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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Influence of living grass Roots and endophytic fungal hyphae on soil hydraulic properties

Soil hydraulic properties are often estimated based on laboratory data or pedotransfer functions dependent on soil physical properties, which often do not consider potential impacts of soil roots or fungal hyphae. Here, we first review current knowledge of how these soil biotic components affect hydraulic properties, then we conducted laboratory experiments to specifically test if the presence of roots and mycorrhizal fungi had a significant effect on the hydraulic properties of two soils with contrasting textures: Flint sand and Hamblen silt loam. Soil cores were seeded with (Panicum virgatum) and grown in a greenhouse over three separate growth periods. The endophytic fungus Serendipita indica was injected as liquid inoculant into designated mycorrhizal cores. Saturated hydraulic conductivity (K sat ) measurements were made with a constant head permeameter, and soil water retention curves were obtained by the evaporation method, supplemented at the dry end for Hamblen silt loam with water activity meter data. Retention curve parameters were obtained by fitting the van Genuchten equation to the resulting measurements. Mean root volume ratios were higher in the mycorrhizal inoculated treatment than in the uninoculated treatment for both soils. For Flint sand, analysis of variance revealed that K sat was reduced by the presence of roots as compared to bare soil. This was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve for Flint sand by increasing water content at saturation and by reducing the slope of the curve. These changes suggested roots created additional porosity and broadened the pore-size distribution. The presence of mycorrhizal fungi accentuated the root effects. The influence of roots and mycorrhizal fungi on hydraulic properties was less obvious for the Hamblen silt loam, as none of the treatments differed from each other at p < 0.05. The results highlight the necessity to consider the impact of root and fungal structures on models of soil hydraulic properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Distribution–Based Global Sensitivity Analysis in Hydrology

Global sensitivity analysis (GSA) is routinely used in academic setting to quantify the influence of input variability and uncertainty on predictions of a quantity of interest. Practical applications of GSA are hampered by its high computational cost, which arises from the need to run large (e.g., groundwater) models multiple times, and by its reliance on the analysis of variance, which formally requires input parameters to be uncorrelated. The former difficulty can be alleviated by replacing expensive models with inexpensive (e.g., polynomial) surrogates, while adoption of distribution-based (rather than variance-based) metrics can, in principle, overcome the latter but at significantly increased computational cost. To make use of distribution-based GSA feasible for regional-scale models with a large number of degrees of freedom, we supplement it with a surrogate model built with polynomial chaos expansions with analytically updated coefficients. Here, we demonstrate the computational efficiency of our algorithm on a case study dealing with evaluation of the effects of temperature variability on annual evapotranspiration at the regional scale.

42 ENGINEERING↗
DOE OSTI2022

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm –2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

2D stochastic-integral models for characterizing random grain noise in titanium alloys

We extend our previous work, in which we applied high-dimensional model representation (HDMR) and analysis of variance (ANOVA) concepts to the characterization of a metallic surface that has undergone a shot-peening treatment to reduce residual stresses, and has, therefore, become a random conductivity field. That example was treated as a onedimensional problem, because those were the only data available. In this study, we develop a more rigorous two-dimensional model for characterizing random, anisotropic grain noise in titanium alloys. Such a model is necessary if we are to accurately capture the 'clumping' of crystallites into long chains that appear during the processing of the metal into a finished product. The mathematical model starts with an application of the Karhunen-Loève (K-L) expansion for the random Euler angles, θ and φ, that characterize the orientation of each crystallite in the sample. The random orientation of each crystallite then defines the stochastic nature of the electrical conductivity tensor of the metal. We study two possible covariances, Gaussian and double-exponential, which are the kernel of the K-L integral equation, and find that the double-exponential appears to satisfy measurements more closely of the two. Results based on data from a Ti-7Al sample will be given, and further applications of HDMR and ANOVA will be discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Fabrication of FCC-SiO{sub 2} colloidal crystals using the vertical convective self-assemble method

