OBJECTIVE: During computed tomography (CT), a large amount of ionizing radiation is emitted to ensure high quality of the obtained radiological image. This study measured the dose distribution around the CT scanner and the exposure of people staying near the CT scanner during the examination.
METHODS: The measurements used an anthropomorphic phantom to assess human exposure to ionizing radiation. The probability of inducing leukemia and other cancers as a result of absorbing doses recorded around the CT device was also calculated.
RESULTS: The highest exposure to scattered radiation in the proximity of the CT scanner is recorded at the gantry of the tomograph, i.e., 55.7 μGy, and the lowest, below lower detection limit of 6 μGy at the end of the diagnostic table. The whole-body detector placed on the anthropomorphic phantom located at the diagnostic table right next to the CT gantry recorded 59.5 μSv and at the end of the table 1.5 μSv. The average doses to the lenses in these locations were: 32.1 μSv and 2.9 μSv, respectively.
CONCLUSIONS: The probability of induction of leukemia or other types of cancer is low, but the need for people to stay in the examination room during a CT examination should be limited to the necessary minimum. Int J Occup Med Environ Health. 2024;37(3).

OBJECTIVE: To describe the reliability of ultra-high-resolution computed tomography (UHR-CT) in the measurement of titanium stapes prostheses using manufacturer data as a reference.
METHODS: This retrospective study included patients treated by stapedectomy with titanium prostheses who underwent UHR-CT between January 2020 and October 2023. Images were acquired using an ultra-high-resolution mode (slice thickness: 0.25 mm; matrix, 1024 × 1024). Two radiologists independently evaluated the length, diameter, and intra-vestibular protrusion of the prosthesis. Post-operative air-bone gaps (ABGs) were recorded.
RESULTS: Fourteen patients were enrolled (mean age, 44.3 ± 13.8 [SD] years, 9 females), resulting in 16 temporal bone UHR-CTs. The exact length was obtained in 81.3 % (n = 13/16) and underestimated by 0.1 to 0.3 mm in the remaining 18.7 % (n = 3/16) CT scans for both readers (mean misestimation: -0.02 ± 0.06 [SD] mm, overall underestimation of 0.43 %). The exact diameter was reported in 75 % (n = 12/16) and 87.5 % (n = 14/16) of the CT scans for readers 1 and 2, respectively, and was off by 0.1 mm in all discrepancies (mean misestimation: 0.01 ± 0.04 [SD] mm, overall overestimation of 2.43 %). Intravestibular prosthesis protrusion was of 0.5 ± 0.43 [SD] mm (range: 0-1) and 0.49 ± 0.44 [SD] mm (range: 0-1.1) for readers 1 and 2, respectively, and did not correlate with ABGs (r = 0.25 and 0.22; P = 0.39 and 0.47 for readers 1 and 2, respectively). Intra and interobserver agreements were excellent.
CONCLUSIONS: UHR-CT provides 99.6 % and 97.6 % accuracy for prosthesis length and diameter measurements, respectively.

OBJECTIVE: To evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma.
METHODS: Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.
RESULTS: 105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778-1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.
CONCLUSIONS: The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

Despite its undeniable advantages, the operation of a CT scanner also carries risks to human health. The CT scanner is a source of ionizing radiation, which also affects people in its surroundings. The aim of this paper is to quantify the radiation exposure of workers at a 3D CT wood scanning workplace and to determine a monitoring program based on measurements of ionizing radiation levels during the operation of a CT log scanner. The workplace is located in the Biotechnology Park of the National Forestry Centre. The ionizing radiation source is located in a protective cabin as a MICROTEC 3D CT machine with an X-ray lamp as X-ray source. The CT scanner is part of the 3D CT scanning line and its function is continuous quality scanning or detection of internal defects of the examined wood. The measurement of leakage radiation during scanning is performed with a metrologically verified meter. The measured quantity is the ambient dose equivalent rate H˙*10. The results of the measurements at the selected measurement sites have shown that, after installation of additional safety barriers, the CT scanner for the logs complies with the most strict criteria in terms of radiation protection. Workers present at the workplace during the operation of the CT scanner are not exposed to radiation higher than the background radiation level.

