The aim of this work was to experimentally compare the contrast improvement factors (CIFs) of a newly developed software-based scatter correction to the CIFs achieved by an antiscatter grid. To this end, three aluminium discs were placed in the lung, the retrocardial and the abdominal areas of a thorax phantom, and digital radiographs of the phantom were acquired both with and without a stationary grid. The contrast generated by the discs was measured in both images, and the CIFs achieved by grid usage were determined for each disc. Additionally, the non-grid images were processed with a scatter correction software. The contrasts generated by the discs were determined in the scatter-corrected images, and the corresponding CIFs were calculated. The CIFs obtained with the grid and with the software were in good agreement. In conclusion, the experiment demonstrates quantitatively that software-based scatter correction allows restoring the image contrast of a non-grid image in a manner comparable with an antiscatter grid.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/rpd/ncv432 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT.
J Med Imaging (Bellingham)
December 2024
University of Houston, Department of Physics, Houston, Texas, United States.
Purpose: Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence.
View Article and Find Full Text PDFRapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.
Spectrochim Acta A Mol Biomol Spectrosc
December 2024
College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected.
View Article and Find Full Text PDF[PLSR model based on near-infrared spectroscopy for the detection of wood fiber anatomy of ].
Ying Yong Sheng Tai Xue Bao
October 2024
Research Institute of Subtropical Forestry, Chinese Academy of Forestry/Zhejiang Key Laboratory of Forest Genetics and Bree-ding, Hangzhou 311400, China.
To rapidly acquire fiber phenotypic data for wood quality assessment, we used a portable NIR spectro-meter to collect spectral data in 100 individuals of at 18-year-old of 20 different provenances, and simultaneously collected wood cores. Wood basic density and the anatomical structure of wood fiber were measured. The standard normal variate (SNV), orthogonal signal correction (OSC), and multiplicative scatter correction (MSC) methods were used for spectral preprocessing, the competitive adaptive reweighted sampling (CARS) method were used for wavelength selection, and the partial least squares regression (PLSR) model were established.
View Article and Find Full Text PDFInterlaboratory Measurement of Adeno-Associated Virus: Comparative Quantification of Full and Empty Capsids.
Hum Gene Ther
December 2024
Prevail Therapeutics, New York, New York, USA.
Recombinant adeno-associated virus (AAV) is one of the main viral vector-based gene therapy platforms. AAV is a virus consisting of a ≈25 nm diameter capsid with a ≈4.7 kb cargo capacity.
View Article and Find Full Text PDFPredicting RNA Structure and Dynamics with Deep Learning and Solution Scattering.
Biophys J
December 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel. Electronic address:
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!