Volume Estimation Research Articles

Cardiac dysfunction in dialysing adults with end-stage kidney disease is associated with exercise intolerance: A pilot observational study.

People with end-stage kidney disease (ESKD) often exhibit impaired cardiac structure and function, which may contribute to poor exercise capacity. This study used multimodal exercise testing to investigate the central and peripheral mechanisms of exercise limitation in adults with ESKD, also comparing in-centre hemodialysis (ICHD) to home hemodialysis (HHD). Seventeen adults (55.5 ± 14.5 years; n = 14 male; n = 12 HHD) participated. Resting cardiac examinations, followed by submaximal cycling cardiopulmonary exercise testing (CPET) and functional exercise testing, revealed cardiac structural abnormalities (increased left ventricular mass) and cardiac injury. Aerobic fitness in adults with ESKD was low, with pulmonary oxygen uptake (V̇O2) at the gas exchange threshold (GET) occuring at 39 ± 8% predicted V̇O2peak. O2 pulse, an estimate of stroke volume (SV), was higher in HHD at rest (p = 0.05, ES = 0.58) and during unloaded cycling (p = 0.05, ES = 0.58) compared to ICHD. However, thoracic bioreactance derived SV at the GET was significantly higher in adults receiving ICHD versus HHD (p = 0.01, ES = 0.74). In adults with ESKD, cardiac output was positively associated with V̇O2 at the GET (r = 0.61, p = 0.04). This study highlights prevalent exercise dysfunction in adults with ESKD undergoing dialysis, with potential distinct differences between in-centre and home hemodialysis, mechanistically linked to underlying cardiac abnormalities.

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), with median volume estimation errors of 19.6% (IQR, 13.2%-29.0%) and 5.3% (IQR: 2.4%-9.7%), respectively. On UofW-LC (124 patients; mean age, 46 years ± 12; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with r2 values of 0.79, 0.98, and 0.97 across the test sets. Conclusion The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in patients with cirrhosis and those with ovarian cancer, in concordance with expert radiologist assessments. Keywords: Abdomen/GI, Cirrhosis, Deep Learning, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also commentary by Aisen and Rodrigues in this issue.

Prediction model for intraoperative implant volume using the 3D surface imaging system (VECTRA XT 3D) in direct-to-implant breast reconstructions.

In direct-to-implant breast reconstruction, accurate preoperative breast volume estimation is crucial for surgeons to select the appropriate implant volume, considering the cosmetic outcomes during surgery. We proposed the prediction model for intraoperative implant volume based on the preoperative estimated volume of the contralateral breast obtained through a three-dimensional surface imaging system (3DSI) as surgeons usually choose the implant volume on the breast which should be reconstructed considering symmetricity with the contralateral breast. We enrolled 97 patients from our single institution who underwent unilateral mastectomy with immediate breast reconstruction using smooth silicone implants between October 2021 and January 2023. Preoperatively, plastic surgeons measured the volume of the contralateral breast using the VECTRA XT 3D imaging system. Data on implant volume and the types of acellular dermal matrix used during surgery, determined by a single surgeon to ensure symmetry, were also collected. Linear regression analysis was utilized to construct the predictive model. In the multiple linear regression analysis with preoperative contralateral breast volume, age, and body mass index as variables, the coefficient of determination of the model expressed as R squared (R2) was 0.554, and except for age, the other variables were statistically significant. When replaced by mastectomy volume instead of age, R2 increased to 0.723 and all variables were significant. 3DSI can be helpful for preoperative surgical planning and postoperative outcome simulation. With our multiple linear regression model, we can predict the intraoperative implant volume using preoperative contralateral breast volume measured by the 3D scans.

