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Reducing unnecessary retakes is a critical responsibility of radiologic technologists and plays an important role in promoting medical safety by minimizing patient radiation exposure. This study aimed to evaluate the impact of retake visualization using the general radiography management system "RADInsight" and the effectiveness of the educational interventions through periodic retake review meetings. General radiographic examinations performed at our institution from June 2023 to October 2024 were retrospectively analyzed. Retake rates and contributing factors were examined by anatomical region and cause. The overall retake rate significantly decreased from a maximum of 7.5% to 2.1%. Positioning errors were the most frequent cause of retakes, with notable improvements observed in lateral views of the knee and elbow following educational intervention. Inexperienced technologists showed a higher incidence of retakes due to motion artifacts and insufficient exposure. The combined use of RADInsight for retake monitoring and targeted educational intervention effectively reduced retake rates. This approach demonstrated its value as a practical strategy to support radiologic technologist training and reduce patient radiation exposure, thereby contributing to enhanced medical safety. Wider adoption and multi-institutional application are anticipated in the future.
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The purpose of this study was to evaluate the impact of contrast enhancement and arterial diameter on the attenuation values of virtual non-contrast (VNC) images (HUVNC) obtained from abdominal dynamic computed tomography (CT) examinations using a rapid kilovolt-switching dual-energy CT system. Seventy-keV virtual monoenergetic (VME) and VNC images were reconstructed from scanned raw data of the unenhanced, arterial, portal, and delayed phases. Circular regions of interest (ROIs) were placed on four arteries of different diameters (abdominal aorta: 20 mmϕ, common iliac artery: 10 mmϕ, superior mesenteric artery: 5 mmϕ, and inferior mesenteric artery: 2.5 mmϕ) at the same anatomical level between each contrast enhancement level. The attenuation values and standard deviations of the VME images (HUVME) and HUVNC in each ROI were measured. VNCError, the differences between HUVME in the unenhanced phase and HUVNC in each contrast enhancement level, was calculated. HUVME decreased as the contrast enhancement level declined, regardless of the arterial diameter. Similarly, the HUVME in the same contrast enhancement level decreased as the arterial diameter decreased. This tendency was particularly evident at high contrast enhancement levels. As arterial diameter decreased or the contrast enhancement level increased, HUVNC and VNCError increased. At HUVME of 400 Hounsfield unit (HU), the maximum VNCError was 78.47 HU at 2.5 mmϕ and 6.68 HU at 20 mmϕ. At HUVME of 130 HU, the influence on HUVNC was smaller than at other contrast enhancement levels, with VNCError of no more than 13.67 HU at 2.5 mmϕ. This study suggests that HUVNC increases are significantly affected by both the contrast enhancement and arterial diameter, especially at high contrast enhancement levels and in narrow arteries.
To compare the diagnostic performance of syngo.CT Brain Hemorrhage (BH) (Siemens Healthcare, Forchheim, Germany) with physician interpretations on noncontrast head CT in patients with suspected intracranial hemorrhage (ICH), and to elucidate the volume and Hounsfield unit (HU) metrics of false-positive (FP) cases. We analyzed 509 noncontrast head CT examinations obtained for suspected ICH. Presence or absence of hemorrhage was determined by board-certified radiologists or clinicians. Among positive cases, hemorrhage subtypes were labeled as intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), subdural hematoma (SDH), epidural hematoma (EDH), and subarachnoid hemorrhage (SAH). BH outputs were categorized as true positive (TP), false positive (FP), false negative (FN), or true negative (TN). For FP cases, lesion location and BH-derived quantitative indices-lesion volume and HU values (minimum, maximum, mean, and standard deviation)-were compared with TP cases. BH achieved a sensitivity 100%, a specificity 82.2%, a FP rate 17.8%, and an FN rate 0%. TP cases comprised IPH 64, SAH 33, SDH 83, EDH 11, and IVH 14. FP findings were located in cerebral sulci (n=22), brain parenchyma (n=12), vessels (n=10), dura mater (n=5), and bone (n=5). All BH indices differed between FP and TP cases (p<0.01). BH showed favorable diagnostic performance relative to physician interpretations, and FP cases exhibited statistically significant differences from TP cases in lesion volume and HU-based metrics.
In mammography, it is desirable for the patient's mean glandular dose (MGD) to be minimal while the signal difference to noise ratio (SDNR) of lesions remains high. However, these two factors are inversely related, and the optimal automatic exposure control (AEC) settings have not been clearly defined. Even the quality assurance programme for digital mammography by the International Atomic Energy Agency (IAEA) specifies only acceptable and achievable SDNR and MGD values for AEC settings, based on the mammography machine model and polymethyl methacrylate (PMMA) phantom thickness. In this report, we propose a method to simultaneously optimize both SDNR and MGD at AEC settings.
