ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION FOR OPTIMIZATION IMAGE QUALITY OF CT SCAN ABDOMEN

Abstract

Adaptive Statistical Iterative Reconstruction is software used to reduce noise. In several hospital uses the ASIR application with varying percentages between radiographers. The purpose of this study was to determine differences in noise and anatomical image information on variations in the percentage of ASIR and ASIR values that reveal optimal CT scan anatomic image information. This type of research is experimental, data are taken from 30 samples of reconstructive CT scan of the abdomen by giving four variations of ASIR (0%, 40%, 60%, and 80%). Noise measurement is done by placing the ROI size of 105.61 mm2 at three points, namely superior liver, inferior liver and middle of the aorta on the axial section. while the assessment of anatomical image information by observation of the results of variations in the value of ASIR by two radiologists. Data analysis uses the One way Anova test to determine differences in noise, Friedman test to determine differences in anatomical image information with a confidence level of 95%. The results showed that there were differences in the abdominal CT scan image noise on variations in the percentage of ASIR with p -alues less than 0.001. Noise decreased with increasing percentage ASIR. The highest noise value is 15.34 at ASIR 0% while the lowest noise is 8.57 at ASIR 80%. There are differences in anatomical image information on the variation of ASIR with p-values less than 0.001. The percentage ASIR of 40% is the optimal ASIR value for displaying CT images of abdominal with mean rank of 3.46.