SU-FF-I-73: Automated Analysis of Contrast Detail Phantom Images for Quality Control

2009 ◽  
Vol 36 (6Part4) ◽  
pp. 2451-2451
Author(s):  
R Jingu ◽  
M Ohki ◽  
H Arimura ◽  
J Morishita ◽  
F Toyofuku ◽  
...  
Keyword(s):  
Quality Control ◽  
Automated Analysis

External quality-control survey of cholesterol analyses performed by 12 lipid research clinics.

1978 ◽  
Vol 24 (9) ◽  
pp. 1477-1484 ◽  
Author(s):  
K Lippel ◽  
S Ahmed ◽  
J J Albers ◽  
P Bachorik ◽  
R Muesing ◽  
...  
Keyword(s):  
Quality Control ◽  
Automated Analysis ◽  
Cholesterol Concentration ◽  
Lipid Research ◽  
External Quality Control ◽  
External Quality ◽  
Accuracy And Precision ◽  
Control Survey ◽  
Report Accuracy

Abstract We report accuracy and precision achieved in the automated analysis for cholesterol in a long-term multilaboratory study, presenting and evaluating the significance of data accumulated by 12 Lipid Research Clinics (LRC's) in the analysis of 18 unknown surveillance pools during three years. The average bias for all pools and for 13 autoAnalyzer II (Technicon Instruments Corp., Tarrytown, N.Y. 10591) instruments in the 12 clinics was -0.41% (range -1.2 to +0.3%), as compared to values established by reference methodology. The regression equation relating observed cholesterol values (y) to reference values (x) was: y = 0.35 + 0.977x. The bias varied from pool to pool (-2.3 to +5.3%), positive biases being observed for pools with cholesterol concentrations less than 1.4 g/liter, and negative biases for those pools with higher concentrations. Total standard deviations ranged between 25 and 75 mg/liter, and total CV's for most individual instruments were between 1 and 3%. Of the variability for a particular pool, less than 20% was due to differences among instruments, and within- and between-run variabilities were approximately equal. These trends were the same as those previously observed [Clin. Chem. 23, 1744 (1977)] in the analysis of bench control pools of known cholesterol concentration.

Technical Note: Fully-automated analysis of Jaszczak phantom measurements as part of routine SPECT quality control

2017 ◽  
Vol 44 (5) ◽  
pp. 1638-1645 ◽  
Author(s):  
Albert Hirtl ◽  
Helmar Bergmann ◽  
Barbara Knäusl ◽  
Thomas Beyer ◽  
Michael Figl ◽  
...  
Keyword(s):  
Quality Control ◽  
Automated Analysis ◽  
Technical Note ◽  
Phantom Measurements

scholarly journals Quality control of digital representations of manuscript texts: proposal of a standards-based method

2021 ◽  
Vol 5 (1) ◽  
pp. 27-44
Author(s):  
Jesús Robledano Arillo
Keyword(s):  
Quality Control ◽  
Digital Image ◽  
Control Method ◽  
Automated Analysis ◽  
Quality Model ◽  
Digital Representations ◽  
Quality Control Method ◽  
Physical Attributes ◽  
High Level

Abstract This study aims to propose a quality control method for digitized versions of manuscript documents that will be relevant for paleographical and codicological analysis. The methodology applied consisted of a systematic review of papers related to automated analysis of the physical characteristics of handwritings and document supports in the field of digital paleography, as well as of the numerous standards that have been emerging in the field of image engineering for quality assessment in digital image recordings. We also worked with a sample of 275 digital representations of pages or double pages of manuscript documentation dating to between the 12th and 17th centuries. As a result of this study, we propose a taxonomy of physical attributes of the handwritings and of their documentary supports that must be represented in the digital image with a high level of fidelity and without any distortions that could lead scholars to erroneous interpretations of the physical and formal characteristics of the original documents. On the basis of this taxonomy, we identified a set of typical distortions caused by digitization processes that can affect the recording quality of the physical attributes previously proposed, as well as a set of parameters and metrics for measuring quality that can be used to create a sufficiently exhaustive quality model. We also detected a series of limitations which, if not properly managed, can compromise the effectiveness of these types of controls.

scholarly journals Automated myocardial segmentation in native t1-mapping cardiovascular magnetic resonance images based on machine learning: a validation study in the UK biobank"s covid-19 subset

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
E Rauseo ◽  
L Lockhart ◽  
JM Paiva ◽  
K Fung ◽  
MY Khanji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
T1 Mapping ◽  
Automated Analysis ◽  
Uk Biobank ◽  
Native T1 ◽  
The Mean ◽  
Good Agreement