In order to determine the optimal conditions for the growth of high-quality 250 nm-SiO{sub 2} colloidal crystals by the vertical convective self-assemble method, the Design of Experiments (DoE) methodology is applied. The influence of the evaporation temperature, the volume fraction, and the pH of the colloidal suspension is studied by means of an analysis of variance (ANOVA) in a 3{sup 3} factorial design. Characteristics of the stacking lattice of the resulting colloidal crystals are determined by scanning electron microscopy and angle-resolved transmittance spectroscopy. Quantitative results from the statistical test show that the temperature is the most critical factor influencing the quality of the colloidal crystal, obtaining highly ordered structures with FCC stacking lattice at a growth temperature of 40°C.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Optimization of the pyrolysis process of empty fruit bunch (EFB) in a fixed-bed reactor through a central composite design (CCD)

The production of crude palm oil from the processing of palm fresh fruit bunches in the palm oil mills in Malaysia hs resulted in a huge quantity of empty fruit bunch (EFB) accumulated. The EFB was used as a feedstock in the pyrolysis process using a fixed-bed reactor in the present study. The optimization of process parameters such as pyrolysis temperature (factor A), biomass particle size (factor B) and holding time (factor C) were investigated through Central Composite Design (CCD) using Stat-Ease Design Expert software version 7 with bio-oil yield considered as the response. Twenty experimental runs were conducted. The results were completely analyzed by Analysis of Variance (ANOVA). The model was statistically significant. All factors studied were significant with p-values < 0.05. The pyrolysis temperature (factor A) was considered as the most significant parameter because its F-value of 116.29 was the highest. The value of R{sup 2} was 0.9564 which indicated that the selected factors and its levels showed high correlation to the production of bio-oil from EFB pyrolysis process. A quadratic model equation was developed and employed to predict the highest theoretical bio-oil yield. The maximum bio-oil yield of 46.2 % was achieved at pyrolysis temperature of 442.15 °C using the EFB particle size of 866 μm which corresponded to the EFB particle size in the range of 710–1000 μm and holding time of 483 seconds.

42 ENGINEERING↗
DOE OSTI2016

Effect of bentonite modification on hardness and mechanical properties of natural rubber nanocomposites

The effect of sodium activation, ion-exchange with tertiary amine salt, surface treatment with non-ionic surfactant, and wet grinding of bentonite on hardness and mechanical properties of natural rubber nanocomposites (NRN) was studied using full factorial design of experiment. Results of X-ray diffraction (XRD) show increase in basal spacing d of bentonite due to modification, while attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) confirm the organic modification of bentonite. Analysis of variance (ANOVA) shows that the main effect of surface treatment increases the hardness and decreases the tensile modulus of the NRN. The surface treatment and wet grinding of bentonite decrease the tensile stresses at 100, 200 and 300% strain of NRN. Sodium activation and ion-exchange negatively affect the compressive properties, while surface treatment significantly improves the compressive properties of NRN.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing Canopy Openness in Open Forests: Spherical Densiometer and Canopy Photography Are Equivalent but Less Sensitive than Direct Measurements of Solar Radiation

In forest ecosystems, canopy openness affects understory light availability, plant growth, and tree species recruitment, thus shaping future forest composition, structure, and functional diversity. Foresters must correctly and quickly measure canopy openness to meet their management objectives. To help guide the selection of an appropriate method for measuring canopy openness, we compared three common techniques that vary in cost, complexity, and time required for measurements and data processing: smartphone-based hemispherical photography, spherical densiometer measurements, and direct measurements of solar radiation (using AccuPAR ceptometer). We measured canopy openness using these three methods on 28 permanent forest health monitoring plots in pine-oak forests of the Central Pine Barrens of Long Island in New York State. By analysis of variance and regression analyses, we found the three methods (particularly densiometer and hemispherical photographs) yielded broadly equivalent and strongly positively correlated descriptions of canopy openness. The direct measurements of solar radiation seemed to have a greater potential to detect subtle variation in forest understory light. Forest managers may sufficiently characterize canopy openness using quick and cheap methods (e.g., spherical densiometers) and avoid larger costs of devices for direct light measurements (e.g., ceptometers) and the larger data processing times of hemispherical photography.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual model has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Machine Learning of Factors Influencing Damping and Frequency of Dominant Inter-area Modes in the WECC Interconnect