UNASSIGNED: Weightbearing computed tomography (WBCT) is becoming a valuable tool in the evaluation of foot and ankle pathology. Currently, cost analyses of WBCT scanners in private practice are lacking in the literature. This study evaluated the costs of acquisition, utilization, and reimbursements for a WBCT at a tertiary referral center, information of particular interest to practices considering obtaining such equipment.
UNASSIGNED: All WBCT scans performed at a tertiary referral center over the 55-month period (August 2016 to February 2021) were retrospectively evaluated. Patient demographics, pathology location, etiology, subspecialty of the ordering provider, and whether the study was unilateral or bilateral were collected. Reimbursement was calculated based on payor source as a percentage of Medicare reimbursement for lower extremity CT. The number of total scans performed per month was evaluated to determine revenue generated per month.
UNASSIGNED: Over the study period, 1903 scans were performed. An average of 34.6 scans were performed each month. Forty-one providers ordered WBCT scans over the study period. Foot and ankle fellowship-trained orthopaedic surgeons ordered 75.5% of all scans. The most common location of pathology was the ankle, and the most common etiology was trauma. The device was cost neutral at 44.2 months, assuming reimbursement for each study was commensurate with Medicare rates. The device became cost neutral at approximately 29.9 months when calculating reimbursement according to mixed-payor source.
UNASSIGNED: As WBCT scan becomes more widely used for evaluation of foot and ankle pathology, practices may be interested in understanding the financial implications of such an investment. To the authors\' knowledge, this study is the only cost-effectiveness analysis of WBCT based in the United States. We found that in a large, multispecialty orthopaedic group, WBCT can be a financially viable asset and a valuable diagnostic tool for a variety of pathologies.
UNASSIGNED: Level III, diagnostic.

Three-dimensional scanning technology has been traditionally used in the medical and engineering industries, but these scanners can be expensive or limited in their capabilities. This research aimed to develop low-cost 3D scanning using rotation and immersion in a water-based fluid. This technique uses a reconstruction approach similar to CT scanners but with significantly less instrumentation and cost than traditional CT scanners or other optical scanning techniques. The setup consisted of a container filled with a mixture of water and Xanthan gum. The object to be scanned was submerged at various rotation angles. A stepper motor slide with a needle was used to measure the fluid level increment as the object being scanned was submerged into the container. The results showed that the 3D scanning using immersion in a water-based fluid was feasible and could be adapted to a wide range of object sizes. The technique produced reconstructed images of objects with gaps or irregularly shaped openings in a low-cost fashion. A 3D printed model with a width of 30.7200 ± 0.2388 mm and height of 31.6800 ± 0.3445 mm was compared to its scan to evaluate the precision of the technique. Its width/height ratio (0.9697 ± 0.0084) overlaps the margin of error of the width/height ratio of the reconstructed image (0.9649 ± 0.0191), showing statistical similarities. The signal-to-noise ratio was calculated at around 6 dB. Suggestions for future work are made to improve the parameters of this promising, low-cost technique.

Traumatic brain injury (TBI) is a major public health problem worldwide. Although computed tomography (CT) scans are often used for TBI workup, clinicians in low-income countries are limited by fewer radiographic resources. The Canadian CT Head Rule (CCHR) and the New Orleans Criteria (NOC) are widely used screening tools to rule out clinically important brain injury without CT imaging. Although these tools are well validated in studies from upper- and middle-income countries, it is important to study these tools in low-income countries. This study sought to validate the CCHR and NOC in a tertiary teaching hospital population in Addis Ababa, Ethiopia.
This single-center retrospective cohort study included patients older than 13 years presenting from December 2018 to July 2021 with a head injury and a Glasgow Coma Scale score of 13-15. Retrospective chart review collected demographic, clinical, radiographic, and hospital course variables. Proportion tables were constructed to determine the sensitivity and specificity of these tools.
A total of 193 patients were included. Both tools showed 100% sensitivity for identifying patients requiring neurosurgical intervention and abnormal CT scans. The specificity for the CCHR was 41.5% and 26.5% for the NOC. Male gender, falling accidents, and headaches had the strongest association with abnormal CT findings.
The NOC and the CCHR are highly sensitive screening tools that can help rule out clinically important brain injury in mild TBI patients without a head CT in an urban Ethiopian population. Their implementation in this low-resource setting may help spare a significant number of CT scans.

