Purpose/objective: Stereotactic radiosurgery of brain metastases requires highly conformal dose distributions. Besides beams setup, characteristics of the linear accelerator collimator may also play a role. In this study we compared the impact of leaf width on the dose outside the target for stereotactic radiosurgery of single brain metastases. Results: The mean dose was evaluated in the first 2 rings of 5 mm around the PTV(table 1). The difference in mean dose for the small lesions(Dpres=24 Gy) of the first ring of 5 mm is 1.8 Gy in favor of the Agility and 0.9 Gy for the larger lesions(Dpres=18 Gy)also in favor of the Agility. The difference is smaller for the larger lesions (figure1). Also for the second ring of 5 mm, adjacent to the first ring, the difference is is 1.1 Gy vs 0.8 Gy also in favor of the Agility. Conclusion: For the small lesions with a volume smaller than 4 cm³ the Agility shows a steeper gradient in the two surrounding rings than the MLCi1. Therefore we recommend the use of the Agility for treating the smaller lesions.
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This study explores the capability of the COMPASS system (IBADosimetry, Germany) to detect leaf positioning errors of a highresolution MLC with 4mm leaf width using a lower resolution(7.62mm detector spacing) 2D matrix of ionisation chambers
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Plant photosynthesis and biomass production are associated with the amount of intercepted light, especially the light distribution inside the canopy. Three virtual canopies (n = 80, 3.25 plants/m2) were constructed based on average leaf size of the digitized plant structures: ‘small leaf’ (98.1 cm2), ‘medium leaf’ (163.0 cm2) and ‘big leaf’ (241.6 cm2). The ratios of diffuse light were set in three gradients (27.8%, 48.7%, 89.6%). The simulations of light interception were conducted under different ratios of diffuse light, before and after the normalization of incident radiation. With 226.1% more diffuse light, the result of light interception could increase by 34.4%. However, the 56.8% of reduced radiation caused by the increased proportion of diffuse light inhibited the advantage of diffuse light in terms of a 26.8% reduction in light interception. The big-leaf canopy had more mutual shading effects, but its larger leaf area intercepted 56.2% more light than the small-leaf canopy under the same light conditions. The small-leaf canopy showed higher efficiency in light penetration and higher light interception per unit of leaf area. The study implied the 3D structural model, an effective tool for quantitative analysis of the interaction between light and plant canopy structure.
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The COMPASS system (IBADosimetry) is a quality assurance (QA) tool whichreconstructs 3D doses inside a phantom or a patient CT. The dose is predictedaccording to the RT plan with a correction derived from 2D measurementsof a matrix detector. This correction method is necessary since a directreconstruction of the fluence with a high resolution is not possible becauseof the limited resolution of the matrix used, but it comes with a blurring of thedosewhich creates inaccuracies in the dose reconstruction. This paper describesthe method and verifies its capability to detect errors in the positioning of aMLC with 10 mm leaf width in a phantom geometry. Dose reconstruction wasperformed forMLC position errors of various sizes at various locations for bothrectangular and intensity-modulated radiotherapy (IMRT) fields and comparedto a reference dose. It was found that the accuracy with which an error inMLCposition is detected depends on the location of the error relative to the detectorsin the matrix. The reconstructed dose in an individual rectangular field for leafpositioning errors up to 5 mm was correct within 5% in 50% of the locations.At the remaining locations, the reconstruction of leaf position errors larger than3 mm can show inaccuracies, even though these errors were detectable in thedose reconstruction. Errors larger than 9 mm created inaccuracies up to 17% ina small area close to the penumbra. The QA capability of the system was testedthrough gamma evaluation. Our results indicate that themean gamma providedby the system is slightly increased and that the number of points above gamma 1ensures error detection for QA purposes. Overall, the correction kernel methodused by the COMPASS system is adequate to perform QA of IMRT treatmentplans with a regular MLC, despite local inaccuracies in the dose reconstruction.
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This study presents an automated method for detecting and measuring the apex head thickness of tomato plants, a critical phenotypic trait associated with plant health, fruit development, and yield forecasting. Due to the apex's sensitivity to physical contact, non-invasive monitoring is essential. This paper addresses the demand for automated, contactless systems among Dutch growers. Our approach integrates deep learning models (YOLO and Faster RCNN) with RGB-D camera imaging to enable accurate, scalable, and non-invasive measurement in greenhouse environments. A dataset of 600 RGB-D images captured in a controlled greenhouse, was fully preprocessed, annotated, and augmented for optimal training. Experimental results show that YOLOv8n achieved superior performance with a precision of 91.2 %, recall of 86.7 %, and an Intersection over Union (IoU) score of 89.4 %. Other models, such as YOLOv9t, YOLOv10n, YOLOv11n, and Faster RCNN, demonstrated lower precision scores of 83.6 %, 74.6 %, 75.4 %, and 78 %, respectively. Their IoU scores were also lower, indicating less reliable detection. This research establishes a robust, real-time method for precision agriculture through automated apex head thickness measurement.
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Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.
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