Introduction: The conventional anti-scatter grid is widely used in X-ray radiography to reduce scattered X-rays, but it increases patient dose. Scatter-correction software offers a dose-reducing alternative by correcting for scattered X-rays without a physical grid. Grids and software correction are necessary to reduce scatter radiation and improve image quality especially for the large body parts. The scatter correction can be beneficial in situations where the use of grid is challenging. The implementation of grids and advanced software correction techniques is imperative to ensure that radiographic images maintain high levels of clarity, contrast, and resolution, and ultimately facilitating more accurate diagnoses. This study compares image quality and radiation dose for abdomen exams using scatter correction software and physical grids.
Methods: An anthropomorphic phantom (abdomen) underwent imaging with varying fat and lean tissue layers and body mass index (BMI) configurations. Imaging parameters included 70 kVp tube voltage, 110 cm SID, and Automatic Exposure Control (AEC) both lateral and central chambers. AP abdomen X-ray projections were acquired with and without an anti-scatter grid, and scatter correction software was applied. Image quality was assessed using contrast to noise ratio (CNR) and signal to noise ratio (SNR) metrics. The tube current mAs was considered an exposure factor that affected radiation dose and was used to compare the VG software and physical grid. Radiation dose was measured using Dose Area Products (DAP). The effective dose was estimated using Monte Carlo simulation-PCXMC software. Paired t-tests were used to investigate the image quality difference between the Gridless and VG software, Gridless and PG, and VG software and PG approaches. For the DAP and effective dose, paired t-test was used to investigate the difference between VG software and PG.
Results: Images acquired with a grid had the highest mean CNR (71.3 ± 32) compared to Gridless (50 ± 33.8) and scatter correction software (59.3 ± 37.9). The mean SNR of the grid images was (82.7.3 ± 38.9), which is 18% higher than the scatter correction software images (70.4 ± 36.7) and 29% higher than in the Gridless images (62.9.3 ± 34). The mean DAP value was reduced by 81% when the scatter correction software was used compared to the grid (mean: 65.4 μGy.m and 338.2 μGy.m, respectively) with a significant difference (p = 0.001). Scatter correction software resulted in a lower effective dose compared to physical grid use, (mean difference± SD = -0.3 ± 0.18 mSv) with a significant difference (P = 0.02).
Conclusion: Scatter correction software reduced the radiation dose required but images employing a grid yielded higher CNR and SNR. However, the radiation dose reduction might affect the image quality to a level that impacts the diagnostic information available. Thus, further research needs to be conducted to optimise the use of the scatter correction software.
Implication For Practice: Objectively, X-ray scatter correction software might be promising in conditions where a grid cannot be applied.
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http://dx.doi.org/10.1016/j.radi.2024.05.006 | DOI Listing |
Anal Methods
January 2025
Department of Food Science and Postharvest Technology, School of Applied Sciences and Technology, Cape Coast Technical University, Cape Coast, Ghana.
This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms.
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February 2025
State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University University, Wuxi, Jiangsu Province, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, China. Electronic address:
The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed.
View Article and Find Full Text PDFChemosphere
January 2025
TNO Environmental Modelling, Sensing and Analysis, Princetonlaan 6-8, 3584 CB, Utrecht, the Netherlands. Electronic address:
Tyre and road wear particles (TRWPs) are estimated to be the largest source of microplastics in the environment and due to the intrinsic use of tyres in our society this will continue to grow. Understanding their degradation mechanisms and subsequent accumulation over time is important to gain insights into the fate and impact of these particles in the environment. Accelerated UV-ageing was performed on cryomilled tyre tread particles and TRWPs from a road simulator to investigate the abiotic degradation of rubber.
View Article and Find Full Text PDFPhotochem Photobiol
January 2025
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Photodynamic therapy (PDT) has shown promise as an adjuvant treatment for malignant pleural mesothelioma when combined with surgical resection. Accurate light dosimetry is critical for treatment efficacy. This study presents an improved method for analyzing light fluence distribution in pleural PDT using a standardized anatomical coordinate system and advanced computational modeling.
View Article and Find Full Text PDFAnal Methods
January 2025
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin.
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