We developed software to automatically measure the CT number linearity using an ACR accreditation phantom image and investigated the CT number linearity of 16 different CT scanners. The software implemented a segmentation-rotation method. After segmenting five objects within the phantom image, the mean CT number of each object was measured and a graph between CT numbers and their densities plotted. Linear regression and its R2 were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed in 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R2 > 0.99) between the CT numbers and their densities. Two scanners (Siemens Emotion 16) had the strongest linear correlation with R2 = 0.999, and two Hitachi Eclos scanners had the weakest linear correlation with R2 < 0.99.

Purpose: This study aims to develop a software tool for investigating patient centering profiles of axial CT images and to implement it to evaluate practices in three hospitals in Indonesia. Methods: The evaluation of patient centering accuracy was conducted by comparing the center coordinate of the patient’s image to the center coordinates of the axial CT image. This process was iterated for all slices to yield an average patient mis-centering in both the x- and y-axis. We implemented the software to evaluate the profile of centering on 268 patient images from the head, thorax, and abdomen examinations taken from three hospitals. Results: We found that 82% of patients were mis-centered in the y-axis (i.e., placed more than 5 mm from the iso-center), with 49% of patients placed 10–35 mm from the iso-center. Most of the patients had a tendency to be placed below the iso-centers. In head examinations, patients were more precisely positioned than in the other examinations. We did not find any significant difference in mis-centering between males and females. We found that there was a slight difference between mis-centering in adult and pediatric patients. Conclusion: Software for automated patient centering was successfully developed. Patients in three hospitals in Indonesia had a tendency to be placed under the iso-center of the gantry.

A method to calculate a corrected effective diameter (DMIL) to more accurately estimate the dose received by a patient in chest computed tomography (CT) examination had been previously proposed. However, the discrepancy between DMIL and water-equivalent diameter (Dw) is still relatively high (i.e. about 6%). Furthermore, the method is still performed manually, so it is laborious and time-consuming. This study aims to improve the corrected effective diameter with bone correction (Deffcorr) and to automatically calculate it. The automated Deffcorr was calculated as the square root of the product of these corrected AP and LAT diameters. The approach was implemented on 30 patients who had undergone chest CT examination with the standard protocol. The results show that the correlation between the Deffcorr and Dw is R2=0.93 with no statistical difference (p&gt;0.05). The automated Deffcorr is 3.1% lower than Dw. While the DMIL is 10.5% higher than Dw and both are statistically different (p&lt;0.05). In conclusion, the new Deffcorr was introduced and the result obtained was closer to Dw than DMIL. This method is simple enough to be used as an alternative method to accurately estimate Dw for radiation dose estimation in clinical chest CT scanning.