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Automatitation method in defining the quality of CT image is needed to optimize CT Scan treatment planning. So, the optimization of treatment planning can also be done automatically. There are various methods proposed to define the quality of an image. The purpose of this study was to find the simple and precision method to define CT image. We compared the performance of Automated Noise Measurement (ANM) and Automated Universal Image Quality Index (UIQI). We also compared them with the Manual noise measurement method based on the level of convergence in homogeneous images. The first step of Automated Noise Measurement was to create binary density slice using threshold values. Then, a masked image was performed by masking the original image and binary image. The standard deviation of every pixel for a certain kernel size was calculated by using a sliding window operation. The fourth step was to make a noise histogram from the noise map and determine the final noise in the image as the histogram peak. Then this calculation was normalized by the peak of the Hounsfield Unit (HU) histogram. All these steps were done with various kernel sizes for different slices in-homogenous phantom. In the Automatic UIQI method, the steps in the ANM method are carried out until the masked image stage, then UIQI is calculated for the masked image. The results show that automatic UIQI was more convergence in defining image quality than manual noise measurement and automated noise measurement by the lowest standard deviation which was only 0.00032867.

Inequality of health care facilities, especially radiology resources, occurs in West Java Province. There are many class A hospitals in provincial capitals, while in areas far from the provincial capital and from DKI Jakarta Province, the quantity and quality of hospitals are still lacking. Likewise with the quantity of radio diagnostic instruments and human resources. 12 radiology specialists and an additional 192 radiographers are needed in West Java Province. Archiving and image communication systems (PACS) can be used as a solution so that health workers in hospitals located far from the city or district centers can consult, and expert conclusions can be obtained from radiology specialists at referral centers. Keywords: PACS, radio diagnostic, radiology specialist, radiographer, West Java

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Purpose This study analysed the sensitivity of the field size from variations in the target volume dimensions, depth, and position. The variations in the target volume analysis were used to determine the width of the field size. Thus, the quality control of the radiation beam can be obtained. Materials and Methods The computed tomography (CT) image of the IBA Dose 1 type of water phantom consists of 350 slices. Variations in the dimension of the target volume were modelled in 10×10×10 cm3, 10×12×10 cm3 , 10.2×10×10.2 cm3, and 15×15×15 cm3. Beam parameters use one beam of irradiation on the central axis 0°, 6 MV energy, 100 cm source-skin distance (SSD), beamlet delta x, and y set to 0.1 cm. Dose distribution in the form of the XZ isodose curve and dose profile was used to observe the field size. Results In this study, the isodose curve was successfully displayed in the XZ isodose curve. The field size’s sensitivity has been successfully reviewed from variations of the target volume, depth, and position. The target X and Z direction analysis is used in determining the width and length of the field size. Conclusion The analysis related to the field size sensitivity study was obtained from a relatively valid calculation. The field size was evaluated with variations in depth of 1.5 cm, 5 cm, 10 cm, and variations in positions of 10 cm, 12 cm, 14 cm, 18 cm, and 20 cm. This study will be used as a reference to validate the distribution of computational environment for radiotherapy research (CERR) dose in the future. Thus, the accuracy of the dose calculation can be obtained.