scholarly journals Feasibility of the Quantitative Assessment Method for CT Quality Control in Phantom Image Evaluation

2021 ◽  
Vol 11 (8) ◽  
pp. 3570
Author(s):  
Ki Baek Lee ◽  
Ki Chang Nam ◽  
Ji Sung Jang ◽  
Ho Chul Kim
Keyword(s):  
Quality Control ◽  
Quantitative Methods ◽  
Principal Component ◽  
Assessment Method ◽  
Ct Scanner ◽  
Quantitative Ct ◽  
Phantom Image ◽  
Contrast Resolution ◽  
Pixel Value ◽  
High Utility

Computed tomography (CT) quality control (QC) is regularly performed with standard phantoms, to bar faulty equipment from medical use. Its accuracy may be improved by replacing qualitative methods based on good visual distinction with pixel value-based quantitative methods. We hypothesized that statistical texture analysis (TA) that covers the entire phantom image would be a more appropriate tool. Therefore, our study devised a novel QC method based on the TA for contrast resolution (CR) and spatial resolution (SR) and proposed new, quantitative CT QC criteria. TA of CR and SR images on an American Association of Physicists in Medicine (AAPM) CT Performance Phantom were performed with nine CT scanner models. Six texture descriptors derived from first-order statistics of grayscale image histograms were analyzed. Principal component analysis was used to reveal descriptors with high utility. For CR evaluation, contrast and softness were the most accurate descriptors. For SR evaluation, contrast, softness, and skewness were the most useful descriptors. We propose the following ranges: contrast for CR, 29.5 ± 15%, for SR, 29 ± 10%; softness for CR, <0.015, for SR, <0.014; and skewness for SR, >−1.85. Our novel TA method may improve the assessment of CR and SR of AAPM phantom images.

scholarly journals Quantitative and semi-quantitative CT assessments of lung lesion burden in COVID-19 pneumonia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaojun Guan ◽  
Liding Yao ◽  
Yanbin Tan ◽  
Zhujing Shen ◽  
Hanpeng Zheng ◽  
...  
Keyword(s):  
Quantitative Assessment ◽  
Quantitative Methods ◽  
Assessment Method ◽  
Lung Lesion ◽  
Common Type ◽  
Assessment Methods ◽  
Lesion Volume ◽  
Quantitative Ct ◽  
Lung Lesions ◽  
Severe Type

AbstractThis study aimed to clarify and provide clinical evidence for which computed tomography (CT) assessment method can more appropriately reflect lung lesion burden of the COVID-19 pneumonia. A total of 244 COVID-19 patients were recruited from three local hospitals. All the patients were assigned to mild, common and severe types. Semi-quantitative assessment methods, e.g., lobar-, segmental-based CT scores and opacity-weighted score, and quantitative assessment method, i.e., lesion volume quantification, were applied to quantify the lung lesions. All four assessment methods had high inter-rater agreements. At the group level, the lesion load in severe type patients was consistently observed to be significantly higher than that in common type in the applications of four assessment methods (all the p < 0.001). In discriminating severe from common patients at the individual level, results for lobe-based, segment-based and opacity-weighted assessments had high true positives while the quantitative lesion volume had high true negatives. In conclusion, both semi-quantitative and quantitative methods have excellent repeatability in measuring inflammatory lesions, and can well distinguish between common type and severe type patients. Lobe-based CT score is fast, readily clinically available, and has a high sensitivity in identifying severe type patients. It is suggested to be a prioritized method for assessing the burden of lung lesions in COVID-19 patients.