The stability of inter-area electromechanical oscillations are critical to power system reliability. Due to the complexities of power systems, relationships between system conditions and oscillation characteristics, such as damping and frequency, tend to be expressed only in generalities. In this study, a list of influential factors on Western Electricity Coordinating Council (WECC) interconnect modal characteristics are identified and evaluated with advanced machine learning techniques including principal component analysis, analysis of variance, random forest feature selection, support vector machine, and artificial neural network approaches.

Hou, Zhangshuan↗
DOE OSTI2016

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗
DOE OSTI2014

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. Methods: The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which a group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers’ similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results: The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10{sup −4} compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a high level of intergroup consistency and agreement in retrieval of similar images. More importantly, perceptually similar cases are also likely to be similar in their underlying benign or malignant pathology and image features of clustered MCs, which could be of diagnostic value in computer-aided diagnosis for lesions with clustered MCs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

MO-E-17A-06: Organ Dose in Abdomen-Pelvis CT: Does TG 111 Equilibrium Dose Concept Better Accounts for KVp Dependence Than Conventional CTDI?

Purpose: In CT imaging, a desirable quality assurance (QA) dose quantity should account for the dose variability across scan parameters and scanner models. Recently, AAPM TG 111 proposed to use equilibrium dose-pitch product, in place of CT dose index (CTDI100), for scan modes involving table translation. The purpose of this work is to investigate whether this new concept better accounts for the kVp dependence of organ dose than the conventional CTDI concept. Methods: The adult reference female extended cardiac-torso (XCAT) phantom was used for this study. A Monte Carlo program developed and validated for a 128-slice CT system (Definition Flash, Siemens Healthcare) was used to simulate organ dose for abdomenpelvis scans at five tube voltages (70, 80, 100, 120, 140 kVp) with a pitch of 0.8 and a detector configuration of 2x64x0.6 mm. The same Monte Carlo program was used to simulate CTDI100 and equilibrium dose-pitch product. For both metrics, the central and peripheral values were used together with helical pitch to calculate a volume-weighted average, i.e., CTDIvol and (Deq)vol, respectively. Results: While other scan parameters were kept constant, organ dose depended strongly on kVp; the coefficient of variation (COV) across the five kVp values ranged between 70–75% for liver, spleen, stomach, pancreas, kidneys, colon, small intestine, bladder, and ovaries, all of which were inside the primary radiation beam. One-way analysis of variance (ANOVA) for the effect of kVp was highly significant (p=3e−30). When organ dose was normalized by CTDIvol, the COV across the five kVp values reduced to 7–16%. The effect of kVp was still highly significant (p=4e−4). When organ dose was normalized by (Deq)vol, the COV further reduced to 4−12%. The effect of kVp was borderline significant (p=0.04). Conclusion: In abdomen-pelvis CT, TG 111 equilibrium dose concept better accounts for kVp dependence than the conventional CTDI. This work is supported by a faculty startup fund from the Cleveland State University.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-C-12A-05: Repeatability Study of Reduced Field-Of-View Diffusion-Weighted MRI On Human Thyroid Gland

