• Journal of radiological science and technology
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• v.42 no.1
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• pp.31-37
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• 2019

Investigate the adequacy awareness of accuracy control of CT apparatus Questionnaire survey and statistical analysis in the analysis according to age, there is a difference between familiarity with accuracy management items (F = 14.187, p<0.001) and necessity of accuracy control (F=8.109, p<0.001), depending on academic background and work history, There is a difference only in familiarity (F=5.103, p<0.05, F=13.394, p<0.001), and according to the scale of the medical institution analysis shows that if you are more interested than senior general hospital grade hospital grade or less It was analyzed. In order to advance the accuracy control level, we have introduced our comprehensive and efficient comprehensive and efficient integrated medical image quality management operation system of the whole medical image equipment including CT device, It is thought that it is necessary to develop human resources capable of doing.

• Journal of the Korea Convergence Society
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• v.7 no.1
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• pp.89-95
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• 2016

The purpose of this experiment intend to evaluate the quality of the image based on the orbit and basal ganglia with high radiosensitivity for the noise, SNR and dose using the five kinds patient fixing pad in brain phantom MDCT(BrillianceTM CT 64 slice, PHILIPS, Netherward). The noise had a higher values in AP than those of others, but the SNR was lower in AP than those of others. The SNR was higher in UP than those of RP, PP, SP and AP. The UP, RP and PP were no statistically significant(p>0.05), whereas it was significant difference between UP, RP, PP and SP, AP(p<0.05). This is causes of the noise difference is generated due to the differences in the radiation absorption dose in accordance with each the component of the absorbed dose level of the detector according to the reference line and each of SOML when the radiation exposured. The CTDIvol(mGy) and DLP of orbit and basal ganglia were 56.95, 911.50, respectively. There is no difference between both mean dose. In conclusion, it is possible to distinguish among a kind of 5 patient fixing pad by using brain phantom MDCT. Overall, patient fixing pad of UP, RP and PP based on a brain phantom MDCT can provide useful information.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.1
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• pp.21-30
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• 2007

Our objective was to evaluate the CT attenuation coefficient and noise of spatial domain filtering as an alternative to additional image reconstruction using different kernels in abdominal CT. Derived from thin collimated source images was generated using abdomen B10 (very smooth), B20 (smooth), B30 (medium smooth), B40 (medium), B50 (medium sharp), B60 (sharp), B70 (very sharp) and B80 (ultra sharp) kernels. Quantitative CT coefficient and noise measurements provided comparable HU (hounsfield) units in this respect. CT attenuation coefficient (mean HU) values in the abdominal were 60.4$\sim$62.2 HU and noise (7.6$\sim$63.8 HU) in the liver parenchyma. In the stomach a mean (CT attenuation coefficient) of -2.2$\sim$0.8 HU and noise (10.1$\sim$82.4 HU) was measured. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. CT images increase the diagnostic accuracy may be controlled by adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undergoing the examination.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.71-81
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• 2008

Our objective was to evaluate the image of spatial domain filtering as an alternative to additional image reconstruction using different kernels in MDCT. Derived from thin collimated source images were generated using water phantom and abdomen B10(very smooth), B20(smooth), B30(medium smooth), B40 (medium), B50(medium sharp), B60(sharp), B70(very sharp) and B80(ultra sharp) kernels. MTF and spatial resolution measured with various convolution kernels. Quantitative CT attenuation coefficient and noise measurements provided comparable HU(Hounsfield) units in this respect. CT attenuation coefficient(mean HU) values in the water were values in the water were $1.1{\sim}1.8\;HU$, air($-998{\sim}-1000\;HU$) and noise in the water($5.4{\sim}44.8\;HU$), air($3.6{\sim}31.4\;HU$). In the abdominal fat a CT attenuation coefficient($-2.2{\sim}0.8\;HU$) and noise($10.1{\sim}82.4\;HU$) was measured. In the abdominal was CT attenuation coefficient($53.3{\sim}54.3\;HU$) and noise($10.4{\sim}70.7\;HU$) in the muscle and in the liver parenchyma of CT attenuation coefficient($60.4{\sim}62.2\;HU$) and noise ($7.6{\sim}63.8\;HU$) in the liver parenchyma. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image scanned with a high convolution kernel(B80) led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. Adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undergoing the examination, may control CT images increase the diagnostic accuracy.