the clay (

Alley-cropping systems that pair economically important tree species with crop production in alleyways have the potential to diversify outputs and improve resource use efficiency. However, manageme...

There is a need to quantify agriculture’s potential to sequester carbon (C) to inform global approaches aimed at mitigating climate change effects. Many factors including climate, crop, soil manage...

Purpose: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo). Approach: The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise–non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency (favg), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results: Pearson and Spearman correlation coefficients 0.97 and 0.96 for favg indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with ∼106 pixels. Conclusions: Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.

Agroforestry systems that integrate useful long-lived trees have been recognized for their potential in mitigating the accumulation of atmospheric fossil fuel-derived carbon (C). Black walnut (Juglans nigra) is frequently planted and cultivated in North America for its valuable lumber and edible nuts, and is highly amenable to the integration of understory crops or livestock in agroforestry systems. However, little is known about C content in black walnut trees, including the amounts of C assimilated into lignocellulosic tissues within different tree compartments. Therefore, allometric equations for above- and below-ground compartments of 10-year-old black walnut trees across diverse locations were developed. Ten grafted black walnut trees from each of four sites across the midwestern USA were destructively harvested for above- and below-ground biomass, and dry biomass weight (DWw), C (Cw) and nitrogen (N; Nw) stocks were quantified. Soils surrounding the harvested trees were sampled and analyzed for soil organic C (SOC) and total N (TN). Total DWw ranged from 27 to 54 kg tree−1, with woody tissues containing an average of 467 g kg−1 C and 3.5 g kg−1 N. Woody tissues differed in Cw and Nw across location, and above-ground sections contained more C and less N compared with most root tissues. The slopes of the allometric equations did not differ significantly among locations, while intercepts did, indicating that trees only differed in initial size across locations. SOC and TN did not vary in distance from the trees, likely because the trees were not yet old enough to have impacted the surrounding soils. Our results establish a foundation for quantifying C and N stocks in newly established black walnut alley cropping systems across diverse environments.

Soil bulk density (ρb) may be purposely reduced in agricultural fields using tillage to improve hydraulic properties. However, tillage alters the soil structure, resulting in unstable soils. As the soil stabilizes, ρb increases over time. While this is known, studies on soil hydraulic properties in tilled soils, including comparisons between tilled and non-tilled soils, commonly assume a rigid soil structure. This study presents changes in soil water retention and saturated hydraulic conductivity (Ksat) as ρb increased dynamically with time following tillage at a loam-textured field site. Over the summer of 2015, soil cores were collected at several depths below the surface following precipitation events. Soil water retention curves and Ksat were determined using pressure cells and the constant head method, respectively. Tillage reduced ρb to 0.94 g cm−3. Changes in ρb increased with depth, reaching a ρb of 1.11 g cm−3 in the 0–5 cm layer, and a ρb of 1.42 g cm−3 at the deepest tilled layer. Soil water retention curves were markedly steeper for samples with higher ρb, indicating an overall increase in water retained at a soil matric potential (Ψ) of −33 kPa. Evaluation of two modeling approaches for water retention as a function ρb indicated that changes in water retention with increases in ρb could be reasonably estimated if a matching point was used. No clear relationship between Ksat and ρb was obvious for ρb 1.06 cm3 cm−3, Ksat decreased markedly (order of magnitude) as ρb increased. Hydraulic properties varied strongly depending on time since tillage and soil depth, and results have implications for models of tilled soils, as well as for studies comparing tilled and non-tilled soils.

Agroforestry systems such as tree windbreaks became a common practice in the U.S. Great Plains following a large tree planting program during the Dust Bowl of the 1930s. Tree windbreaks combine the potential to increase biomass and soil carbon (C) storage while maintaining agricultural production. However, our understanding of the effect of trees on soil organic carbon (SOC) is largely limited to the upper 30 cm of the soil. This study was conducted in the Great Plains to examine the impact of tree plantings ranging in age from 15 to ~ 115-years on SOC storage and relevant soil properties. We quantified SOC stocks to 1.25 m depth within eight tree plantings and in the adjacent farmed fields within the same soil map unit. Soil samples were also analyzed for inorganic carbon, total nitrogen, pH (in water and KCl), bulk density, and water stable aggregates. Averaged across sites, SOC stocks in the 1.25 m were 16% higher beneath trees than the adjacent farmed fields. Differences ranged from + 10.54 to a – 5.05 kg m−2 depending on the site, climate, and tree species and age. The subsurface soils (30-125 cm) beneath trees stored 7% more SOC stocks than the surface 30 cm (9.54 vs. 8.84 kg m−2), respectively. This finding demonstrates the importance of quantifying C stored at deeper depths under tree-based systems when tree SOC sequestration is being assessed. Overall, our results indicate the potential of trees to store C in soils and at deeper depths.