Improving linear programming model for assessing product sales taking into account social responsibility of consumers

The object of this study is the process of analyzing the volume of product sales in the retail market, where producers and consumers with different levels of social responsibility operate. Advancements are aimed at solving the problem of insufficient consideration of the social responsibility of economic entities in modern economic and mathematical models. In the work, the improvement of the linear programming model, intended for the evaluation of sales of alternative variants of goods with different social utility to consumers with different social responsibility, by taking into account some real conditions of making purchases, was carried out. As a result of the study, a modification of the model was proposed that makes it possible to take into account the behavior of buyers who choose a product randomly. Also, with the help of additional restrictions, the model takes into account the fact that consumers with different social responsibility make purchases in parallel in time. In addition, the approach to identifying a set of consumer groups in the aspect of social responsibility using the method of questionnaires of the ranking type and building a decision tree of consumers is described. The proposed modification of the model was tested on the example of evaluating the sales volumes of environmentally-friendly and non-environmentally-friendly varieties of goods in the office paper market. It is shown that not taking into account the added restrictions in the model could significantly affect the estimates of sales volumes. The proposed improvements to the model make it possible to obtain more realistic estimates of the volume of sales of goods, taking into account the social responsibility of consumers, as well as quantitative and qualitative characteristics of competitors’ offers. This contributes to better decision-making support for manufacturing socially useful products, in particular, environmentally-friendly ones

Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data

ABSTRACT The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates the key components of the GHSL, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today, e.g. achieving an IoU of 0.98 for the water class, 0.92 for the built-up class, and 0.8 for the non-residential class at 10 m resolution. At 100 m resolution, we find that the MAE of built-up surface estimates corresponds to 6% of the grid cell area, the MAE for the building height estimates is 2.27 m, and we find a total allocation accuracy of 83% for resident population. This paper consolidates the theoretical foundation of the GHSL and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.

Thinning relationships of woody encroachers in a US southwestern shrubland

Woody plant encroachment degrades the economic and environmental potential of drylands by altering processes such as nutrient fluxes, ecohydrology, and ecosystem services. Though past research has investigated the encroachment process, relatively little is known about post-encroachment shrub community ecology. To better quantify dynamics within post-woody encroachment shrub communities, we combined USGS lidar height data and multispectral imagery to estimate shrub density, shrub height, shrub cover, and shrub volume across the Jornada Basin Long Term Ecological Research (JRN-LTER) site in southern New Mexico, USA. Structural estimates were analyzed in search of telltale signs of community competition, specifically, the presence of thinning relationships. Results demonstrated density-dependent thinning relationships in shrub communities of creosote and mesquite, indicating a large role for competition in arid shrub communities even at relatively low shrub densities and cover (∼35% cover). In addition, shrub volume estimates better modeled the expected thinning dynamics of shrub communities than shrub canopy cover measurements. Overall, our results indicate the utility of lidar data in extending two-dimensional descriptions of woody vegetation structure (i.e., woody canopy cover) into the critical third dimension (i.e., woody plant volume), as well as the relative importance of competition and demographic bottlenecks to vegetation structure in drylands.

Sonographic findings using the SAFE-A protocol in pre- and post-hemodialysis patients

BackgroundAccurate assessment of relative intravascular volume is one of the cornerstones for the proper management of hospitalized patients requiring hemodialysis. Currently, the use of dynamic parameters such as bedside ultrasonography is recommended to support the assessment of the intravascular volume profile. This study aimed to prospectively evaluate findings of sonographic assessment of intravascular volume estimate (SAFE-A) protocol among hemodialysis inpatients with end-stage renal disease, before and after the hemodialysis sessions, and correlate these findings with the net ultrafiltrate (UFNET).ResultsA positive correlation was found between the negative variation of 1 point in the score of the SAFE-A protocol with the withdrawal of 426.73 mL of net ultrafiltrate.ConclusionsThere was a strong correlation between the score of the SAFE-A protocol and the net ultrafiltrate. Therefore, this study concludes that the application of the SAFE-A protocol in dialysis patients demonstrates a correlation between the suggested score and volume status, consistent with findings from the original study conducted in a distinct population.

CineVN: Variational network reconstruction for rapid functional cardiac cine MRI.

To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients. The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated. CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods. Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.

