Novel EBT3 film calibration using an integrated HSV color space analysis.

PURPOSE: Radiochromic EBT3 films are commonly used as dosimeter for clinical practice and research on radiotherapy. In principle, they are associated with a flatbed scanner to determine the optical density change, which can be correlated to the absorbed dose after calibration. Several approaches have been proposed to reduce the uncertainties during acquisition and to compensate the lighting inhomogeneities, thus improving the dose measurement. Those works have shown that good accuracy can be achieved for absorbed dose using EBT3 films, at the expense of complex data processing and time-consuming acquisition protocols. METHOD: We introduce the new method to determine the calibration curve based on the HSV color space analysis, which provides robustness and invariance to illumination changes. RESULTS: With this new approach, it allows to compute the calibration curve by performing only a single scan of film pieces regardless either the lateral positions or control points on the scanner bed. Using the hue channel in HSV color space, we prove that the dose can be accurately reconstructed with a much simpler protocol than when using RGB channels with blank scans rectification. Our HSV approach includes comparative gamma index for conventional film analysis. It achieves a gamma index (3%/3mm) over 99% when comparing measurement and AAA computation for a modulated beam. CONCLUSION: Compared to most existing approaches, our approach does not rely on complex mathematical reconstructions or additional scans. Instead, it uses another color model representation to rectify the scanner response, coping the dose measurement homogeneity problem over the scanner window. It facilitates the overall scan calibration to be much simpler, save time, and less manipulations, which also decreases the risk of human error.

3-axis computer numerical control machine positioning error dataset for thermal error compensation.

This article reports on a comprehensive dataset detailing positioning errors in a 3-axis milling center machine (MCM) with computer numerical control (CNC) specifically curated for thermal error compensation. The data, which includes separate datasets for the X, Y, and Z axes, was collected through systematic measurements using an interferometric laser (IL) system under monitored thermal conditions. Each axis's acquisition was recorded with a resolution to capture dynamic variations influenced by thermal fluctuations. Temperature measurements were obtained using resistance temperature detectors (RTD) installed in the bearing housings of each axis for monitoring of thermal conditions throughout the data collection process in each axis. The dataset comprises raw positional and error data for each axis alongside metadata describing parameters such as bearing temperature, heating cycle, and machine operating conditions. This dataset can potentially be a valuable resource for researchers, enabling them to develop and validate real-time thermal error compensation algorithms, thereby enhancing CNC machining precision for each axis independently and collectively. Furthermore, the dataset's structured format facilitates comparative studies across different machine configurations and operational contexts, contributing to advancements in manufacturing technology and improvements in process parameter design and optimization.

Fully Integrated Biosensing System for Dynamic Monitoring of Sweat Glucose and Real-Time pH Adjustment Based on 3D Graphene MXene Aerogel.

The development of noninvasive glucose sensors capable of continuous monitoring without restricting user mobility is crucial, particularly for managing diabetes, which demands consistent and long-term observation. Traditional sensors often face challenges with accuracy and stability that curtail their practical applications. To address these issues, we have innovatively applied a three-dimensional porous aerogel composed of Ti3C2Tx MXene and reduced graphene oxide (MX-rGO) in electrochemical sensing. It significantly reduces the electron-transfer distance between the enzyme's redox center and the electrode surface while firmly anchoring the enzyme layer to effectively prevent any leakage. Another pivotal advancement in our study is the integration of the sensor with a real-time adaptive calibration mechanism tailored specifically for analyzing sweat glucose. This sensor not only measures glucose levels but also dynamically monitors and adjusts to pH fluctuations in sweat. Such capabilities ensure the precise delivery of physiological data during physical activities, providing strong support for personalized health management.

Magnification calibration of X-ray 3D microscopy using micro-line structures.