This study aimed to verify the contouring accuracy of the artificial intelligence (AI)-based auto-segmentation software Contour+ (MVision AI Oy, Helsinki, Finland) both quantitatively and visually, and to evaluate its clinical validity for the thoracic region in Japanese patients. Ten thoracic radiotherapy cases with lung lesions were analyzed. Contour+ was used to automatically delineate both lungs, trachea, bronchus, esophagus, spinal cord, and heart. Three observers visually evaluated the auto-contours using a five-point scoring system, and the final manually corrected contours were used as the reference to calculate the dice similarity coefficient (DSC), Hausdorff distance (HD), and volume differences. In all cases, the AI auto-contours were evaluated as "clinically acceptable with minor modifications (score ≥3)," with an average score of 4.4. The mean DSC values were 1.00 for the lungs, 0.99 for the trachea, 0.91 for the bronchi, 0.86 for the esophagus, 0.99 for the spinal cord, and 0.99 for the heart, indicating high agreement. The mean HD values were 5.68 mm, 8.72 mm, and 3.30 mm for the bronchi, esophagus, and heart, respectively. The mean volume changes after manual correction were 4.02 cc for the bronchi, 2.55 cc for the esophagus, and 2.58 cc for the heart. AI-based auto-segmentation software Contour+ demonstrated high geometric agreement and clinical validity for major thoracic organs in Japanese patients, suggesting its potential to reduce the contouring workload and promote standardization in radiotherapy treatment planning.
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The purpose of this study was to shorten scan time for three-dimensional fat-suppressed T2-weighted imaging (3D FS-T2WI) using DIXON and compressed sensing (HyperSense: HS), while maintaining adequate visualization of the brachial plexus. 3D FS-T2WI of a phantom and the neck of healthy volunteers were acquired while varying the echo time (TE), echo train length (ETL) and HS factor (HSf). Image quality was evaluated based on the visual assessment of the brachial plexus in volunteers and the contrast ratios between nerve-mimicking, muscle-mimicking, blood-mimicking, and fat-mimicking materials in the phantom. The highest nerve-to-muscle and nerve-to-blood contrast ratios, as well as the best visual evaluation scores, were observed with longer TE values. As ETL increased, both tissue contrast ratios and visual evaluation scores decreased; however, scan time became shorter. Increasing the HSf did not significantly affect contrast ratios or visual evaluation scores, but it also contributed to a shorter scan time. Using the parameters TE=110 ms, ETL=120, and HSf=2.5, it was possible to shorten scan time while maintaining visualization of the brachial plexus.
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This study aimed to evaluate the performance of several large language models (LLMs) on the Japanese National Examination for Radiological Technologists and to characterize their performance profiles. We utilized a dataset comprising questions from 12 consecutive years of the national examination (the 65th to the 76th iterations), excluding items that were officially retracted or deemed inappropriate. 5 distinct LLMs (ChatGPT-3.5, Gemini 2.5 Flash, Gemini 2.5 Pro, Copilot, and Claude Sonnet 4) were prompted to answer these questions. The accuracy of each LLM was calculated for the entire question set and for subsets categorized by question format. Across the entire examination and within numerous subject areas, Gemini 2.5 Pro achieved the highest accuracy. An analysis by question format revealed a general trend: most LLMs demonstrated superior performance on text-based questions, followed by calculation-based and then image-based questions. However, some models exhibited notably strong performance specifically on calculation-based problems. While LLMs demonstrate considerable proficiency in answering questions from the National Examination for Radiological Technologists, our findings also reveal significant limitations, particularly in their capacity to interpret image-based problems. This study highlights both the potential utility and the current challenges of leveraging LLMs as supplementary learning tools for this professional certification examination.
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The objective of this study was to evaluate metal artifacts on axial planes without visible metal by simulating postoperative computed tomography (CT) examinations following coil embolization of dural arteriovenous fistulas. Specifically, we assessed the behavior of metal artifacts in the pericoil region and aimed to propose an imaging protocol that minimizes metal artifact occurrence. An intracranial metallic coil phantom was constructed and scanned using two different area detector CT systems. The behavior of metal artifacts around the coil was evaluated according to the axial position of the metal coil, collimation width, reconstruction algorithm, scan mode, and the presence or absence of metal artifact reduction (MAR). We confirmed that CT value elevation occurred on axial planes without visible metal due to artifacts originating from the metallic coil. This phenomenon was observed regardless of coil position, scan mode, or the use of MAR. It also occurred with all reconstruction algorithms except model-based iterative reconstruction. Furthermore, narrowing the collimation width reduced the magnitude of CT value elevation. Because artifacts occurring on axial planes without visible metal may be reduced by using a narrower collimation width, scanning with narrow collimation is recommended for postoperative CT examinations following cerebral aneurysm embolization.
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