Soil thermal properties play important roles in dynamic heat and mass transfer processes, and they vary with soil water content (θ) and bulk density (ρ b). Both θ and ρ b change with time, particularly in recently tilled soil. However, few studies have addressed the full extent of soil thermal property changes with θ and ρ b. The objective of this study is to examine how changes in ρ b with time after tillage impact soil thermal properties (volumetric heat capacity, C ᵥ, thermal diffusivity, k, and thermal conductivity, λ). The study provides thermal property values as functions of θ and ρ b and of air content (n ₐᵢᵣ) on undisturbed soil cores obtained at selected times following tillage. Heat pulse probe measurements of thermal properties were obtained on each soil core at saturated, partially saturated (θ at pressure head of −50 kPa) and oven‐dry conditions. Generally, k and λ increased with increasing ρ b at the three water conditions. The C ᵥ increased as ρ b increased in the oven‐dry and unsaturated conditions and decreased as ρ b increased in the saturated condition. For a given θ, a larger ρ b was associated with larger thermal property values, especially for λ. The figures of C ᵥ, k and λ versus θ and ρ b, as well as C ᵥ, k and λ versus n ₐᵢᵣ, represented the range of soil conditions following tillage. Trends in the relationships of thermal property values with θ and ρ b were described by 3‐D surfaces, whereas each thermal property had a linear relationship with n ₐᵢᵣ. Clearly, recently tilled soil thermal property values were quite dynamic temporally due to varying θ and ρ b. The dynamic soil thermal property values should be considered in soil heat and mass transfer models either as 3‐D functions of θ and ρ b or as linear functions of n ₐᵢᵣ. HIGHLIGHTS: Thermal property values for a range of θ and ρ b were measured on undisturbed soil cores. Freshly tilled soil thermal property values were quite dynamic temporally. The thermal property values of a tilled soil were described as 3‐D surfaces with θ and ρ b. The thermal property values of a tilled soil varied linearly with n ₐᵢᵣ.

Soil heat flux Gs is an important component of the surface energy balance. Soil heat flux plates (SHFPs) are widely used to measure Gs, although several errors are known to occur. The Philip correction has been applied to minimize errors in Gs measured by SHFPs (Gp) if the soil thermal conductivity λs, SHFP thermal conductivity λp, and plate geometry function H are known. The objective of this study is to evaluate the effectiveness of the Philip correction for a variety of SHFPs. The λp were determined without thermal contact resistance and differed from the manufacturer-specified λp. A simplified H formulation was similar to or less than the full H equation for different SHFP shapes. The G ratio (Gp/Gs) was sensitive to λs/λp and H when they were relatively small. Compared with the Gs determined by a gradient method (Gs_grad), the Gp measured under a full corn (Zea mays, L.) canopy in the field underestimated Gs by 38%–62%. After applying the Philip correction, almost all Gp agreed better with Gs_grad. Generally, the Gp corrected with measured plate parameters agreed better with Gs_grad than those corrected with manufacturer-specified values. The Gp corrected with the simplified and full H expression differed for different SHFPs. These results indicate that SHFPs always underestimate Gs and that the performance of the Philip correction is affected by λp, plate dimensions, and H. An alternative method to measure Gs by a three-needle heat-pulse sensor or a gradient method, in which soil temperature and water content are measured at several depths, is recommended.