Sex estimation using long bones in the largest burial site of the Copper Age: Linear discriminant analysis and random forest

Sex estimation of the individuals in a sample is fundamental for any bioarchaeological study to define a particular demographic assemblage or to classify isolated remains. Long bones are an excellent alternative for sex estimation when the most dimorphic anatomical parts are not preserved or are highly altered. Here we propose a set of discriminant functions and classification models to estimate the sex of prehistoric individuals using linear discriminant analysis and machine learning approaches. Different osteometric variables were taken from the humeri, ulnae, radii, femurs and tibias of a sample of 109 articulated skeletons buried in the collective tomb of Camino del Molino (Region of Murcia, SE-Spain), dated to the 3rd millennium BC. Sex was estimated based on standard anthropological methods and ancient DNA analysis of a control sample. Fifty-two discriminant functions with prediction thresholds higher than 0.8 on the ROC curve were obtained using independent (22) and combined variables (30). The best LDA models for sex prediction were those based on proximal epiphyseal widths or their combination with other variables, reaching values close to 0.98 on the ROC curve. The random forest-based model obtained an accuracy of 0.94 and confirmed the importance of epiphyseal widths in sex classification. This analysis is more comprehensive than univariate LDA, as it allows for ranking the importance of bones in sex discrimination and considers correlations between long bones rather than treating them as independent observations. In contrast, applying LDA to each bone makes it easier to predict the sex of other coeval collections that do not have such a complete sample. This work aims to overcome the scarcity of methods that can be applied to sex estimation of the large volume of isolated remains from Camino del Molino and for other Mediterranean skeletal series from the Late Prehistory with high biological affinity and that share similar environmental conditions.

DeepBreath: Breathing Exercise Assessment with a Depth Camera

Practicing breathing exercises is crucial for patients with chronic obstructive pulmonary disease (COPD) to enhance lung function. Breathing mode (chest or belly breathing) and lung volume are two important metrics for supervising breathing exercises. Previous works propose that these metrics can be sensed separately in a contactless way, but they are impractical with unrealistic assumptions such as distinguishable chest and belly breathing patterns, the requirement of calibration, and the absence of body motions. In response, this research proposes DeepBreath, a novel depth camera-based breathing exercise assessment system, to overcome the limitations of the existing methods. DeepBreath, for the first time, considers breathing mode and lung volume as two correlated measurements and estimates them cooperatively with a multitask learning framework. This design boosts the performance of breathing mode classification. To achieve calibration-free lung volume measurement, DeepBreath uses a data-driven approach with a novel UNet-based deep-learning model to achieve one-model-fit-all lung volume estimation, and it is designed with a lightweight silhouette segmentation model with knowledge transferred from a state-of-the-art large segmentation model that enhances the estimation performance. In addition, DeepBreath is designed to be resilient to involuntary motion artifacts with a temporal-aware body motion compensation algorithm. We collaborate with a clinical center and conduct experiments with 22 healthy subjects and 14 COPD patients to evaluate DeepBreath. The experimental result shows that DeepBreath can achieve high breathing metrics estimation accuracy but with a much more realistic setup compared with previous works.

Volumetric Assessment of Traumatic Intracranial Hematomas: Is ABC/2 Reliable?

Accurate measurement of traumatic intracranial hematoma volume is important for assessing disease progression and prognosis, as well as for serving as an important end-point in clinical trials aimed at preventing hematoma expansion. While the ABC/2 formula has traditionally been used for volume estimation in spontaneous intracerebral hemorrhage, its adaptation to traumatic hematomas lacks validation. This study aimed to compare the accuracy of ABC/2 with computer-assisted volumetric analysis (CAVA) in estimating the volumes of traumatic intracranial hematomas. We performed a dual-center observational study that included adult patients with moderate-to-severe traumatic brain injury. Volumes of intracerebral, subdural (SDHs), and epidural hematomas from admission computed tomography scans were measured using ABC/2 and CAVA, and compared using the Wilcoxon signed-rank test, Spearman's rank correlation, Lin's concordance correlation coefficient (CCC), and Bland-Altman plots. Prognostic significance for outcomes was evaluated through logistic and linear regression models. In total, 1,179 patients with 1,543 hematomas were included. Despite a high correlation (Spearman coefficients between 0.95 and 0.98) and excellent concordance (Lin's CCC from 0.89 to 0.96) between ABC/2 and CAVA, ABC/2 overestimated hematoma volumes compared with CAVA, in some instances exceeding 50 ml. Bland-Altman analysis highlighted wide limits of agreement, especially in SDH. While both methods demonstrated comparable accuracy in predicting outcomes, CAVA was slightly better at predicting craniotomies and midline shift. We conclude that while ABC/2 provides a generally reliable volumetric assessment suitable for descriptive purposes and as baseline variables in studies, CAVA should be the gold standard in clinical situations and studies requiring more precise volume estimations, such as those using hematoma expansion as an outcome.

