Radiology Research and Practice最新文献

Background: Intravenous (IV) iodine-based contrast agents are administered during computed tomography (CT) examination to enhance the density differences between lesions and surrounding parenchyma, which is important for lesion characterization, and to demonstrate vascular anatomy and vessel patency. Quality of contrast enhancement has a significant influence on diagnostic interpretation and subsequent management. In this study, we assessed the quality of portal venous phase abdominal CT scans performed with a manual injection of a fixed dose of contrast, which is the routine practice at Tikur Anbessa Specialized Hospital (TASH). The effect of age and sex was also assessed.

Method: A hospital-based retrospective review was performed to identify patients who have had a precontrast and postcontrast abdominal CT scan from November 4, 2020, to September 30, 2022. All patients with abdominal CT scans having precontrast and portal venous phase scans were included in the study. All CT scans were reviewed by the principal investigator and the quality of contrast enhancement was assessed.

Results: In this study, there were a total of 379 patients. The mean hepatic attenuations in precontrast and portal venous phase scans were 59.05 ± 6.69 HU and 103.73 ± 12.84 HU. The proportion of scans with less than 50 HU enhancement was 68% (n = 258). There was a significant association between age and sex with contrast enhancement.

Conclusion: The hepatic contrast enhancement pattern of abdominal CT scan at the study institution reveals a concerning degree of image quality. This is evidenced by the high number of suboptimal contrast enhancement indices and the highly variable enhancement patterns across different patients. This can have a negative impact on the diagnostic performance of CT imaging and can adversely affect the management. Furthermore, both sex and age affect the pattern of enhancement.

Background: There is great variability between centers regarding contrast injection protocols. They should only be injected if they can provide useful information for diagnosis with the necessary and sufficient quantity of iodine. We wanted to know through this study if the use of iodinated contrast media is optimised in abdominal CT scans performed for cancer assessment in Lomé.

Materials and methods: It was a cross-sectional, descriptive, and analytical study with a prospective collection over a period of 6 months in three CT units in Lomé. It involved abdominal CT scans performed for oncological evaluation. Data were reported as the mean ± standard deviation. The Pearson correlation coefficient, ANOVA, chi-square, and the Fisher test were used.

Results: A total of 218 examinations were recorded. The female sex represented 56.88% of the patients. The mean age was 50.92 ± 15.78 years. The mean weight was 70.46 ± 15.23 kg. The mean BMI was 24.91 ± 5.32 kg/m². The examinations were performed with a voltage of 120 kV in 195 cases (89.45%). The mean dose of injected iodine was 0.42 ± 0.09 gI/kg with a dose of 0.40 gI/kg at 80 kV and 0.45 gI/kg at 130 kV. The mean injection rate was 2.90 ± 0.34 mL/s. The mean injected volume was 83.19 ± 7.29 mL. The mean duration of the injection was 30.60 ± 7.39 s. The mean iodine delivery rate was 0.98 ± 0.17 gI/s. There was no saline injection in 152 cases (69.72%). Liver contrast enhancement was satisfactory in 94.5% of cases. There was a strong negative linear correlation between the dose of injected iodine and weight.

Conclusions: Optimization guidelines for the use of iodinated contrast media are not always applied. Therefore, monitoring and benchmarking programmes for iodinated contrast injection protocols that involve all radiology personnel should be implemented.

Introduction: Recent advancements in technology have propelled the applications of artificial intelligence (AI) in various sectors, including healthcare. Medical imaging has benefited from AI by reducing radiation risks through algorithms used in examinations, referral protocols, and scan justification. This research work assessed the level of knowledge and awareness of 225 second- to fourth-year medical imaging students from public universities in Ghana about AI and its prospects in medical imaging.

Methods: This was a cross-sectional quantitative study design that used a closed-ended questionnaire with dichotomous questions, designed on Google Forms, and distributed to students through their various class WhatsApp platforms. Responses were entered into an Excel spreadsheet and analyzed with the Statistical Package for the Social Sciences (SPSS) software version 25.0 and Microsoft Excel 2016 version.

Results: The response rate was 80.44% (181/225), out of which 97 (53.6%) were male, 82 (45.3%) were female, and 2 (1.1%) preferred not to disclose their gender. Among these, 133 (73.5%) knew that AI had been incorporated into current imaging modalities, and 143 (79.0%) were aware of AI's emergence in medical imaging. However, only 97 (53.6%) were aware of the gradual emergence of AI in the radiography industry in Ghana. Furthermore, 160 people (88.4%) expressed an interest in learning more about AI and its applications in medical imaging. Less than one-third (32%) knew about the general basic application of AI in patient positioning and protocol selection. And nearly two-thirds (65%) either felt threatened or unsure about their job security due to the incorporation of AI technology in medical imaging equipment. Less than half (38% and 43%) of the participants acknowledged that current clinical internships helped them appreciate the role of AI in medical imaging or increase their level of knowledge in AI, respectively. Discussion. Generally, the findings indicate that medical imaging students have fair knowledge about AI and its prospects in medical imaging but lack in-depth knowledge. However, they lacked the requisite awareness of AI's emergence in radiography practice in Ghana. They also showed a lack of knowledge of some general basic applications of AI in modern imaging equipment. Additionally, they showed some level of misconception about the role AI plays in the job of the radiographer.

Conclusion: Decision-makers should implement educational policies that integrate AI education into the current medical imaging curriculum to prepare students for the future. Students should also be practically exposed to the various incorporations of AI technology in current medical imaging equipment.

Aim: This study aimed to investigate the frequency of unnecessary tests requested in Be'sat Hospital in Hamadan.

Materials and methods: This descriptive research was conducted in order to investigate the frequency of unnecessary requests for CT scan and radiography of patients referring to the imaging department of Be'sat Hospital in Hamadan in a 4- to 6-month period. Patient information, including gender, age, type of CT scan test, the reason for requesting the test, the expertise of the requesting physician, and the result of the radiologist's report on each test, was extracted and collected.

Results: A total of 1000 CT scans were evaluated. The mean age of these patients was about 36 years and most of them were men. The highest and lowest percentages of unnecessary cases were related to CT scans of the brain (42.3%) and facial bones (2.3%), respectively. The most and the least unnecessary CT scans based on the reason given for the request were related to multiple physical trauma (30.7%) and chronic kidney disease (1.5%), respectively.

Conclusion: In all tests, over 74% of the reports were unnecessary and less than 26% were necessary. Therefore, it is necessary to reduce unnecessary requests to reduce the radiation dose of patients. Also, the knowledge of doctors should be increased in the field of appropriate evaluation of CT scan tests based on clinical guidelines.