Infiltration is one of the most important hydrological processes in ecosystems, having important influence on soil erosion control and runoff, soil moisture content and groundwater recharge. This is particularly important considering the recent growing number of restoration initiatives worldwide encouraging forest cover expansion, mainly by tree planting. Here, we conducted a systematic review of scientific literature reporting infiltration measurements in forests restored by tree planting in the tropics and subtropics. We found 11 studies representing 67 data comparisons in eight countries. Overall results indicate that infiltration increases but is not fully recovered to reference conditions in forests restored by tree planting. Recovery of infiltration varied depending on land-use, soil texture and restoration age. Recovery of infiltration was higher for cultivated than for pastures and bare soils. Clayey soils had higher infiltration recovery than sandy soils. Our findings identified some knowledge gaps that should be addressed to improve our understanding of when and why tree planting to restore forests may promote the recovery of infiltration in tropical and subtropical soils. First, information about recovery of infiltration capacities when using tree planting for forest restoration results are scarce. Second, infiltration in restored forests should be monitored over time, including long-term studies and measurements on water movement through the soil profile and evaluating potential interactions between infiltration and soil attributes. Details on the effects that different forest restoration techniques (e.g., soil preparation, tree species diversity and densities) could have on infiltration is virtually unknown. Information on the influence of disturbance level on infiltration prior to tree planting is also severely lacking.

Agroforestry systems (AFS) have the potential to foster long-term carbon sequestration and nutrient uptake. Yet, information on sequestration rates is still scarce, especially for AFS in temperate regions and for maturing AFS. This study aims to determine the rate and amount of carbon and nitrogen uptake in a 17-year-old northern red oak (Quercus rubra)–pecan (Carya illinoinensis) silvopastoral planting in Fayetteville, AR, USA. Seven oak and pecan trees were felled to develop AFS-specific allometric equations for above-ground biomass, carbon, and nitrogen. Tree-stand woody biomass (DWw), carbon (Cw) and nitrogen (Nw) and leaf biomass (DWL), carbon (CL), and nitrogen (NL) were calculated with these equations. Diameter at 1.37 m above ground (DBH) was measured annually, and a non-linear mixed-effect model was used to estimate absolute (AGR) and relative growth rates. DWw and Cw was 7.1 and 3.4 Mg ha−1 for pecan and 26.6 and 12.7 Mg ha−1 for oak, which corresponds to a carbon sequestration rate of 0.75 and 0.20 Mg C ha−1 yr−1, respectively. Total N uptake was approximately 66 and 71 g N tree−1 yr−1 for oak and pecan. The mixed-effect model with individual-tree-level random effects for all parameters provided the best representation of DBH growth of oak and pecan, likely due to the high heterogeneity of site characteristics. The AGR explained the non-linear plant growth and reached its maximum of 0.017 and 0.0179 m yr−1 for oak and pecan, respectively, 11 years after planting. This suggests that carbon and nitrogen uptake also declined after 11 years.

Purpose: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo). Approach: The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise–non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency ([Formula: see text]), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results: Pearson and Spearman correlation coefficients 0.97 and 0.96 for [Formula: see text] indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with [Formula: see text]. Conclusions: Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.

Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4–10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative 4-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation. In this simulation study, we provide a proof-of-concept that the core ideas work.

Radiomic features extracted from CT imaging can be used to quantitively assess COVID-19. The objective of this work was to extract and analyze radiomics features in RT-PRC confirmed COVID-19 cases to identify relevant characteristics for COVID-19 diagnosis, prognosis, and treatment. We measured 29 morphology and second-order statistical-based radiomics features from 310 lung lesions extracted from 48 chest CT cases. Features were evaluated according to their coefficient of variation (CV). We calculated the CV for each feature under two statistical conditions: one with all lesions weighted equally and one with all cases weighted equally. In analyzing the patient data, there were 6.46 lesions-per-case and for 81.25% of cases, the lesions presented with bilateral lung involvement. For all radiomic features examined except ‘energy’, the CV was higher in the lesion distribution than the case distribution. The CV for morphological features were larger than second-order in both distributions, 181% and 85% versus 50% and 42%, respectively. The most variable features were ‘surface area’, ‘ellipsoid volume’, ‘ellipsoid surface area’, ‘volume’, and ‘approximate volume’, which deviated from the mean 173-255% in the lesion distribution and 119-176% in the case distribution. The features with the lowest CV were ‘homogeneity’, ‘discrete compactness’, ‘texture entropy’, ‘sum average’, and ‘elongation’, which deviated less than 31% by case and less than 25% by lesion. Future work will investigate integrating this data with similar studies and other diagnostic and prognostic criterion enhancing the role of CT in detecting and managing COVID-19.