Purpose: To investigate the repeatability of reduced field-of-view diffusion-weighted imaging (rFOV DWI) in quantifying apparent diffusion coefficients (ADCs) for human thyroid glands in a clinical setting. Methods: Nine healthy human volunteers were enrolled and underwent 3T MRI exams. For each volunteer, 3 longitudinal exams (2 weeks apart) with 2 repetitive sessions within each exam, including rFOV and conventional full field-of-view (fFOV) DWI scans, were performed. In the acquired DWI images, a fixed-size region of interest (ROI; diameter=8mm) was placed on thyroid glands to calculate ADC. ADC was calculated using a monoexponential function with a noise correction scheme. The repeatability of ADC was assessed by using coefficient variation (CV) across sessions or exams, which was defined to be: r = 1-CV, 0 < r < 1, where CV=STD/m, STD is the standard deviation of ADC, and m is the average of ADC across sessions or exams. An experienced radiologist assessed and scored rFOV and fFOV DW images based on image characteristics (1, nondiagnostic; 2, poor; 3, satisfactory; 4, good; and 5, excellent).Analysis of variance (ANOVA) was performed to compare ADC values, CV of ADC, repeatability of ADC across sessions and exams, and radiologic scores between rFOV and fFOV DWI techniques. Results: There was no significant difference in ADC values across sessions and exams either in rFOV or fFOV DWI. The average CVs of both rFOV and fFOV DWI were less than 13%. The repeatability of ADC measurement between rFOV and fFOV DWI was not significantly different. The overall image quality was significantly higher with rFOV DWI than with fFOV DWI. Conclusion: This study suggested that ADCs from both rFOV and fFOV DWI were repeatable, but rFOV DWI had superior imaging quality for human thyroid glands in a clinical setting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

4D-CT scans reveal reduced magnitude of respiratory liver motion achieved by different abdominal compression plate positions in patients with intrahepatic tumors undergoing helical tomotherapy

Purpose: While abdominal compression (AC) can be used to reduce respiratory liver motion in patients receiving helical tomotherapy for hepatocellular carcinoma, the nature and extent of this effect is not well described. The purpose of this study was to evaluate the changes in magnitude of three-dimensional liver motion with abdominal compression using four-dimensional (4D) computed tomography (CT) images of several plate positions. Methods: From January 2012 to October 2015, 72 patients with intrahepatic carcinoma and divided into four groups underwent 4D-CT scans to assess respiratory liver motion. Of the 72 patients, 19 underwent abdominal compression of the cephalic area between the subxiphoid and umbilicus (group A), 16 underwent abdominal compression of the caudal region between the subxiphoid area and the umbilicus (group B), 11 patients underwent abdominal compression of the caudal umbilicus (group C), and 26 patients remained free breathing (group D). 4D-CT images were sorted into ten-image series, according to the respiratory phase from the end inspiration to the end expiration, and then transferred to treatment planning software. All liver contours were drawn by a single physician and confirmed by a second physician. Liver relative coordinates were automatically generated to calculate the liver respiratory motion in different axial directions to compile the 10 ten contours into a single composite image. Differences in respiratory liver motion were assessed with a one-way analysis of variance test of significance. Results: The average respiratory liver motion in the Y axial direction was 4.53 ± 1.16, 7.56 ± 1.30, 9.95 ± 2.32, and 9.53 ± 2.62 mm in groups A, B, C, and D, respectively, with a significant change among the four groups (p < 0.001). Abdominal compression was most effective in group A (compression plate on the subxiphoid area), with liver displacement being 2.53 ± 0.93, 4.53 ± 1.16, and 2.14 ± 0.92 mm on the X-, Y-, and Z-axes, respectively. There was no significant difference in respiratory liver motion between group C (displacement: 3.23 ± 1.47, 9.95 ± 2.32, and 2.92 ± 1.10 mm on the X-, Y-, and Z-axes, respectively) and group D (displacement: 3.35 ± 1.55, 9.53 ± 2.62, and 3.35 ± 1.73 mm on the X-, Y-, and Z-axes, respectively). Abdominal compression was least effective in group C (compression on caudal umbilicus), with liver motion in this group similar to that of free-breathing patients (group D). Conclusions: 4D-CT scans revealed significant liver motion control via abdominal compression of the subxiphoid area; however, this control of liver motion was not observed with compression of the caudal umbilicus. The authors, therefore, recommend compression of the subxiphoid area in patients undergoing external radiotherapy for intrahepatic carcinoma.

60 APPLIED LIFE SCIENCES↗