More similarity than difference: comparison of within- and between-sex variance in early adolescent brain structure.

Adolescent neuroimaging studies of sex differences in the human brain predominantly examine mean differences between males and females. This focus on between-groups differences without probing relative distributions and similarities may contribute to both conflation and overestimation of sex differences and sexual dimorphism in the developing human brain. We aimed to characterize the variance in brain macro- and micro-structure in early adolescence as it pertains to sex at birth using a large sample of 9-11 year-olds from the Adolescent Brain Cognitive Development (ABCD) Study (N=7,723). Specifically, for global and regional estimates of gray and white matter volume, cortical thickness, and white matter microstructure (i.e., fractional anisotropy and mean diffusivity), we examined: within- and between-sex variance, overlap between male and female distributions, inhomogeneity of variance via the Fligner-Killeen test, and an analysis of similarities (ANOSIM). For completeness, we examined these sex differences using both uncorrected (raw) brain estimates and residualized brain estimates after using mixed-effects modeling to account for age, pubertal development, socioeconomic status, race, ethnicity, MRI scanner manufacturer, and total brain volume, where applicable. The overlap between male and female distributions was universally greater than the difference (overlap coefficient range: 0.585 - 0.985) and the ratio of within-sex and between-sex differences was similar (ANOSIM R range: -0.001 - 0.117). All cortical and subcortical volumes showed significant inhomogeneity of variance, whereas a minority of brain regions showed significant sex differences in variance for cortical thickness, white matter volume, fractional anisotropy, and mean diffusivity. Inhomogeneity of variance was reduced after accounting for other sources of variance. Overlap coefficients were larger and ANOSIM R values were smaller for residualized outcomes, indicating greater within- and smaller between-sex differences once accounting for other covariates. Reported sex differences in early adolescent human brain structure may be driven by disparities in variance, rather than binary, sex-based phenotypes. Contrary to the popular view of the brain as sexually dimorphic, we found more similarity than difference between sexes in all global and regional measurements of brain structure examined. This study builds upon previous findings illustrating the importance of considering variance when examining sex differences in brain structure.

A Validated Estimate of Visceral Adipose Tissue Volume in Relation to Cancer Risk.

Despite the recognized role of visceral adipose tissue (VAT) in carcinogenesis, its independent association with cancer risk beyond traditional obesity measures remains unknown due to limited availability of imaging data. We developed an estimation equation for VAT volume (L) using Elastic Net Regression based on demographic and anthropometric data in a subcohort of participants in the UK Biobank (UKB; N = 23,148) with abdominal MRI scans. This equation was externally validated in 2,713 participants from the 2017-2018 National Health and Nutrition Examination Survey (NHANES) according to sex, age, and race groups. We then applied the equation to the overall UKB cohort of 461,665 participants to evaluate the prospective association between estimated VAT (eVAT) and cancer risk using Cox proportional hazards models. We also calculated the population attributable risk (PAR) of cancer associated with eVAT and BMI. eVAT showed a high correlation with measured VAT in internal and external validations (r = 0.81-0.86). During a median 12-year follow-up in the UKB, we documented 37,397 incident cancer cases; eVAT was significantly associated with elevated risk of obesity-related and individual cancers, independent of BMI and waist circumference. PAR for total cancer associated with high (quartiles 2-4 vs 1) eVAT (9.0-11.6%) was higher than high BMI (Q2-4 vs 1; 5.0-8.2%). eVAT showed robust performance in both UKB and NHANES and was associated with cancer risk independent of BMI and waist circumference. This study provides a potential clinical tool for VAT estimation and underscores that VAT can be an important target for cancer prevention.