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Innovate UK Background  Regional assessment of septal native T1 values with cardiovascular magnetic resonance (CMR) is used to characterise diffuse myocardial diseases. Previous studies suggest its potential role in detecting early pathological alterations, which may help identify high-risk subjects at early disease stages. Automated analysis of myocardial native T1 images may enable faster CMR analysis and reduce inter-observer variability of manual analysis. However, the technical performance of such methodologies has not been previously reported. Purpose  We tested, in a subset of UK Biobank participants, the degree of agreement between CMR septal myocardial T1 values obtained from our machine learning (ML) algorithm and septal native T1 values computed from manual segmentations. Methods  We analysed the first 292 participants who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and had CMR imaging (1.5 Tesla, Siemens MAGNETOM Aera). T1 mapping was performed in a single mid-ventricular short axis (SAX) slice using ShMOLLI (WIP780B) sequences. Three experienced CMR readers independently measured native T1 values by manually placing a single region of interest (ROI) covering half of the anteroseptal and half of the inferoseptal wall using cvi42 post-processing software (version 5.11). A mean T1 value for each participant was then calculated. A ML algorithm developed by Circle Cardiovascular Imaging Inc. was then applied to the same images to derive the myocardium T1 values automatically. The algorithm was previously trained to segment myocardium from SAX T1 and non-T1 mapping images on two external CMR datasets. We compared the mean septal ROI T1 values to the mean myocardium T1 values predicted by the ML algorithm. Results  Two studies were excluded after quality control. The ML-derived and the manually calculated mean T1 values were significantly correlated (r = 0.82, p < 0.001). The Bland-Altman analysis between the two methods showed a mean bias of 3.64 ms, with 95% limits of agreement of −38.88 to 53.46 ms, indicating good agreement (figure 1). Conclusions  We demonstrated strong correlation and good agreement between native T1 values obtained from our automated analysis method and manual T1 septal analysis in a subset of UK Biobank participants. This algorithm may represent a valuable tool for clinicians allowing for fast and potentially less operator-dependent myocardial tissue characterisation. However, validation of more extensive datasets and quality control processes are needed.

scholarly journals Automated quality control of ultrasound based on in-air reverberation patterns

Ultrasound ◽  
2017 ◽  
Vol 25 (4) ◽  
pp. 229-238 ◽  
Author(s):  
Pepijn van Horssen ◽  
Arnold Schilham ◽  
Dennis Dickerscheid ◽  
Niels van der Werf ◽  
Han Keijzers ◽  
...  
Keyword(s):  
Quality Control ◽  
Pilot Study ◽  
Image Interpretation ◽  
Ultrasound Image ◽  
Automated Analysis ◽  
Quality Parameters ◽  
Signal Loss ◽  
Image Degradation ◽  
Linear Transducer ◽  
Over Time

Ultrasound image degradation originates primarily from transducer defects and potentially undermines reliable image interpretation. Systematic quantitative quality control is often neglected due to the limited resources available for this task. We propose a quantitative quality control based on in-air reverberation images. These images serve as an initial indication of image degradation. They are easily generated for any (curvi-)linear transducer independent of the level of expertise of the operator. Automated analysis is presented to extract quality parameters based on the in-air reverberation pattern. Static images acquired by the clinical user are transferred to a server where analysis is performed. The results are available to the sonographer prior to clinical use and transducer status can be remotely monitored with trend analysis over time. The method was evaluated for normal functioning and defect transducers. A pilot study was performed over a period of three weeks to assess reproducibility and practical feasibility. All reverberation images were successfully analysed for different transducer types and vendor-specific image presentation. The proposed quality parameters are sensitive to signal loss and allow differentiation of type and severity of image degradation. The pilot study was well received by the sonographers for the simplicity of the method and the measurements were consistent over time. The proposed automated analysis method of ultrasound quality control can monitor (curvi-)linear transducer status in the entire hospital, overcoming previous limitations for periodic quality control. Implementation of the method can reduce the number of defective transducers routinely used in clinical practice.

Health communication ethics and CDC quality-control guidelines for information

2003 ◽  
Vol 118 (3) ◽  
pp. 193-196 ◽  
Author(s):  
Jeffrey W McKenna ◽  
Terry F Pechacek ◽  
Donna F Stroup
Keyword(s):  
Quality Control ◽  
Health Communication ◽  
Communication Ethics
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