We have developed a software to automatically find the contrastdetail (C-D) curve based on the statistical low-contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics. Methods: A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2-pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C-D curve was obtained and the target size at an MDC of 0.6% (i.e., 6-HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer. Results: The developed system was able to measure C-D curves from different phantoms and scanners. We found that the C-D curves follow a power-law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low-contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD. Conclusions: The statistical LCD measurement method can generate a C-D curve automatically, quickly, and objectively.

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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: The current study proposes a method for automatically measuring slice thickness using a non-rotational method on the middle stair object of the AAPM CT performance phantom image. Method: The AAPM CT performance phantom was scanned by a GE Healthcare 128-slice CT scanner with nominal slice thicknesses of 0.625, 1.25, 2.5, 3.75, 5, 7.5 and 10 mm. The automated slice thickness was measured as the full width at half maximum (FWHM) of the profile of the middle stair object using a non-rotational method. The non-rotational method avoided rotating the image of the phantom. Instead, the lines to make the profiles were automatically rotated to confirm the stair’s location and rotation. The results of this non-rotational method were compared with those from a previous rotational method. Results: The slice thicknesses from the non-rotational method were 1.55, 1.86, 3.27, 4.86, 6.58, 7.57, and 9.66 mm for nominal slice thicknesses of 0.625, 1.25, 2.4, 3.75, 5, 7.5, and 10 mm, respectively. By comparison, the slice thicknesses from the rotational method were 1.53, 1.87, 3.32, 4.98, 6.77, 7.75, and 9.80 mm, respectively. The results of the nonrotational method were slightly lower (i.e. 0.25%) than the results of the rotational method for each nominal slice thickness, except for the smallest slice thickness. Conclusions: An alternative algorithm using a non-rotational method to measure the slice thickness of the middle stair object in the AAPM CT performance phantom was successfully implemented. The slice thicknesses from the nonrotational method results were slightly lower than the rotational method results for each nominal slice thickness, except at the smallest nominal slice thickness (0.625 mm).

This study aims to find the optimum threshold for the automatic measurement of slice thickness using ACR CT accreditation phantom. The ACR CT accreditation phantom was scanned using Siemens Somatom Perspective CT scanner. The nominal slice thicknesses of 1.5, 3, 5, 6, 7, and 10 mm were investigated. Our automated method was developed to obtain accurate slice thickness values. Several threshold values from 0.10 to 0.50 with increment of 0.05 to find optimum value were investigated. The results obtained from each threshold were then compared with the nominal slice thickness to determine the optimal threshold value. It is found that the optimum threshold in the automatic measurement of slice thickness with nominal slice thickness values from 1.5 to 10.0 mm is from 0.35 to 0.40. Using this range, the different between the nominal slice thickness and measured slice thickness is within 0.5 mm. The optimal threshold for automatic slice thickness measurement has been determined. The optimal threshold would lead to more accurately automated slice thickness measurement.