Estimating merchantable and non-merchantable wood volume in slash walls using terrestrial and airborne LiDAR

Slash walls are an effective forest management practice to prevent deer herbivory and promote forest regeneration following a timber harvest. However, the material cost, i.e. amount of wood material, needed for quality slash walls is unknown or at best based on highly uncertain empirical estimation. Quantifying how total and merchantable timber wood volume varies with width and height across slash walls will facilitate future planning and application of slash walls. In this study, we estimated wood volume per unit length (∼30.48 m) from total stem cross-sectional area in 40 randomly selected cut-through passages cut into 14 slash walls located at Cornell University’s Arnot Teaching and Research Forest (ATRF) in south-central New York. Within each passage we used Terrestrial Laser Scanning (TLS) to identify individual stems and obtain cross-sectional surface area. We found that TLS derived stem diameters were highly consistent with manual stem diameter measurements for 10 selected wall passages (N = 308, R2 = 0.933, RMSE = 1.881 cm). However, TLS tends to underestimate total cross-sectional area due to omission of small stem cross-sections (diameter < 5.08 cm). Cross-sections of small stems often have a low number of total points even when point density is high, especially when they are not facing the scanner. They are also more vulnerable to occlusion (i.e., lack of points due to blocking by other stems). TLS-based wood volume estimates were similar across both short and tall slash walls in the study with an average volume of 21.34 m3/30 m for short slash walls and 21.50 m3/30 m for tall slash walls, corresponding to 16.9 t dry biomass /30 m and 17.0 t dry biomass /30 m respectively. The small difference in woody volume was due to tall slash walls achieving additional height mainly through non-compacted small diameter crown and brush tops that have little woody volume. Average merchantable (diameter > 15.24 cm) and non-merchantable (diameter < 15.24 cm) timber fraction was 68.75 % and 31.35 % for short slash walls and 65.04 % and 34.96 % for tall slash walls. We further assessed slash wall height and width using Airborne Laser Scanning (ALS) for eight other tall slash walls. We found TLS measurements are representative of whole slash wall height and width variability based on ALS measurements. Our results provide novel data and methodology to estimate the construction cost of slash walls which are critical for optimizing slash wall applications.

Liquid territories: the configuration of geographic space in the cartography of the Mekong River’s catchment areas

AbstractThe role played by the Mekong River in the organization of land and people is inextricably linked with a particular spatial category. The concept of the hydrological catchment extends the space of the river far beyond the limits of the river’s perennial waterbodies, to encompass vast areas inhabited by millions of people speaking different languages. Fundamental to the estimation of precipitation and water volume, areal denotations of the Mekong’s basin, delta and floodplain have been repeatedly drawn on maps by geographers, planners, engineers and cartographers. Mapped representations of the Mekong River however are not only the result of recording the flows of water, nor the domain of a single discourse. With diverging intentions, distinct and sometimes conflicting projections of the basin, delta and floodplain have prescribed the differentiation and unification of parts of mainland Southeast Asia, to articulate liquid territories that are outside a single state’s jurisdiction. To understand how the Mekong’s catchments emerge as the geographic reference for human activities, the paper examines the technical and cultural notions that underpin the preparation of these maps. Focusing on early European maps of the Mekong’s delta and floodplain, the paper draws on the discourses of hydrology, geography, and cartography to ask what territories are produced and maintained by evoking the geography of the river’s flows.

Relative Blood Volume Monitoring during Continuous Renal Replacement Therapy: A Prospective Observational Study.