X-ray microscopy using computed tomography (CT) is an excellent three-dimensional imaging instrument. Three-dimensional X-ray microscopy (3DXRM) is a nondestructive imaging technique used to inspect internal and external structures in units of submicrometers or less. The 3DXRM, although attractive, is mostly used as an observation instrument and is limited as a measurement system in quantitative evaluation and quality control. Calibration is required for use in measurement systems such as coordinate measurement systems, and specific standard samples and evaluation procedures are needed. The certified values of the standard samples must ideally be traceable to the International System of Units (SI). In the 3DXRM measurement system, line structures (LSs) are fabricated as prototype standard samples to conduct magnification calibration. In this study, we evaluated the LS intervals using calibrated cross-sectional scanning electron microscopy (SEM). A comparison of the evaluation results between SEM and 3DXRM for the LS intervals provided the magnification calibration factor for 3DXRM and validated the LSs, whereby the interval methods and feasibility of constructing an SI traceability system were evaluated using the calibrated SEM. Consequently, a magnification calibration factor of 1.01 was obtained for 3DXRM based on the intervals of the LSs evaluated by SEM. A possible route for realizing SI-traceable magnification calibration of 3DXRM has been presented.

Heavy metal in radiation therapy: A simple method to differentiate hip prosthesis materials.

BACKGROUND: In recent years, the number of hip replacement patients receiving radiation therapy has steadily increased. In parallel, strategies have been developed to reduce metal artifacts in computed tomography (CT) images and improve the accuracy of dose calculation algorithms. However, in certain situations, knowledge of the type of prosthesis material is required to accurately determine the dose distribution. PURPOSE: This study aims to identify physical materials in hip prostheses to correctly assign them in the treatment planning system and improve dose calculation accuracy. METHODS: We first verified the validity of the extended CT mass density calibration curve measured on titanium (Ti) and stainless steel (SS) metal inserts of two different diameters. Then using dedicated reference objects of various circular diameters, we developed a method based on interpolation functions to differentiate between Ti and SS material groups. Forty data sets from 18 patients were used to validate our method on two different reconstruction kernels: a standard Br44f and the electron DirectDensity (Sd40f) kernels from Siemens. RESULTS: Hounsfield units (HU) of Ti and SS inserts were found to vary widely depending on insert diameter, CT spectrum, and reconstruction kernels due to cupping artifacts. The largest HU difference (-79%) was obtained for SS at 70 kV with Br44f when the diameter increased from 8 to 30 mm. Therefore, under these conditions, the extended CT-density calibration curve is not recommended for heavy metal density determination. Using our interpolation-based method, we achieved excellent detection (100%) and material differentiation (100%) results for stems in both reconstruction kernels. At CT energies between 110 and 140 kV, the detection and material differentiation rates were 93.3% and 92.9% for the heads and 93.3% and 92.9% for the acetabular cups, respectively, with the Br44f. Similarly, the use of Sd40f resulted in detection and differentiation rates of 94.7% and 100% for the heads and 100% and 95.0% for the acetabular cups, respectively. CONCLUSION: This method makes it possible to differentiate between hip prosthesis materials and correctly assign them to the Ti or SS group without prior knowledge of the prosthesis type, regardless of the reconstruction kernels. In combination with the Acuros XB (Varian) or Monte Carlo dose algorithms, excellent dosimetric accuracy can be achieved even in the vicinity of hip prostheses. By performing basic measurements, the method can be adapted to other CT units and reconstruction kernels, replacing the use of an extended CT-density calibration curve.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits.

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial-spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

Advanced mathematical modeling for preciseestimation of CT energy spectrum using a calibration phantom.

