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MDCT에서의 Convolution Kernel 종류에 따른 공간 영역 필터링의 영상 평가

Evaluation to Obtain the Image According to the Spatial Domain Filtering of Various Convolution Kernels in the Multi-Detector Row Computed Tomography

권대철|유병규|이후민
대한방사선과학회 (Korean Society of Radiological Science)|2008.03|Journal of Radiological Science and Technology|제31권 제1호|pp. 71-82

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참고문헌

키워드

KernelNoise변조전달함수전산화단층촬영Computed Tomography노이즈감약계수MTF커널CT Attenuation Coefficient
주제분류방사선과학

초록

CT 영상은 커널의 종류와 재구성 방법에 따라 다양하게 나타나며, 관심 영역의 CT감약계수 및 노이즈는 정밀도에 영향을 준다. 커널의 종류에 따른 노이즈, 공간분해능 및 MTF를 측정하여 영상을 평가하였다. 다중채널CT 스캐너를 이용하여 팬텀 및 복부를 스캔 하였고, 커널은 B10(very smooth), B20(smooth), B30 (medium smooth), B40(medium), B50(medium sharp), B60(sharp), B70(very sharp), B80(ultra sharp)으로 재구성하여 물, 공기, 간의 실질 조직, 근육, 지방 부위를 ROI 기능을 이용하여 평균의 CT감약계수와 표준편차인 노이즈를 정량적으로 측정하여 영상을 비교하였다. 그 결과CT 감약계수는 물(1.1~1.8HU), 공기(-998~-1,000HU)이고, 물에서의 노이즈(5.4~44.8HU), 공기(3.6~31.4HU)이다. 인체에서 간 실질 조직과 지방, 근육의 CT 감약계수와 노이즈를 커널에 따라 측정하였다. 지방의 CT 감약계수(-2.2~0.8HU), 간의 실질 조직에서 CT감약계수(60.4~62.2HU), 노이즈(7.6~63.8HU), 근육의 CT감약계수(53.3~54.3HU), 노이즈(10.4~70.7HU) 사이에서 분포하였고, 커널이 높아질수록 노이즈도 증가하였다. 영상의 질을 높이기 위해서는 검사부위에 따라 노이즈를 감소하기 위해 적절한 커널을 선택하여 CT 검사를 하여야 한다.

Abstract

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 coefficientand noise measurements provided comparable HU(Hounsfield) units in this respect. CT attenuation coefficient(mean HU) values in the water were 1.1~1.8HU, air(-998~-1000HU) and noise in the water(5.4~44.8HU), air(3.6~31.4HU). In the abdominal fat a CT attenuation coefficient(-2.2~0.8HU) and noise(10.1~82.4HU) was measured. In the abdominal was CT attenuation coefficient(53.3~54.3HU) and noise(10.4~70.7HU) in the muscle and in the liver parenchyma of CT attenuation coefficient(60.4~62.2HU) and noise (7.6~63.8HU) in the liver parenchyma. Image reconstructed witha 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.

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