Hematocrit monitoring during continuous renal replacement therapy (CRRT) allows the continuous estimation of relative blood volume (RBV). This may enable early detection of intravascular volume depletion prior to clinical sequelae. We aimed to investigate the feasibility of extended RBV monitoring and its epidemiology during usual CRRT management by clinicians unaware of RBV. Moreover, we studied the association between changes in RBV and net ultrafiltration (NUF) rates. In a cohort of adult intensive care unit patients receiving CRRT, we continuously monitored hematocrit and RBV using a pre-filter noninvasive optical sensor. We analyzed temporal changes in RBV and investigated the association between RBV change and NUF rates, using the classification of NUF rates into low, moderate, or high based on predefined cut-offs. We obtained &gt;60,000 minute-by-minute measurements in &gt;1,000 CRRT hours in 36 patients. The median RBV change was negative (decrease) in 69% of patients and the median peak change in RBV was -9.3% (interquartile range: -3.9% to -14.3%). Moreover, the median RBV decreased from baseline by &gt;5% in 40.2% of measurements and by &gt;10% in 20.6% of measurements. Finally, RBV decreased significantly more when patients received a high NUF rate (&gt;1.75 mL/kg/h) compared to low or moderate NUF rates (5.32% vs. 1.93% or 1.97%, p &lt; 0.001). Continuous hematocrit and RBV monitoring during CRRT was feasible. RBV decreased significantly during CRRT, and decreases were greater with higher NUF rates. RBV monitoring may help optimize NUF management and prevent the occurrence of intravascular volume depletion.

Dosimetric and radiobiological advantages from deep inspiration breath-hold and free breath technique for left-sided breast radiation using 3DCRT, IMRT and Rapid Arc methods-a complete assessment.

The verification and use of the best treatment approach using 3D conformal radiation therapy (3DCRT), intensity modulated radiation therapy (IMRT) and Rapid Arc methods for left breast radiation with dosimetric and radiobiological characteristics. The use of custom-built Python software for the estimation and comparison of volume, mean dose, maximum dose, monitor units and normal tissue integral dose along with radiobiological parameters such as NTCP, tumor control probability, equivalent uniform dose and LKB's effective volume from 3DCRT, IMRT and Rapid Arc planning with deep inspiration with breath holding (DIBH) and free breadth (FB) techniques. Volume growth of three-fourth in DIBH compared with FB causes a decrease in cardiac doses and complications because the left lung expands, pulling the heart away from the chest wall and the treatment area. A tiny area of the left lung was exposed during treatment, which reduced the mean dose. There was little difference in the treatment approaches because the spinal cord was immobile in both techniques. Rapid Arc is the unmatched modality for left-sided breast irradiation with significant patient breath-hold, as shown by the comparison of dosimetric and radiobiological parameters from treatment techniques with a deep inspiration breath-hold approach.

Evaluating Linear Heuristics for Ventricular Volume in Healthy Adults Using a Fully Automated Algorithm: Implications for Defining the Normal.

Linear metrics for ventricular volume play a large role in the rapid, approximate evaluation of ventricular volume. In this article, we automatically extract linear measures of ventricular volume to explore their correlation with lateral ventricular volume (LVV) in the healthy adult population and comprehensively define normal values. We automatically extract Evans' ratio (ER), Frontal-Occipital Horn Ratio (FOHR), and anteroposterior lateral ventricle index (ALVI) from an open MRI data set of healthy adults (https://brain-development.org/ixi-dataset/). Indices have been correlated with corresponding LVVs and lateral ventricular volumes divided by supratentorial brain volumes. Spearman rank correlation was used to compare strength of correlation. ER shows correlation with lateral ventricle volume based on sex (r = 0.58; men, r = 0.65; women P < .001), including when controlling for supratentorial volume (r = 0.57; men, r = 0.63). ER did not profoundly correlate with age (r = 0.29, men; r = 0.35, women; P < .001) and seemed normally distributed around 0.25. ALVI showed strong correlation with LVV with only slight gender differences (r = 0.83, men; r = 0.84, women) and LVV to supratentorial cortical volume ratio (r = 0.9, men; r = 0.86, women). FOHR was also normally distributed around a value of 0.37 and showed moderate correlation with LVV (r = 0.68, men; r = 0.73, women) and LVV to supratentorial cortical volume ratio (r = 0.69, men; r = 0.74, women). ALVI is a newer index with strong correlation with LVV and has strong potential for clinical use. Both FOHR and ER show moderate correlation with LVV. Reference values for linear estimates of ventricular volume may help clinicians better identify patients with pathological ventriculomegaly.