Cardiovascular disease is a leading cause of death worldwide. Among all cardiovascular diseases, coronary artery disease (CAD) is a major contributor to mortality, accounting for approximately 10% of deaths per annum. Imaging techniques such as computed tomography (CT) can provide information on the location, composition, and severity of disease within the coronary arteries. However, cardiac motion makes protocol optimization for coronary CT angiography (CCTA) image acquisition and reconstruction uniquely challenging. The clinical trials necessary to optimize these protocols require a cohort of diverse patients, clinical staff, and are time-consuming. Consequently, there is a need for safer, less expensive, and faster alternatives. A virtual imaging trial (VIT) enables studies on the optimization of CCTA protocol parameters in a controlled (simulated) environment. In this work, we developed a framework to integrate computational models of CAD with computational, anthropomorphic phantoms (XCAT) and a CT simulator (DukeSim) so that VITs can be performed with these tools to optimize CCTA protocols for clinical tasks. To demonstrate the framework, we included a calcification in the right coronary artery of a 50th percentile BMI male XCAT phantom. The calcification was subjected to cardiac motion (60, 90, 120 BPM) and underwent simulated imaging with and without contrast enhancement (simulating a 370 mgI/mL injection) and was reconstructed via filtered back-projection (FBP) with two kernels (B26f, B70f). Measurements of the right coronary artery under imaging indicated that the small calcification was readily detected below 90 BPM, with no iodinated contrast media, and reconstructed via FBP with the B70f kernel. As demonstrated, this new VIT framework can provide an efficient means or CCTA protocol optimization.

Land use conversions can strongly impact soil organic matter (SOM) storage, which creates paramount opportunities for sequestering atmospheric carbon into the soil. It is known that land uses such as annual cropping and afforestation can decrease and increase SOM, respectively; however, the rates of these changes over time remain elusive. This study focused on extracting the kinetics (k) of turnover rates that describe these long-term changes in soil C storage and also quantifying the sources of soil C. We used topsoil organic carbon density and δ13C isotopic composition data from multiple chronosequences and paired sites in Russia and United States. Reconstruction of soil C storage trajectory over 250 years following conversion from native grassland to continual annual cropland revealed a C depletion rate of 0.010 yr−1 (first-order k rate constant), which translates into a mean residence time (MRT) of 100 years (R2≥0.90). Conversely, soil C accretion was observed over 70 years following afforestation of annual croplands at a much faster k rate of 0.055 yr−1. The corresponding MRT was only 18 years (R2=0.997) after a lag phase of 5 years. Over these 23 years of afforestation, trees contributed 14 Mg C ha−1 to soil C accrual in the 0 to 15 cm depth increment. This tree-C contribution reached 22 Mg C ha−1 at 70 years after tree planting. Over these 70 years of afforestation, the proportion of tree C to whole-soil C increased to reach a sizable 79 %. Furthermore, assuming steady state of soil C in the adjacent croplands, we also estimated that 45 % of the prairie C existent at the time of tree planting was still present in the afforested soils 70 years later. As an intrinsic property of k modeling, the derived turnover rates that represent soil C changes over time are nonlinear. Soil C changes were much more dynamic during the first decades following a land use conversion than afterwards when the new land use system approached equilibrium. Collectively, results substantiated that C sequestration in afforested lands is a suitable means to proactively mitigate escalating climate change within a typical person's lifetime, as indicated by MRTs of a few decades.

Forage management and environmental conditions affect water soluble carbohydrate (WSC) storage, and, in turn, influence ruminant forage utilization in silvopastoral systems. The objective was to determine effects of four dependent variables: forage species [(non-native, C3 (orchardgrass (Dactylis glomerata L.)) and native C4 mix (8:1:1 big bluestem (Andropogon gerardii Vitman), little bluestem (Schizachyrium scoparium Michx. Nash) and indiangrass (Sorghastrum nutans L.))], fertility (poultry litter and an unfertilized control), forage sampling date (mid-May, late-May, early-June, mid-June, and late-June), and hour of day (0800, 1100, 1400, and 1700 h) on WSC accumulation in a silvopasture. Concentrations of WSC (g kg DM−1) were greater (p ≤ 0.05) for C3 forages, with poultry litter not impacting WSC accumulation. Overall, WSC was greatest in mid-June, with the lowest WSC concentration observed at 0800 compared to 1100, 1400, and 1700 h (p ≤ 0.05). Therefore, harvesting forages later in the day resulted in greater WSC. A stepwise regression model indicated acid detergent fiber, ash, and forage P concentration were the best predictors (R2 = 0.85, p ≤ 0.05) of forage WSC. These results may be useful in future studies aimed at explaining diurnal cattle grazing preference and optimum forage harvest timing in silvopastoral systems.