Chemical Fingerprint Analysis and Simultaneous Determination of Nucleosides and Amino Acids in Kang Fu Xin Liquid by High Performance Liquid Chromatography with Diode Array Detector

2020 ◽  
Vol 16 (7) ◽  
pp. 831-843
Author(s):  
Yuwen Wang ◽  
Shuping Li ◽  
Liuhong Zhang ◽  
Shenglan Qi ◽  
Huida Guan ◽  
...  
Keyword(s):  
Amino Acids ◽  
Quality Control ◽  
Uric Acid ◽  
Control Method ◽  
Principal Component ◽  
Gradient Elution ◽  
Hplc Method ◽  
Array Detector ◽  
Fingerprint Analysis ◽  
Wavelength Switching

Background and Objective: Kang Fu Xin liquid (KFX) is an official preparation made from the ethanol extract product from P. Americana. The present quality control method cannot control the quality of the preparation well. The aim of the present study is to establish a convenient HPLC method for multicomponents determination combined with fingerprint analysis for quality control of KFX. Methods: An HPLC-DAD method with gradient elution and detective wavelength switching program was developed to establish HPLC fingerprints of KFX, and 38 batches of KFX were compared and evaluated by similarity analysis (SA), hierarchical clustering analysis (HCA), and principal component analysis (PCA). Meanwhile, six nucleosides and three amino acids, including uracil, hypoxanthine, uric acid, adenosine, xanthine, inosine, tyrosine, phenylalanine and tryptophan in KFX were determined based on the HPLC fingerprints. Results: An HPLC method assisted with gradient elution and wavelength switching program was established and validated for multicomponents determination combined with fingerprint analysis of KFX. The results demonstrated that the similarity values of the KFX samples were more than 0.845. PCA indicated that peaks 4 (hypoxanthine), 7 (xanthine), 9 (tyrosine), 11, 13 and 17 might be the characteristic contributed components. The nine constituents in KFX, uracil, hypoxanthine, uric acid, adenosine, xanthine, inosine, tyrosine, phenylalanine and tryptophan, showed good regression (R2 > 0.9997) within test ranges and the recoveries of the method for all analytes were in the range from 96.74 to 104.24%. The limits of detections and quantifications for nine constituents in DAD were less than 0.22 and 0.43 μg•mL-1, respectively. Conclusion: The qualitative analysis of chemical fingerprints and the quantitative analysis of multiple indicators provide a powerful and rational way to control the KFX quality for pharmaceutical companies.

Quantitative Methods for Measuring Neighborhood Characteristics in Neighborhood Health Research

2018 ◽  
Author(s):  
Dustin T. Duncan ◽  
William C. Goedel ◽  
Rumi Chunara
Keyword(s):  
Quantitative Methods ◽  
Assessment Method ◽  
Self Report ◽  
Neighborhood Characteristics ◽  
Web Based ◽  
Neighborhood Characteristic ◽  
Social Observation ◽  
Gis Methods ◽  
Information Retrieval Methods ◽  
Online Sources

Research connecting neighborhoods and health has characterized neighborhood factors in multiple ways. This chapter discusses standard and emerging methods to measure and study neighborhood characteristics. In particular, this chapter provides an overview of neighborhood characteristic assessment methods, including self-report, systematic social observation, geographic information system (GIS) methods, Web-based geospatial methods, real-time geospatial methods, crowd-sourced geospatial methods, and information retrieval methods from online sources such as Instagram and Twitter. This chapter also discusses the strengths and limitations of each neighborhood characteristic assessment method (e.g., ease of administration, validity), and readers are provided with examples of each neighborhood assessment method applied in the epidemiology and population health literature.

scholarly journals A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

2021 ◽  
Vol 13 (10) ◽  
pp. 5608
Author(s):  
Manjiang Shi ◽  
Qi Cao ◽  
Baisong Ran ◽  
Lanyan Wei
Keyword(s):  
Disaster Recovery ◽  
Social Process ◽  
Principal Component ◽  
Assessment Method ◽  
International Consensus ◽  
Long Term Stability ◽  
The People ◽  
Policy Guidance ◽  
The Impact ◽  
Post Disaster