Background: After stereotactic body radiation therapy (SBRT) for lung tumors, follow-up CT scans remain a pitfall. The early detection of local relapse is essential to propose a new treatment. We aim to create a local recurrence predictive score using pre- and post-therapeutic imaging criteria and test it on a validation cohort. Methods: Between February 2011 and July 2016, lung tumors treated by SBRT with available pretreatment fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) and follow-up CT scans were retrospectively analyzed. The risk factors associated with relapse were identified by univariate logistic regression on a train cohort. The score was created using these factors, merging clinical and imaging criteria associated with local relapse, and then tested on an independent validation cohort. Overall and local relapse-free survival at 1 and 3 years were recorded. Results: Twenty-eight patients were included in the train cohort and ten in the derivation cohort (male 74%, median age 70 ± 12 years). Five variables significantly associated with local recurrence (female gender; sequential enlargement; craniocaudal growing; bulging margins; standardized uptake value (SUVmax > 5.5)) were combined to create the score on five points. With the threshold >2.5/5, the sensitivity and specificity of the score on the validation cohort were 100% and 88%, respectively. Overall survival and local relapse-free survival at 1 and 3 years were 89% and 42%, and 89% and 63%, respectively. Conclusion: The local recurrence risk score created has high sensitivity (100%) and specificity (88%), upon independent validation cohort, to detect local relapse. This score is easy to use in daily clinical practice.

Complex medical devices are controlled by instructions sent from a host personal computer (PC) to the device. Anomalous instructions can introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical device components (e.g., manipulation of device motors), or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human error (e.g., using the wrong protocol, or misconfiguring the protocol\'s parameters by a technician), or host PC software bugs. Thus, anomalous instructions might represent an intentional threat to the patient or to the device, a human error, or simply a non-optimal operation of the device. To protect medical devices, we propose a new dual-layer architecture. The architecture analyzes the instructions sent from the host PC to the physical components of the device, to detect anomalous instructions using two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instruction\'s content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies in both the clinical objective and patient contexts using a set of supervised classifiers pre-trained for each specific context. The proposed dual-layer architecture was evaluated in the computed tomography (CT) domain, using 4842 CT instructions that we recorded, including two types of CF anomalous instructions, four types of clinical objective context instructions and four types of patient context instructions. The CF layer was evaluated using 14 unsupervised anomaly detection algorithms. The CS layer was evaluated using six supervised classification algorithms applied to each context (i.e., clinical objective or patient). Adding the second CS supervised layer to the architecture improved the overall anomaly detection performance (by improving the detection of CS anomalous instructions [when they were not also CF anomalous]) from an F1 score baseline of 72.6%, to an improved F1 score of 79.1% to 99.5% (depending on the clinical objective or patient context used). Adding, the semantics-oriented CS layer enables the detection of CS anomalies using the semantics of the device\'s procedure, which is not possible when using just the purely syntactic CF layer. However, adding the CS layer also introduced a somewhat increased false positive rate (FPR), and thus reduced somewhat the specificity of the overall process. We conclude that by using both the CF and CS layers, a dual-layer architecture can better detect anomalous instructions to medical devices. The increased FPR might be reduced, in the future, through the use of stronger models, and by training them on more data. The improved accuracy, and the potential capability of adding explanations to both layers, might be useful for creating decision support systems for medical device technicians.

OBJECTIVE: The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso-center, and reconstruction filter.
METHODS: An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full-width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso-center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers.
RESULTS: The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso-center position by up to 4 cm above and 4 cm below the iso-center. The automated results were similar (to within 0.1 mm) for various reconstruction filters.
CONCLUSIONS: We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results. It provided an easy and effective method of determining slice thickness.