Agroforestry systems play an important role in sustainable agroecosystems owing to enhanced atmospheric carbon assimilation (by soils and plants), nutrient cycling, enhanced soil biodiversity, and reduced soil disturbance (and subsequent soil erosion), all relative to monocropping systems. The magnitude of these ecosystem and provision services, however, relies on the type of agroforestry system employed (e.g., shelterbelts, silvopastures), as well as edaphic and climatic factors. Nonetheless, agroforestry’s potential to improve soil quality and soil conservation has been widely recognized as a major benefit of these managed agroecosystems, both in the tropics and temperate regions of the world. With increasing concern regarding climate change, microclimate modification with agroforestry systems such as shading to protect coffee and cacao crops in the tropics improves production resilience while maintaining ground cover, improving infiltration, and reducing erosion. The multifunctionality supported by agroforestry systems consistently underpins soil erosion prevention and overall soil health. Whether seeking to restore productivity to degraded lands, improve productivity of marginal lands, or increase the resiliency and plasticity of established agroecosystems, agroforestry practices provide direct and ancillary benefits to protect and enhance soil quality. Land managers have the opportunity to optimize and integrate tree, crop, and livestock production with regard to soil and climate variation to achieve their production goals while sustaining soil resources and supplying multiple ecosystem services. This chapter aims to provide an overview of soil conservation benefits during the production of food, fuel, and fiber in agroforestry systems.

Carbon cycling is a highly complex yet critical process, especially in silvopastures, which have multiple pools with the ability to store large amounts of C. However, it is largely unknown how site‐specific variables, including edaphic factors, vegetation type, topography, and management, affect soil CO2 flux in silvopastures. Therefore, this study aimed to evaluate CO2 fluxes in a silvopastoral system as affected by soil moisture (udic and aquic), forage species (C4; native grass mix and orchardgrass [C3; Dactylis glomerata L.]), fertilization (poultry litter and a control), and grazing pressure (grazed and ungrazed). Temperature and volumetric water content (VWC) were measured simultaneous with flux measurements during summer grazing in 2018 and 2019. Averaged across years, fluxes were at least 20% greater (P ≤ .05) in the native mix fertilized with poultry litter than in the unfertilized native mix and fertilized or unfertilized orchardgrass, likely owing to increased microbial diversity activity in the rhizosphere of poultry litter‐amended native grass species. Across years, CO2 flux was 7% greater (P ≤ .05) in the fertilized ungrazed areas and 7% lower (P ≤ .05) in the unfertilized ungrazed areas, respectively, compared with both fertilized grazed and unfertilized grazed areas. Carbon dioxide flux was correlated (P ≤ .05) with sampling date, soil temperature, and VWC. Study results improve the understanding of C dynamics in complex silvopasture systems and may assist producers in their selection of forage species and nutrient sources when designing silvopastoral systems for enhanced regional sustainability.

Grass root production and decomposition is a major source of C entering soils, although rates are largely unknown based on edaphic and management factors. Therefore, study objectives were to evaluate four explanatory variables including forage species (native and nonnative), fertility (poultry litter and a control), soil moisture (udic and aquic), and pasture management (grazed and an ungrazed control) in order to evaluate driving factors for root turnover and subsequent soil organic matter formation in silvopastoral systems using the root litter bag technique. Native grass root decomposition was accelerated relative to the nonnative forage based on root mass balance, as well as the exponential decay function, likely owing to greater five‐ and six‐C sugars and more digestible root tissues of native grasses. These physiochemical results suggest more favorable microbial food sources, which culminate in faster decomposition and greater microbially derived organic matter. Overall, there was greater root sloughing and subsequent soil organic matter formation potential with native grass species and poultry litter applications, with soil moisture affecting decomposition to a lesser extent. This study contributes to the understanding of complex interactions of grass species, soil moisture, nutrients, and grazing, which controls primary productivity, as well as nutrient cycling and C sequestration in silvopastures.

