22. Impact of iterative reconstructions on image quality in clinical CT images demonstrated by a novel noise power spectrum measurement tool

2017 ◽  
Vol 44 ◽  
pp. 11
Author(s):  
H. Pasquier ◽  
F. Gardavaud ◽  
A. Rahmouni ◽  
A. Luciani
Keyword(s):  
Image Quality ◽  
Power Spectrum ◽  
Ct Images ◽  
Measurement Tool ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Spectrum Measurement

Efektifitas Penggunaan Sistem AEC (Smart mA) untuk Optimisasi Dosis dan Kualitas Citra pada Protokol CT Abdomen

2021 ◽  
Author(s):  
Habib Syeh Alzufri ◽  
◽  
Dede Nurmiati
Keyword(s):  
Image Quality ◽  
Power Spectrum ◽  
Good Condition ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Ct Number ◽  
Phantom Image ◽  
Water Phantom ◽  
Body Tissues ◽  
Power Spectrum Method

This study aims to analyze Automatic Exposure Control (AEC) with Smart mA software on water phantom image quality and CTDIvol dose. The researched image quality is CT Number and noise. In addition, the CT Number is evaluated for accuracy, uniformity, and noise using the Noise Power Spectrum method. The results of image measurements with and without Smart mA on CT Number accuracy are still in the Standard range of ± 4 CT, the uniformity value of CT Number and noise is also still within the Standard, namely ± 2 CT. The use of Smart mA increases the noise value by 14.29%. The noise value from the noise power spectrum analysis when using Smart mA is higher than without using Smart mA. Meanwhile, the CTDIvol radiation dose from using Smart mA decreases by 52.33%. Image quality using Smart mA has a CT Number value almost the same or uniform with the test object, namely water phantom, so that the use of Smart mA can characterize body tissues well, but the noise value generated is more significant than without using Smart mA. Although the noise value generated by Smart mA is more excellent, visually, the noise value does not disturb the radiologist too much in determining the diagnosis because the image quality is still in good condition so that it can give a dose according to the patient's body thickness according to the ALARA principle. Keywords: CT Number, CTDIvol, AEC, NPS.

Reply to 'Comments on the noise power spectrum of CT images'

1987 ◽  
Vol 32 (8) ◽  
pp. 1049-1050
Author(s):  
M F Kijewski ◽  
P F Judi
Keyword(s):  
Power Spectrum ◽  
Ct Images ◽  
Noise Power ◽  
Noise Power Spectrum

Comments on 'The noise power spectrum of CT images'

1987 ◽  
Vol 32 (8) ◽  
pp. 1047-1048 ◽  
Author(s):  
K Faulkner ◽  
B M Moores
Keyword(s):  
Power Spectrum ◽  
Ct Images ◽  
Noise Power ◽  
Noise Power Spectrum

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs

2016 ◽  
Vol 43 (9) ◽  
pp. 4996-5008
Author(s):  
Soyoung Lee ◽  
Guanghua Yan ◽  
Philip Bassett ◽  
Arun Gopal ◽  
Sanjiv Samant
Keyword(s):  
Quality Assurance ◽  
Image Quality ◽  
Wavelet Analysis ◽  
Power Spectrum ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Quantitative Image

The noise power spectrum of CT images

1987 ◽  
Vol 32 (5) ◽  
pp. 565-575 ◽  
Author(s):  
M F Kijewski ◽  
P F Judy
Keyword(s):  
Power Spectrum ◽  
Ct Images ◽  
Noise Power ◽  
Noise Power Spectrum
Sign in / Sign up

Export Citation Format

Share Document