The Thai Journal of Radiological Technology

Quality control of abdominal radiography in patients with acute abdominal pain

Chawee Luechabhun — 2024-08-26

Currently, the number of radiographic imaging of patients with acute abdominal pain has increased. Patients seeking treatment for such conditions must receive accurate, prompt, and precise services to enable healthcare providers to diagnose and treat them safely. Controlling the quality of acute abdominal series radiographic images are crucial to ensure high-quality images. Key factors affecting image quality include the patient, radiology staff, and the equipment used for imaging. These main factors encompass several sub-factors that also influence image quality, such as the patient's condition, the expertise of the imaging staff, and the appropriate environment for radiographic imaging. Effective control of all these factors helps improve the quality of radiographic images, ensuring that patients undergoing acute abdomen series radiography receive fast, accurate, and safe services. This, in turn, enables physicians to accurately and swiftly diagnose diseases from radiographic images and treat patients effectively to restore their health.

Radiotherapy in pelvic malignancies at King Chulalongkorn Memorial Hospital, Thai Red Cross Society

Wisan Arphasetthasakul — 2024-08-25

Radiotherapy involves treating malignant cells using beams of intense energy to destroy cancer cells, making precision critical at every step. This process begins with patient preparation, treatment simulation, irradiation, positioning, and the use of appropriate immobilization techniques. However, variations can occur, such as discrepancies between patient positioning during simulation and actual treatment. For patients with pelvic malignancies, maintaining a full bladder and an empty rectum is essential to minimize side effects on surrounding normal organs. Controlling the volume of these organs is challenging, so image verification is necessary before treatment, using methods like EPID (Electronic Portal Imaging Device) or CBCT (Cone Beam Computed Tomography). n brachytherapy, imaging is particularly crucial. At KCMH, both pre- and post-treatment MRI imaging is performed to aid doctors in evaluating and planning treatment. In radiotherapy, acquiring images is a key responsibility of radiologic technologists. Therefore, this article is intended for those interested in enhancing their knowledge of pelvic cancer treatment with radiotherapy at King Chulalongkorn Memorial Hospital, Thai Red Cross Society.

Respiratory motion management techniques for radiotherapy of thoracic and upper abdominal tumors at King Chulalongkorn Memorial Hospital

Metinee Wisetrinthong — 2024-08-25

The radiation therapy precisely targets high doses of radiation and minimizes the risk of radiation exposure to neighboring healthy tissue of the modern radiotherapy machines and treatment planning techniques has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we explain how to manage breathing motions in the field of division of Radiation Oncology, Department of Radiotherapy, King Chulalongkorn Memorial Hospital to reduce the effects of breathing motion, which will allow precise irradiation and can reduce the amount of radiation on Organs at risk and normal tissues, which will result in fewer side effects from radiation

Optimizing the minimum detectable difference of chest protocol digital radiography system by V-shaped line gauge phantom and Taguchi analysis

Samrit Kittipayak — 2024-05-22

Background: The Minimum Detectable Difference (MDD) of Digital Radiography (DR) image systems was quantified and optimized using a self-developed V-shaped line gauge phantom and Taguchi optimization analysis. The increased clinical applications of the DR system are mainly due to its ability to provide instant and precise imaging for radiologists to diagnose. Objective: The purpose of this study was to optimize parameters for MDD values based on Taguchi's analysis and to verify a self-developed V-shaped line gauge phantom for the chest protocol of the DR system. Materials and methods: The chest protocol was exposed using the V-shaped line gauge and solid acrylic water phantom in various sizes with the DR system. Five factors were assigned in this study: (A) focal spot, (B) kVp, (C) mAs, (D) filter, and (E) phantom thickness. Since each factor could have two or three levels, eighteen groups of factor combinations were organized according to Taguchi’s algorithm. The V-shaped line gauge images were estimated. ANOVA was adopted to determine the significant factors and MDD values to evaluate the spatial resolution. Results: The optimal parameters for the highest Signal-to-Noise (S/N) ratio were as follows: (A) large focal spot, (B) 140 kVp, (C) 6.25 mAs, (D) 0.1 mmCu filter, and (E) phantom thickness of 16 cm. Conclusion: The optimal parameters in this study provided an MDD value of 0.87 mm compared to 1.66 mm for conventional parameters. The self-developed V-shaped line gauge phantom also proved to be suitable for the DR system. Furthermore, all factors affected the image quality with statistical significance following the ANOVA analysis.

Evaluation of pulmonary tuberculosis diagnosis in chest radiographs using the AXIR-CX artificial intelligence program from confirmation results based on the Xpert MTB/RIF Ultra method in recipients with negative AFB sputum results