Toward field-scale groundwater pumping and improved groundwater management using remote sensing and climate data

Groundwater overdraft in the western United States has prompted water managers to develop groundwater management plans that include mandatory reporting of groundwater pumping (GP). However, most irrigation systems in this region are not equipped with irrigation water flow meters to record GP and performing quality control of the available metered GP data is difficult due to the scarcity of reliable secondary GP estimates. We hypothesize that Landsat-based actual evapotranspiration (ET) estimates from OpenET can be used to predict GP and aid in quality control of the metered GP data. The objectives of this study are to: 1) pair OpenET estimates of consumptive use (Net ET, i.e., actual ET less effective precipitation) and metered annual GP data from Diamond Valley, Nevada, and Harney Basin, Oregon; 2) evaluate linear regression and machine learning models to establish the GP vs Net ET relationship; and 3) compare GP estimates at the field- and basin-scales. Results from using a bootstrapping technique showed that the mean absolute errors and root mean square errors for field-scale GP depth are ∼11 % and ∼14 % across Diamond Valley and Harney Basin based on the OpenET ensemble mean, which showed the highest skill among all the OpenET ET models. Moreover, the regression models explained 50 %-70 % variance in GP depth and ∼90 % variance in GP volumes. Our GP volume estimates are also within 7 % and 17 % of the total reported and measured volumes in Diamond Valley and Harney Basin, respectively, and the estimated average irrigation efficiency of 87 % aligns with known center-pivot system efficiencies. Additionally, the OpenET ensemble proves to be useful for identifying discrepancies in metered GP data, which are subsequently flagged as outliers. Results from this study illustrate usefulness of satellite-based ET estimates for estimating GP and metered GP data quality control and have the potential to help estimate historical GP.

Development of Local Volume Table for Pinus caribbaea var. hondurensis (Senecl) in Area J4, Omo Forest Reserve, Nigeria

Volume estimation is a crucial part of every forest or plantation because it provides the quantity of timber or wood available at a given period and predicts future or expected growth of the forest. Unfortunately, local volume table are not readily available for some–economically important tree species in Nigeria. Hence, it is of paramount importance that local volume tables for economic tree species such as Pinus caribbaea in Area J4, Omo Forest reserve, Ogun state, Nigeria. Aim: The aim of this study is to develop a local volume table for Pinus caribbaea in Area J4, Omo Forest Reserve, Ogun State, Nigeria. Study Design: Simple random sampling was used for this study. Ten Temporary Sample Plots (TSP) of equal size 25m x 25m were randomly located in the selected plantation (P. caribbaea: established in 1997). All the trees with diameter at breast height (dbh) ≥ 10cm in each TSP were enumerated. Place and Duration of Study: The study was carried out in the Pinus caribbaea plantation in Area J4, Omo forest reserve, Ogun State from November 9th to 20th, 2023. Methodology: The total tree height, merchantable height, diameters at the top, middle, base, and dbh were measured and used to estimate stand volume. A volume table was developed using five selected models, and the best model was selected using least Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Data were analyzed using descriptive statistics and regression at α0.05 with R statistical software. Results: The results obtained in P. caribbaea plantation revealed that the dbh and height were 27.93±6.95 cm and 28.72±3.47 m, respectively, while individual tree volume was 0.83±0.57m3. The combined variable model ( was selected as the best model to develop local volume table for P. caribbaea (AIC = -54.73, BIC = -45.49 and RMSE = 0.20). The statistical metrics implied a suitable selected model which can be use for future predictions with minimal error. Conclusion: The developed volume table for estimating the quantity of wood or timber available in the Pinus caribaea plantation at a given time. The volume model and local volume table developed are to be applied only to the Pinus caribaea plantation in the study area for continuous estimation and valuation of the species.