Soil carbon dioxide emission (SCE) is known to be impacted by multiple factors including sources of respiration, environmental factors, and crop and soil management practices, yet few reports exist on how crop row position and growth stage affect SCE in sorghum [Sorghum bicolor (L.) Moench] fields. The objective of this study was to examine the effects of row position and growth stage on SCE dynamics in a field under grain and sweet sorghum production in northern Alabama. Soil CO2 emissions were measured weekly during the growth period using a closed soil CO2 survey chamber system. The results showed that SCEs increased steadily from approximately 4 μmol CO2 m–2 s–1 during the vegetative stage and reached maximum values of approximately about 5 μmol CO2 m–2 s–1 and 6 μmol CO2 m–2 s–1 for grain sorghum and sweet sorghum, respectively. Soil CO2 emissions then decreased to approximately 2 μmol CO2 m–2 s–1 at the maturity and post‐maturity periods for both sorghum types. Significant differences in SCE were observed between row positions. Soil CO2 emissions at the row (R) positions were 19% greater than at interrow (I) positions during the entire growing season under grain sorghum production. Significant difference occurred only during the vegetative stage (SCE at R was 9% greater than at I position) under sweet sorghum production. Additionally, a significantly greater response of SCE to soil temperature (Ts) during the vegetative stage was observed under sweet sorghum production but not grain sorghum production. This study provides evidence that SCE is affected by row position, growth stage, and crop type and suggests that crop row position and growth stage should be taken into consideration for SCE estimation.

The Soil Management Assessment Framework (SMAF) is a quantitative soil quality (SQ) evaluation tool that is widely applied to assess soil response to specific agricultural management practices over time. Considering the reported SQ benefits of agroforestry (AF) systems and the potential usefulness of SMAF, the objective of this study was to evaluate the effects of tree species (pecan [Carya illinoinensis (Wangenh.) K. Koch] and northern red oak [Quercus rubra L.]), soil fertility source (poultry litter [PL] and inorganic N fertilizer [control]), and soil depth (0–15 and 15–30 cm) on SMAF‐derived SQ indices after 17 yr of management at an AF site in northwest Arkansas. Averaged across soil depth, soil organic C scores under red oak with PL application had a lower score (0.48) than red oak fertilized with inorganic N (0.60) and pecan receiving long‐term PL applications (0.60), which did not differ from pecan with inorganic N fertilizer application (0.51). Averaged across soil depth, the soil quality index (SQI) for pecan receiving PL applications was 1.1 times greater than that under red oak receiving PL and soils under pecan receiving inorganic N fertilizer. Soil quality assessments use in AF are novel, as SMAF has not been used to identify soil health in these systems, although specific tree crop codes need to be developed in SMAF. Results of this study demonstrate that soils planted under various tree species respond dissimilarly to fertilizer sources and that management may improve overall SQ.

Corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production dominate Midwestern U.S. agriculture and impact the regional carbon and nitrogen cycles. Sustaining soil carbon is important for corn‐soybean production (CS); however, quantifying soil carbon changes requires long‐term field measurements and/or model simulations. In this study, changes in soil organic (SOC), inorganic (SIC), and total (TC) carbon; pH; total nitrogen (TN); and net ecosystem production (NEP) were measured in a conventional corn‐soybean rotation in central Iowa. Soil samples (n = 42; 0–120 cm depth) were collected from two adjacent fields in 2005 and 2016. Eddy‐flux stations set up in the fields continuously monitored NEP from 2005–2016, and net biome production (NBP) was calculated using yield records. The DayCENT model was used to simulate the effects of conventional management practices on soil carbon and calibrated with field‐measured NEP and SOC. Measured soil TC (0–120 cm) decreased by −14.19 ± 6.25 Mg ha−1, with highest reductions in SOC and SIC (p, Key Points: Eddy covariance shows a decrease in ecosystem C after 11 yr of corn‐soybean production. Soil inorganic C decreased in 90–120 cm, and organic C decreased in 0–15 cm after 11 yr. DayCENT simulations suggest current conventional corn‐soybean rotations decrease all SOC pools in topsoil layer., ORISE http://dx.doi.org/10.13039/100006229

Cells associated with an abnormal (cancerous) growth exchange flows, morph freely and grow hand-in-glove with their immediate environment, the extracellular matrix (ECM). The cell structure experiences two mass flows in counterflow. Flowing into the structure are nutrients and flowing out is refuse from the metabolically active biomass within. The physical effect of the evolution of the cell and extracellular structure is more flow and mixing in that space, that is, more mixing than in the absence of a biological growth in that space. The objective of the present theory is to predict the increase in the size of the cell cluster as a function of its structure, and also to predict the critical cluster sizes that mark the transitions from one distinct cluster configuration to the next. This amounts to predicting the timing and the main features of the transitions from single cell to clusters with two, four, eight and more cells, including larger clusters with cells organized on its outer surface. The predicted evolution of the size and configuration of the cell cluster is validated successfully by comparison with measurements from several independent studies of cancerous and non-cancerous growth patterns.

