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Estimation of Organ Dose, Effective Dose, and Cancer Risk in Abdominal CT Scan Patients Putri, S. S.; Intifadhah, S. H.; Putri, E. R.
Atom Indonesia Vol 50, No 3 (2024): DECEMBER 2024
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2024.1502

Computed tomography scan (CT scan) is a modality that is used to diagnose diseases inside the human body. In the scanning process, the patient will receive radiation from the CT scanner, so that it is necessary to calculate the amount of radiation dose. The purpose of this study was to determine the organ dose, effective dose, and cancer risk received by abdominal examination patients. Data taken from the results of abdominal examination patients at Radiology Installation of A.W. Sjahranie Regional Hospital Samarinda using 16-slice CT scan modality GE BRIVO type D3161T. The data collected included 150 patients, both female and male, with ages ranging from 15 to 79 years. Dosimetry parameters taken from CT scan results are the exposure factor (kV, mAs), scan length, computed tomography dosimetry indeks volume (CTDIvol), and dose length product (DLP) of the patient. CTDIvol and DLP of the patient are used to calculate the organ dose, effective dose, and cancer risk values of abdominal CT scan patients. Then the effective dose value received by the abdominal CT scan examination patient is compared with the Nuclear Energy Regulatory Agency of Indonesia (BAPETEN) standard based on the CTDIvol and DLP values of the patient, and also compared with the International Commission Radiological Protection (ICRP) standard. Based on the results of organ dose estimation calculations, the average value of the stomach is 0.82 mSv, the gonads are 0.54 mSv, and the bladder is 0.28 mSv. Meanwhile, the average value of effective dose received by abdominal examination patients is 5.28 mSv with an average cancer risk of 0.029 %. Based on the CTDIvol and DLP values of the patients, the 3rd quartile values of the patients were 8.25 mGy and 413.84 mGy.cm. This value is still below the value recommended by BAPETEN when viewed from the 2021 Diagnostic Reference Level (DRL) guidelines. The effective dose received by one patient exceeded the standard set by the ICRP. Meanwhile, the cancer risk received by patients is still in a low percentage.

Radiation Dose and Image Quality of Bladder Cancer Patients Analysis on Abdominal CT-Scan Examinations Anthon, R.; Intifadhah, S. H.; Putri, E. R.
Atom Indonesia Vol 51, No 1 (2025): APRIL 2025
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2025.1526

The bladder is a subperitoneal, hollow muscular organ that acts as areservoir for urine and located in the lower abdomen. Bladder cancer is one of health issues that can affect many people each year. Bladder cancer ranks as the 10th most common cancer worldwide. Early management includes cancer screening using abdominal CT-Scan. The objective of this study was to analyze the radiation dose received by patients and the image quality of patients underwent abdominal CT scans based on the Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) values obtained. Data analysis management, specifically using quantitative analysis techniques, involved observing 20 bladder cancer patients with a total of 2,653 images. The IndoseCT software was used for analyzing the radiation dose to patients, while the IndoQCT software was used for analyzing image quality in CT-Abdomen examinations based on Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) values. The results showed that the radiation dose received by patients during CT-Abdomen examinations was higher than the dose output by the device. The maximum dose output by the device (CTDIvol) was 50.10 mGy, and the minimum was 6.70 mGy, while the maximum dose received by patients (SSDE) was 53.34 mGy, and the minimum was 9.34 mGy. The image quality results for CT-Abdomen examinations based on SNR and CNR values indicated that the image quality obtained was adequate for diagnostic purposes.

Brain Tumor Segmentation on MR and CT Images Using Fuzzy C-Means and Active Contour Methods Hamid, M.; Mu'ti, A.; Intifadhah, S. H.; Putri, E. R.
Atom Indonesia Vol 51, No 1 (2025): APRIL 2025
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2025.1466

A brain tumor is a dangerous brain disease that can attack anyone. It can be described as the abnormal growth of cells in or around the brain, leading to impaired brain function. The first step in diagnosing a brain tumor is to perform an MRI (Magnetic Resonance Imaging) scan. The research aims to analyze the segmentation results of brain tumor MRI and CT (Computed Tomography) images using the Fuzzy C-Means and Active Contour methods. The evaluation is based on ROC parameters, including accuracy, dice score, precision, and sensitivity. The methodology involves analyzing data from secondary image sources, using MATLAB for the segmentation process, and evaluating the results of image segmentation by radiologists. Four ROC measurements were used for each method. The segmentation evaluation results for MRI images show that the Fuzzy C-Means method achieved a precision of 0.92; sensitivity of 0.64; dice score of 0.76; and accuracy of 0.61. The Active Contour method, on the other hand, obtained a precision of 0.97; a sensitivity of 0.99; a dice score of 0.98; and an accuracy of 0.96. For CT images, the Fuzzy C-Means method yielded a precision of 0.72; sensitivity of 0.98; dice score of 0.83; and accuracy of 0.71. The Active Contour method obtained a precision of 0.96; a sensitivity of 0.95; a dice score of 0.96; and an accuracy of 0.92. These results indicate that the Active Contour method, especially with MRI images, provides better segmentation performance. In conclusion, the segmentation results from the Active Contour method can be used as additional information for doctors in diagnosing the presence of tumors.

Brain Tumor Segmentation in MR Images Using Swin Transformer Nur, A.; Nurhanafi, K.; Putri, E. R.
Atom Indonesia Vol 51, No 2 (2025): AUGUST 2025
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/aij.2025.1580

Brain tumors are abnormal tissue growths in the brain. These brain tumors can have a negative impact on human health, one of which can interfere with brain functions such as vision, balance, and so on. Therefore, early detection needs to be done, one of which is by using medical imaging modalities, i.e., MRI. However, analyzing MRI scans requires careful observation and a high level of proficiency. Thus, medical image segmentation is required. Segmentation is important in medical image analysis as it allows medical experts to distinguish between abnormal and normal tissues. This study aims to determine the ability of the swin transformer architecture in segmenting brain tumor MR images. The image data used was BraTS 2021 data with a total of 1,250 images. The data were divided into three, i.e., training set, validation set, and testing set with a ratio of 70:15:15. Swin Transformer provided two main concepts, i.e., hierarchical feature maps and attention window shifts. The Swin Transformer initially was divided the image into small patches, which were then converted into vector form. After that, it was passed through W-MSA for local area and SW-MSA for cross window area. Next, multiple patches were merged into one, so that the image resolution gradually decreased, and then restored back to the original resolution. Based on this, the segmentation results were evaluated using a confusion matrix using DSC, IoU, and sensitivity metrics. The results of brain tumors MR image segmentation with Swin Transformer obtained evaluation values, i.e., 0.97313 for DSC, 0.94767 for IoU, and 0.96450 for sensitivity. It can be concluded that the Swin Tranformer can effectively segment brain tumor MR images.

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