Signal/Noise and Sensitometry Limitations in Chest Radiography: Implications of Regional Exposure Control

1985 ◽  
Author(s):  
D B. Plewes ◽  
C. G. Shaw ◽  
M. Ivanovich
Keyword(s):  
Chest Radiography ◽  
Exposure Control ◽  
Signal Noise

Automatic Exposure Control in Chest Radiography

2019 ◽  
Vol 19 (1) ◽  
pp. 713
Author(s):  
Sung-Sik Choi ◽  
Cheong-Hwan Lim ◽  
Sung-Hun Jeoung
Keyword(s):  
Chest Radiography ◽  
Exposure Control ◽  
Automatic Exposure Control

A Study on the Using of Automatic Exposure Control in the Chest Radiography

2019 ◽  
Vol 42 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Sung-Sik Choi ◽  
◽  
Cheong-Hwan Lim ◽  
Sung-Hoon Jung
Keyword(s):  
Chest Radiography ◽  
Exposure Control ◽  
Automatic Exposure Control

Clinical evaluation of manual and automatic exposure control techniques in film-based chest radiography

1996 ◽  
Vol 69 (823) ◽  
pp. 650-654 ◽  
Author(s):  
E Ismailos ◽  
I Mastorakou ◽  
N L Kelekis ◽  
K P Papadopoulos ◽  
E P Efstathopoulos ◽  
...  
Keyword(s):  
Clinical Evaluation ◽  
Chest Radiography ◽  
Exposure Control ◽  
Automatic Exposure Control ◽  
Control Techniques

scholarly journals Influence of X-ray Detector of Automatic Exposure Control Unit(AEC)on Digital Chest Radiography

2001 ◽  
Vol 57 (9) ◽  
pp. 1121-1127
Author(s):  
HIDEMICHI KAWATA ◽  
SEIJI OOKUBO ◽  
KEIICHIROU HYOUDOU ◽  
RYUUJI IKEMATSU ◽  
HITOSHI TANIGAWA ◽  
...  
Keyword(s):  
Chest Radiography ◽  
Control Unit ◽  
Exposure Control ◽  
Automatic Exposure Control ◽  
X Ray ◽  
Digital Chest

Signal/Noise Ratio and Elemental Detectivity by Eels

1982 ◽  
Vol 40 ◽  
pp. 488-489
Author(s):  
R. F. Egerton
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Elemental Analysis ◽  
Electron Energy Loss Spectroscopy ◽  
Emission Spectroscopy ◽  
X Ray ◽  
Signal Noise ◽  
Characteristic Signal ◽  
Noise Ratio ◽  
Electron Energy Loss

An important parameter governing the sensitivity and accuracy of elemental analysis by electron energy-loss spectroscopy (EELS) or by X-ray emission spectroscopy is the signal/noise ratio of the characteristic signal.

Aortic arch calcification is strongly associated with coronary artery calcification

VASA ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 106-114 ◽  
Author(s):  
Adem Adar ◽  
Hakan Erkan ◽  
Tayyar Gokdeniz ◽  
Aysegul Karadeniz ◽  
Ismail G. Cavusoglu ◽  
...  
Keyword(s):  
Coronary Artery ◽  
High Risk ◽  
Aortic Arch ◽  
Chest Radiography ◽  
Coronary Artery Calcification ◽  
Independent Predictor ◽  
Study Group ◽  
Discriminative Performance ◽  
Sensitivity Specificity ◽  
Aortic Arch Calcification

Background: We aimed to investigate the association between aortic arch and coronary artery calcification (CAC). We postulated that low‐ and high‐risk CAC scores could be predicted with the evaluation of standard chest radiography for aortic arch calcification (AAC). Patients and methods: Consecutive patients who were referred for a multidetector computerized tomography (MDCT) examination were enrolled prospectively. All patients were scanned using a commercially available 64‐slice MDCT scanner for the evaluation of CAC score. A four‐point grading scale (0, 1, 2 and 3) was used to evaluate AAC on the standard posterior‐anterior chest radiography images. Results: The study group consisted of 248 patients. Median age of the study group was 52 (IQR: 10) years, and 165 (67 %) were male. AAC grades (r = 0.676, p < 0.0001) and age (r = 0.518, p < 0.0001) were significantly and positively correlated with CAC score. Presence of AAC was independently associated with the presence of CAC (OR: 11.20, 95 % CI 4.25 to 29.52). An AAC grade of ≥ 2 was the strongest independent predictor of a high‐risk CAC score (OR: 27.42, 95 % CI 6.09 to 123.52). Receiver operating characteristics curve analysis yielded a strong predictive ability of AAC grades for a CAC score of ≥ 100 (AUC = 0.892, P < 0.0001), and ≥ 400 (AUC = 0.894, P < 0.0001). Absence of AAC had a sensitivity, specificity and accuracy of 90 %, 84 % and 89 %, respectively, for a CAC score of < 100. An AAC grade of ≥ 2 predicted a CAC score of ≥400 with a sensitivity, specificity and accuracy of 68 %, 98 % and 95 %, respectively. Conclusions: AAC is a strong and independent predictor of CAC. The discriminative performance of AAC is high in detecting patients with low‐ and high‐risk CAC scores.

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