PURPOSE: This research aims to develop an advanced mathematical model using a CT calibration phantom to accurately estimate the CT energy spectrum in clinical settings, enhancing imaging quality and patient dose management. METHODS: Data were collected from a CT scanner using a CT calibration phantom at various energy levels (80, 100, 120, and 135 kVp). The data was optimized to refine the energy spectrum model, followed by cross-validation with Monte Carlo simulations. RESULTS: The developed model demonstrated high precision in estimating the CT energy spectrum at all tested energy levels, with R-squared values above 0.9738 and an R-squared value of 0.9829 at 100 kVp. The model also showed low Normalized Root Mean Square Deviation (NRMSD) ranging from 0.6698 % to 1.8745 %. The Mean Energy Difference (ΔE) between the estimated and simulated spectrum consistently remained under 0.01 keV. These results were comparable to recent studies, which reported higher NRMSD and ΔE. CONCLUSIONS: This study presents a significantly improved model for estimating the CT energy spectrum, offering greater accuracy than existing models. Its strengths include high precision and the use of standard equipment and algorithmic values. While the current use of 13 plugs is adequate, incorporating plugs with varied densities could enhance accuracy. This model has potential for improving imaging quality and optimizing patient dosing in clinical applications. Future trends may include extending energy spectrum estimation to megavoltage domains and integrating technologies like EPID and MVCT for better dose distribution prediction in high-energy photon beam therapy.

Ensemble surrogate modeling of advective-dispersive transport with intraparticle diffusion model for column-leaching test.

Column-leaching tests are a common approach for assessing the leaching behavior and resulting environmental risks of contaminated soils and waste materials, which are frequently reused for various construction purposes. The observed breakthrough curves of the contaminants are influenced by the complex dynamics of solute transport and kinetic inter-phase mass transfer. Disentangling these interactions necessitates numerical models. However, inverse modeling and sensitivity analysis can be time-consuming, especially when sorption kinetics are explicitly described by intraparticle diffusion, which requires discretizing the domain both in the flow direction along the column axis and inside the grains. To circumvent the need for such computationally intensive models, we have developed two different ensemble surrogate models. These models employ two separate ensemble methods: random forest stacking and inverse-distance weighted interpolation. Each method is applied to base surrogate models that cover different parts of the parameter space. The base surrogate models use the method of Extremely randomized Trees (ExtraTrees). The defined parameter range is based on the German standard for column-leaching tests. To optimize the base surrogate models, we utilized adaptive-sampling methods based on three distinct infill criteria: maximizing the expected improvement, staying within a certain Mahalanobis distance to the best estimate (both for exploitation), and maximizing the standard deviation (for exploration). The ensemble surrogate model demonstrates excellent performance in emulating the behavior of the original numerical model, with a relative root mean squared error of 0.09. We applied our proposed ensemble surrogate model to estimate the complete posterior parameter distribution using Simulation-Based Inference, specifically Neural Posterior Estimation, to determine the full parameter distribution conditioned on copper-leaching data from two different soils. Samples drawn from the posterior distribution align perfectly with the observed data for both the surrogate and original models.

Modeling the response of sesame (Sesamum indicum L.) to different soil fertility levels under rain-fed conditions in the semi-arid areas of western Tigray, Ethiopia.

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

Repeatability of two methods for estimating scapular kinematics during dynamic functional tasks.

Best practices for scapular motion tracking are still being determined. The repeatability of different scapular kinematic procedures needs to be evaluated. The purpose of this study was to assess the test-retest reliability of two scapular kinematic procedures: double calibration with AMC (D-AMC) and individualized linear modelling (LM). Ten healthy participants had their upper body movement tracked with optical motion capture in two identical sessions. Five scapular calibration poses were performed, and seven dynamic functional tasks were tested. Scapular angles were calculated from both procedures (D-AMC vs LM). The D-AMC approach uses two poses (neutral and maximum elevation) and tracks the scapula with a rigid cluster, while the LM approach predicts scapular positioning from humeral angles based on equations built from the calibration pose data. Angle waveforms and repeatability outcomes were compared. Internal and upward rotation angle waveforms were significantly different (p < 0.05) between kinematic procedures for some tasks, with maximum mean differences up to 17.3° and 23.2°, respectively. Overall, repeatability outcomes were similar between procedures, but the LM approach was slightly better for tilt and the D-AMC approach was notably improved for upward rotation in certain tasks. For example, minimal detectable changes during the Forward Transfer ranged from 6.9° to 11.9° for the D-AMC and 8.9° to 25.3° for the LM. Discrepancies between procedures may be a function of the calibration poses chosen. Additional calibration poses may improve the comparisons between procedures.