Corn [Zea mays L.] and soybean [Glycine max (L.) Merr.] are important US crops, and soil management typically comprises tillage activities, yet improvements in management practices may have substantial impact on C and water dynamics. This study’s aim was to determine management impact on C and water fluxes. Four eddy covariance stations monitored evapotranspiration (ET) and net ecosystem production (NEP) in 2016–2017 in two corn–soybean–rotation systems—a conventional and a transitional system to reduced till with cover crop (i.e., aspirational). Net biome production (NBP), gross primary production (GPP), ecosystem respiration (RE), and inherent water use efficiency (IWUE*) were calculated. Aspirational site NEP was higher than the conventional site with 565 vs. 421 g C m⁻² in corn, and 108 vs. −64 g C m⁻² in soybean. The aspirational RE was lower than under conventional management for both corn and soybean. Aspirational corn GPP was lower than conventional with 1285 and 1405 g C m⁻², and no difference in soybean with 750 and 742 g C m⁻². Linear regression (p < 0.05) showed higher NEP regression slopes in spring in the conventional compared with the aspirational system, with −0.016 and −0.004 in soybean, and −0.012 and −0.005 in corn. The soybean-years were a C source in both management systems. Although annual ET was similar among crops and management with 589 to 610 mm, the growing season IWUE* was higher under conventional management. Reduced tillage substantially improved C dynamics in corn and soybean, whereas ET was less affected.

Coronary artery disease (CAD) is one of the leading causes of death world-wide. Because of its great clinical importance, technological and diagnostic advances occur to combat it at a similarly high rate. With the number of clinical trials that would be necessary, this becomes infeasible and virtual clinical trials (VCT) are necessary. These require virtual patients and virtual pathologies. A generative adversarial network (GAN) was used to create a library of coronary plaques which were physiologically validated with finite element analysis. The resulting plaque library consists of a large number of realistic, variable virtual pathologies for use in VCTs.

In photon-counting CT, detector energy thresholds directly affect image quality attributes such as contrast and noise. The purpose of this study was to identify optimum energy thresholds using a comprehensive virtual clinical trial platform. The virtual trial was done using a computational, anthropomorphic phantom and a photon-counting CT simulator. The phantom (adult male, 50th percentile body mass index) was chosen from the library of extended cardiac-torso (XCAT) phantoms. A vessel growth algorithm was used to model detailed hepatic arteries within the liver. Five computational lesions were inserted to the liver. The phantom was “imaged” with iodinated contrast at three contrast phases, i.e. arterial, portal venous, and delayed phases. All virtual sinograms were simulated using a validated CT simulator (DukeSim) modeling a prototype photon-counting scanner (CounT, Siemens Healthcare). The simulator included an energy- and threshold-dependent detector model accounting for X-ray and electronic effects and noise. At each contrast phase, the phantom was imaged at 35 detector energy threshold combinations with five lower-energy thresholds (20-40 keV) and seven upper-energy thresholds (50-80 keV). For each scan, two “threshold” images (photons detected beyond a threshold) and one “bin” image (photons detected between the two thresholds) were acquired. Noise magnitude, image contrast, and contrast-to-noise ratio (CNR) were measured in the aorta and the liver lesions. Optimum energy thresholds were identified as those yielding higher CNRs. From the simulations, noise magnitude was found to increase and CNR decrease with the increase in energy thresholds. Keeping the low threshold constant, the noise decreased and CNR increased with the increase in the upper energy threshold. Therefore, our results suggest that a combination of a lower threshold at 20-30 keV and upper threshold at 80 keV maximize the image quality. This study demonstrates an application of a virtual trial platform to quantify and optimize the quality of photon-counting CT.

