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

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.

scholarly journals Technical Note: A fully automated purge and trap-GC-MS system for quantification of volatile organic compound (VOC) fluxes between the ocean and atmosphere

2014 ◽  
Vol 11 (6) ◽  
pp. 2979-3002 ◽  
Author(s):  
S. J. Andrews ◽  
S. C. Hackenberg ◽  
L. J. Carpenter
Keyword(s):  
High Efficiency ◽  
High Sensitivity ◽  
Automated Analysis ◽  
Technical Note ◽  
Purge And Trap ◽  
Surface Ocean ◽  
New Information ◽  
Essential Components ◽  
Sensitivity And Selectivity ◽  
The Tropics

Abstract. The oceans are a key source of a number of atmospherically important volatile gases. The accurate and robust determination of trace gases in seawater is a significant analytical challenge, requiring reproducible and ideally automated sample handling, a high efficiency of seawater–air transfer, removal of water vapour from the sample stream, and high sensitivity and selectivity of the analysis. Here we describe a system that was developed for the fully automated analysis of dissolved very short-lived halogenated species (VSLS) sampled from an under-way seawater supply. The system can also be used for semi-automated batch sampling from Niskin bottles filled during CTD (Conductivity, Temperature, Depth) profiles. The essential components comprise of a bespoke, automated purge and trap (AutoP & T) unit coupled to a commercial thermal desorption and gas chromatograph–mass spectrometer (TD-GC-MS). The AutoP & T system has completed five research cruises, from the tropics to the poles, and collected over 2500 oceanic samples to date. It is able to quantify >25 species over a boiling point range of 34–180 °C with Henry's Law coefficients of 0.018 and greater (CH2I2, kHcc dimensionless gas/aqueous) and has been used to measure organic sulfurs, hydrocarbons, halocarbons and terpenes. In the east tropical Pacific, the high sensitivity and sampling frequency provided new information regarding the distribution of VSLS, including novel measurements of a photolytically driven diurnal cycle of CH2I2 within the surface ocean water.

Design of a graticule indicating central axis position for use with portal imaging

2003 ◽  
Vol 3 (2) ◽  
pp. 105-108
Author(s):  
K. Cooke ◽  
L. Askew ◽  
D. Howson ◽  
S. Collins
Keyword(s):  
Quality Control ◽  
Technical Note ◽  
Central Axis ◽  
Portal Imaging ◽  
Electronic Portal Imaging ◽  
On Line ◽  
Axis Position ◽  
Portal Film

This technical note describes a port film graticule that can be used to determine the central axis of a portal image when used with megavoltage film or on-line electronic portal imaging. The construction and quality control of the portal film graticule are highlighted.

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.

Phantom Measurements for Quality Control in Quantitative Computed Tomography

1993 ◽  
Vol 49 (1-3) ◽  
pp. 237-240 ◽  
Author(s):  
K.A. Jessen ◽  
P. Franklin ◽  
L.C. Jensen ◽  
J.J. Christensen
Keyword(s):  
Computed Tomography ◽  
Quality Control ◽  
Quantitative Computed Tomography ◽  
Phantom Measurements

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.

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