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

Correlating Variations in the Dynamic Resistance Signature to Weld Strength in Resistance Spot Welding Using Principal Component Analysis

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
David W. Adams ◽  
Cameron D. E. Summerville ◽  
Brendan M. Voss ◽  
Jack Jeswiet ◽  
Matthew C. Doolan
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Spot Welding ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Weld Strength ◽  
Dynamic Resistance ◽  
Resistance Spot ◽  
Resistance Spot Welds ◽  
Key Features

Traditional quality control of resistance spot welds by analysis of the dynamic resistance signature (DRS) relies on manual feature selection to reduce the dimensionality prior to analysis. Manually selected features of the DRS may contain information that is not directly correlated to strength, reducing the accuracy of any classification performed. In this paper, correlations between the DRS and weld strength are automatically detected by calculating correlation coefficients between weld strength and principal components of the DRS. The key features of the DRS that correlate to weld strength are identified in a systematic manner. Systematically identifying relevant features of the DRS is useful as the correlations between weld strength and DRS may vary with process parameters.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Solder Bumps Inspection UsingPrincipal Component Analysis in Active Thermography

2014 ◽  
Vol 912-914 ◽  
pp. 1529-1533
Author(s):  
Xiang Ning Lu ◽  
Ji Guang Han ◽  
Su Hua Han ◽  
Su Ya Wang ◽  
Ming Hui Shao
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Source Distribution ◽  
Inspection System ◽  
Thermal Contrast ◽  
Ir Camera ◽  
Active Thermography ◽  
Solder Bumps ◽  
Pixel Value

A nondestructive inspection system has been developed using the active thermography technology. However the solder defects can not be distinguished directly from the thermal images captured by IR camera because of the heating non-uniformities. The principal component analysis is explored to identify the defects of the solder bumps accurately and effectively. Thermal contrast image is obtained by subtracting source distribution map from the original thermal image. The hot spots corresponding to 16 solder bumps are segmented. The pixels with the thermal contrast values ranging from 0.18°C to 0.20°C are counted, and the maximum and the minimum of the pixel value are found out respectively, which are used in the principal component analysis. The missing bump is identified by the first principal component score. The results show that it is effective using the principal component analysis in active thermography for defects inspection of solder bumps in microelectronic packaging.

scholarly journals A New Semi-Quantitative Process Safety Assessment Method and Its Application for Fluorochemical Industry

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1695
Author(s):  
Guangpei Cong ◽  
Duhui Lu ◽  
Mei Liu ◽  
Qi Wang ◽  
Wei Yu
Keyword(s):  
Chemical Industry ◽  
Quantitative Methods ◽  
Safety Management ◽  
Assessment Method ◽  
Risk Identification ◽  
Main Line ◽  
Process Safety ◽  
Case Comparison ◽  
Improved Model ◽  
Risk Processes

Traditional hazard and operability analysis (HAZOP) is one of the most widely applied methods for process safety management in process enterprises. Due to its principles based on the conservative and qualitative judgment, it often leads to too conservative risk identification results for the fluorine chemical industry usually with high-risk processes to keep the continuity of production. Most of improved quantitative and semi-quantitative methods are based on the layer of protection analysis (LOPA) to resolve the over-conservative problem of traditional HAZOP with the database of LOPA. However, the improved model, taking LOPA as the main line and HAZOP only as the provider of scenarios and influencing factors, is limited to the fact that LOPA can only analyze complete and independent protection layers (IPLs). Therefore, in order to realize the quantitative or semi-quantitative analysis of disaster causes and consequences, a new semi-quantitative HAZOP method takes HAZOP as the main line to integrate LOPA, F&EI (fire and explosion index) for quantitatively calculating the reduction factors, probability on failure demand (PFD) of general protection layers (GPLs) and PFD of IPLs. With the case comparison of fluorine chemical industry, it is proved that this new method can effectively improve the problem that traditional HAZOP are too conservative in complex scenarios.

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