The purpose of this study was to develop, validate, and evaluate a method for measuring noise texture directly from patient CT images (i.e., “in vivo”). The method identifies target regions within patient scans which are least likely to have major contribution of patient anatomy, detrends these regions locally, and measures noise power spectrum (NPS) there using previously phantomvalidated techniques. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom at three phases of contrast enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120kV at 3 dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, I50) algorithms using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of 1) a measurement in parenchymal regions of anatomy-subtracted (i.e. “noise only”) scans, and 2) a measurement in the same region of “noise-free” (pre-noise-insertion) images. To assess the performance of the in vivo NPS, the accuracy and bias of NPS average frequency (favg), and integrated noise magnitude were reported across the simulated scan population representing 2 reconstruction algorithms, 3 kernels, 3 dose levels, and 3 liver vasculature-to-parenchyma contrast levels. Pearson and Spearman correlation coefficient pairs were 0.97 and 0.93, and 1.0 and 0.99 for favg and noise magnitude, respectively. Finally, the NPS estimation method was further deployed on clinical cases to assess the feasibility of clinical analysis.

Topography plays a crucial role in spatial distribution of nutrients in soils; however, studies to quantify topographic influence on soil nutrient distribution from a silvopasture system are mostly lacking. To address this question, a 4.3-ha silvopasture site in northwest Arkansas was selected and a total of 51 topsoil (0–15 cm thickness) samples were collected and analyzed for primary (total N [TN], P, K), secondary (Ca, Mg, S), and micronutrients (Fe, Zn, Cu, Mn, B, Na). Topographic information was acquired from 12 terrain attributes derived from a 1-m digital elevation model. The prediction model was based on random forest. Results showed TN, S, and P were best predicted, whereas Cu, Ca, and Mn had the lowest prediction performance. Levels of S, Ca, Zn, Fe, and TN increased with SAGA wetness index, valley depth, flow accumulation, and multi-resolution valley bottom flatness index. Normalized height and slope height were positively related to Na but negatively to B and Cu distribution. Aspect had a positive influence on P and Mg concentrations. Based on terrain attributes, the study site could be divided into four topographic functional units (TFU), namely A, B, C, and D; TFU A had the highest nutrients present, whereas TFU B had the lowest P, K, Zn, Cu, Fe, and Ca but highest Na content. However, Mn, Mg, and B did not vary among TFUs. This study affirmed topographic influences on soil nutrient distribution, and the resulting continuous soil nutrient maps are useful for fine-tuning production systems through optimum nutrient and pasture management.

Evapotranspiration (ET) is a vital component of a field water balance. Canopy chambers are a promising method for determining crop ET because they are portable and applicable at a relatively small plot (m2) scale. Although a variety of canopy chamber designs have been proposed, field tests are still necessary to evaluate chamber performance for measuring crop ET. The objectives of this study are (1) to construct and use an improved canopy chamber to measure ET of three crops [corn (Zea mays, L.), soybean (Glycine max), and reconstructed mixed prairie] and (2) to compare the canopy chamber measurements with flux tower results and field water balance measurements (i.e., rainfall, soil water storage, ET and drainage). Three cropping systems including corn/soybean in a corn-soybean rotation, and reconstructed mixed prairie were studied in central Iowa. Canopy chamber daytime measurements were performed on 18 days in 2013 (a relatively dry growing season) and on 15 days in 2014 (a relatively wet growing season). Based on the results, the differences in daily ET and seasonal cumulative ET between canopy chambers and an eddy covariance flux tower over the measurement periods were within 5%, providing evidence that the portable canopy chamber can accurately measured ET. The chamber ET values and field water balance ET values had similar patterns over the 2013 and 2014 measurement periods, and the differences of cumulative results were less than 10%. In conclusion, the canopy chamber was proven to be an effective method for measuring small plot ET.

Crop canopy CO exchange rate (CER) includes crop photosynthesis and soil/plant respiration. A portable canopy chamber is effective in determining crop CER values at a relatively small spatial (m) scale. The objectives of this study were to use a canopy chamber to measure CO fluxes in corn ( L.) and soybean [ (L.) Merr.]. Chamber measurements were performed for 18 and 15 d in 2013 and 2014, respectively. The canopy chamber measures instantaneous CER fluxes, and daily and daytime cumulative CO values were calculated from the instantaneous CER. The chamber CER results were compared with nearby eddy covariance (EC) flux tower measurements at a variety of time scales, i.e., instantaneous, daily, and daytime cumulative (multiple months). The daily and daytime cumulative chamber CER values were within 5% of the EC results, providing evidence for the effectiveness of the portable canopy chamber method. In conclusion, the portable canopy chamber provides reliable CO flux measurements despite the small size of field plots.