Narawin Kulnara — 2024-08-24

Introduction: Tuberculosis is a problem for the public health system both globally and in Thailand. When screening for pulmonary tuberculosis using radiographic images, a shortage of radiologists to interpret the images was found. Therefore, the AXIR-CX artificial intelligence program was introduced for image interpretation. This study aims to evaluate the performance of the AXIR-CX artificial intelligence program in screening for pulmonary tuberculosis using digital chest radiographs, with the Xpert MTB/RIF Ultra method as the reference standard. Methods: A retrospective descriptive design was used, with digital chest radiographs randomly selected from samples at six primary hospitals in Chanthaburi province using the AeroDR 2 1417S DR detector. Data were collected from October 1, 2019, to September 30, 2023. The data were analyzed using diagnostic test statistics, ROC curve analysis, and Kappa statistics for agreement measurement. Results: The AXIR-CX artificial intelligence program analyzed 459 chest X-ray images and found that the sensitivity and specificity were 80.6% (95% CI: 69.5-88.9) and 59.2% (95% CI: 54.1-64.1), respectively. The accuracy was 62.5% (95% CI: 58.1-66.9). The positive likelihood ratio was 1.97 (95%CI: 1.67 to 2.33), and the negative likelihood ratio was 0.33 (95%CI: 0.20 to 0.53). The ROC curve analysis yielded a cutoff point of 0.5, with the area under the ROC curve being 69.9% (95% CI: 64.6-75.1). When setting the significance level at 0.05, there was no statistically significant difference in the area under the ROC curve between males and females (p-value = 0.12), and there was no statistically significant difference in the area under the ROC curve between the age groups of 15-54 years and those aged 55 years and older (p-value = 0.29). The kappa coefficient between the AXIR-CX artificial intelligence program and the Xpert MTB/RIF Ultra method was 0.22 (95% CI: 0.15-0.29). Conclusion: The AXIR-CX artificial intelligence program can be used with caution for screening pulmonary tuberculosis in primary hospitals, as the decision criteria for estimating the area under the ROC curve is relatively low, and the level of agreement strength is fair.

Setup errors and CTV to PTV margin for upper abdominal cancer with intensity modulated radiotherapy technique using electronic portal imaging device in Sakon Nakhon Hospital

Wimonmart Tongngarm — 2024-05-22

Introduction: The systematic and random setup errors for patients with upper abdominal cancer are specific to each hospital. Objective: This study aimed to determine the systematic error, random error, and CTV to PTV margin for patient positioning in upper abdominal cancer with intensity-modulated radiotherapy (IMRT) using the electronic portal imaging device (EPID). Materials and Methods: Patient information and setup errors of 32 patients undergoing radiotherapy were collected. The data on setup errors in three directions (longitudinal, lateral, and vertical) were retrospectively collected from the Mosiaq Platform. Subsequently, individual and population systematic and random errors were calculated. The CTV to PTV margins were then determined using the Van Herk equation. Finally, correlations between setup error and BMI, age, and PTV volume were analyzed. Results: The results showed that the setup errors in the longitudinal, lateral, and vertical directions were 2.59±1.44, 2.17±0.95, and 1.66±0.59 mm, respectively. The population systematic errors in the longitudinal, lateral, and vertical directions were 1.44, 0.95, and 0.59 mm, respectively. The population random errors in the longitudinal, lateral, and vertical directions were 2.59, 2.17, and 1.66 mm, respectively. The determined CTV to PTV margins in the longitudinal, lateral, and vertical directions were 5.42, 3.89, and 2.62 mm, respectively. There was no correlation between setup error and BMI, age, or PTV volume, with p-values > 0.05. Conclusion: The CTV to PTV margins were less than 5 mm in all directions except the longitudinal direction. This finding may be useful for reviewing the PTV margin for Sakon Nakhon hospital.

Repeat film analysis and its implications for quality assurance in diagnostic radiology: An institutional case study

Bunchai Nittayasupaporn — 2024-05-25

The primary objective of this research was to analyze the rate of repeated chest examinations using the Reject and Usage Analysis tool program of the Samsung XGEO-GC80 X-ray machine. A retrospective study was conducted at King Chulalongkorn Memorial Hospital (KCMH) in the Department of Radiological Diagnostic. The study involved collecting statistics and closely examining the causes behind rejected images within the machine system. Among the key findings were Chest examinations accounted for the highest number of images, comprising 39.32% of the total 40,400 images. Repeated images were most prevalent in chest examinations, making up 32.21%of the 7,550 rejected images and 6.02% of all images. The primary reason for rejecting images was improper positioning, constituting 75.47% of all rejected images. In chest examinations, the most common reason for image rejection was improper breathing, contributing to 54.3% of all 1,321rejected images and 14.0% of all rejected images in the study. Furthermore, the study highlighted that a significant portion of X-ray image rejections resulted from human errors, primarily from radiological technologists. To reduce the rate of repeated imaging, the following recommendations were proposed for i) comprehensive evaluation of the patient's condition before the X-ray examination, ii) Ensuring proper patient positioning, iii) Implementation of various initiatives within the healthcare unit, including regular training for radiologists, and iv) Establishment of criteria for accepting radiographic images across all radiological technologists within the diagnostic department. In conclusion, these analyses are essential to enhance the quality of radiological services at KCMH, minimize image rejections, and ultimately improve patient care and radiation safety.

Development of a web application for stroke diagnosis assistance using deep learning artificial intelligence on computed tomography image

Titipong Kaewlek — 2024-05-26

Stroke presents a significant health risk for the elderly, demanding precise diagnosis through computed tomography (CT) imaging. Expert interpretation is crucial, and present artificial intelligence (AI) proves invaluable for radiologists, ensuring accuracy. Various accessible programs, but requiring payment, can be installed on devices. This study aims to develop a web application for stroke detection in brain CT images. The web application, compatible with computers, phones, and tablets, employs deep learning AI for stroke classification. Using a dataset of 1,636 images (1,111 normal, 525 stroke), about 70% (1,175 images) were used to train, 20% (329 images) for validation, and 10% (132 images) for test the AI model (Deep learning: VGG-16). Evaluation metrics, including accuracy, sensitivity, specificity, F1-score, and are under curve (AUC), gauged the web app's performance. Design and functionality were assessed through a 5-point Likert scale by three radiologists. Results show impressive accuracy, sensitivity, specificity, F1-score, and AUC (0.969, 0.952, 0.978, 0.952, 0.965). Design and performance scores were 4.13 ± 0.38 and 4.37 ± 0.33. In conclusion, the web application effectively diagnoses strokes in brain CT images, offering a user-friendly experience on the internet.