To develop and validate a method for assessing the performance and long-term constancy of Automatic Exposure Control (AEC) in interventional fluoroscopy using retrospectively collected clinical data from a Radiation Dose Management System (RDMS). The method uses the system-reported Patient Equivalent Thickness (PET) as a surrogate for PMMA attenuation, enabling continuous evaluation of the air kerma rate-thickness response under clinical conditions. Three angiographic systems from two different manufacturers were monitored over two years (>135,000 fluoroscopic and acquisition events in the RDMS). Air kerma-rate data were grouped in 1-cm PET bins and analysed quarterly. Temporal deviations in the median air kerma rate and interquartile range (IQR) were computed relative to a reference quarter, and exponential air kerma rate-thickness fits were used to derive compensation coefficients (λ) as indicators of AEC stability. PET was experimentally validated against PMMA and water thickness (R2>0.98). One of the systems showed stable AEC behaviour, with quarterly median deviations within±5% and λ variations within a proposed±0.02cm⁻1 tolerance. In the other system, a 72% deviation was observed due to a detector failure; normal performance was restored after detector replacement and protocol updates. Outside this event, all systems remained within EFOMP±20% constancy limits. RDMS-based PET analysis reliably reproduces phantom-derived ESAKR-thickness behaviour and enables continuous, clinically driven AEC surveillance. The method is sensitive to hardware faults and configuration changes, and provides a robust complement-not a replacement-to periodic phantom-based AEC testing.

This study investigates the influence of arm positioning and metallic objects on radiation dose distribution during emergency CT scans, where time-critical workflows demand both speed and precision. Sub-optimal arm positioning and metallic artifacts can significantly alter dose patterns and compromise patient safety. Five clinical cases undergoing chest, abdomen, and pelvis (CAP) CT scans with automatic exposure control (AEC) at 120 kVp were analyzed using GATE/Geant4 Monte Carlo simulations. Patient-specific voxel models were generated from clinical CT datasets, and the scanner was modeled with z-axis tube current modulation. Variations in arm position (arms raised, crossed, or positioned alongside the torso) and the presence of metallic objects such as belts, keys, and necklaces were simulated. Dose maps were extracted in voxel format, and patient-specific CTDIvol-like metrics were computed and compared with scanner-reported CTDIvol values. Significant dose alterations were observed due to non-optimal arm positioning and metallic artifacts. Cases 1 and 4, both with metallic belts, exhibited elevated pelvic doses of 3.448 × 10⁻3 mGy and 3.658 × 10⁻3 mGy. Case 2, with arms crossed over the chest, recorded a 52% higher chest dose (5.244 × 10⁻3 mGy) compared to Case 1. Case 5, with arms positioned downward and a necklace, registered the highest maximum dose (5.475 × 10⁻3 mGy), 105% higher than the lowest dose in Case 3, where arms were raised with minimal metallic interference. Comparison with scanner-reported CTDIvol showed agreement within - 1.6 to - 17.8% across cases, confirming model consistency despite the absence of angular modulation. Improper arm positioning and metallic artifacts increase radiation dose in emergency CT, particularly in sensitive regions. Optimized positioning and artifact management are essential, even under time constraints, to ensure safe and effective dose delivery. Emergency radiology protocols should emphasize rapid but accurate arm positioning and removal of metallic objects to minimize radiation risks without compromising diagnostic quality.

Clinical X-ray dark-field radiography has shown to be promising for visualizing different lung pathologies. To keep the radiation dose as low as reasonably achievable (ALARA principle), individualized exposure planning is necessary. However, the current scanning-based implementation of dark-field radiography complicates the use of automatic exposure control. Previously, a BMI-based linear regression model was proposed as a substitute. Here, we aim to improve this proposed model by investigating multiple linear regression for patient-individual exposure planning of dark-field chest radiography. For this retrospective study, 273 posteroanterior thorax images acquired at a prototype system for dark-field chest radiography were analyzed retrospectively regarding the X-ray tube current needed to achieve the target radiation dose. Different multiple linear regression models were tested to find the optimal multiple regression model for predicting the necessary tube current based on a person's weight, height, age, and sex. R2 score, the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were used to evaluate the goodness-of-fit of different regression models. Each model was also compared to a BMI-based model. To predict the target tube current for dark-field chest radiography, multiple linear regression using ordinary least squares performed best (R2 = 0.712, RMSE = 0.234, and MAPE = 0.033). In comparison, simple linear regression using only the body mass index achieved only R2 = 0.627, RMSE = 0.266, and MAPE = 0.037. Multiple linear regression allows better exposure planning in X-ray dark-field chest radiography than simple linear regression.

We evaluated radiation dose and detectability changes with automatic exposure control (AEC) according to object size in dual-source (DS) and fast kV switching (FS) dual-energy computed tomography (DECT). A phantom with five section diameters (16-36cm) was scanned using different AEC settings (DS: Quality Reference mAs [QRmAs] 300-700; FS: Noise Index [NI] 8-12). Volume CT dose index (CTDIvol) and detectability index (d') for iodine were measured. Clinical CTDIvol data from 40 to 80kg patients undergoing liver dynamic DECT were retrospectively analyzed. In DS-DECT, CTDIvol increased slightly with section diameter but plateaued at QRmAs 600-700 for 31-36cm (31cm: 24.1 mGy; 36cm: 22.5-22.7 mGy), and d' decreased for larger sections. Clinical CTDIvol did not differ significantly among weight groups (40-<50kg: 21.5 mGy; 50-<60kg: 22.2 mGy; 60-<70kg: 22.8 mGy; mean; p = 0.13). In FS-DECT, CTDIvol and d' varied with NI and section diameter: for the 26-cm section, CTDIvol ranged from 15.0 to 30.8 mGy and d' from 37.5 to 59.0; for 36-cm section, CTDIvol was 39.9 mGy and d' 24.0-27.8, with smaller variations than single-energy CT (SECT). Clinical CTDIvol increased with patient weight up to 70kg (40-<50kg: 20.3 mGy, 50-<60kg: 25.8 mGy, 60-<70kg: 29.2 mGy; mean; p < 0.05). AEC behavior in DECT differs from SECT, causing variations in dose and detectability. Appropriate AEC settings in DECT can achieve image quality comparable to SECT.

The IEC exposure index (EI), deviation index (DI), and target exposure index (EIT), represent critical standardized metrics for the evaluation of exposure and quality in radiographic imaging. This work develops and validates a systematic procedure to estimate the EIT for eight of the most common radiography imaging protocols utilizing automatic exposure control (AEC) from measurements acquired under reference conditions. A model was developed to define the relationship between a systems AEC logic, and an estimation of the EI under flat-field conditions (EIFFC). Separately, clinical data and acquisition protocol information for resultant EI during patient studies were also collected for the eight protocols studied: Chest posteroanterior (PA), Chest lateral, Abdomen anteroposterior (AP), Pelvis AP, L-Spine AP, C-Spine AP, T-Spine AP, and Ribs AP. Data were collected from 41 x-ray units spanning seven institutions. For each protocol on each unit the EIFFC was computed based on the acquisition protocol, as well as median EI from clinical exams to produce a scaling factor (SF). Kruskal-Wallis statistical tests were used to compare SF's between vendors and AEC cell configurations. SFs for eight radiographic imaging protocols have been produced per vendor and per AEC cell selection. A workflow has been established for end-users to follow to apply these SFs to flat-field measurements taken at their own locations to establish local EIT. The study results show that in seven of the eight imaging protocols, the SFs for most units included in the study report SFs within 1 DI(±25%) of their respective final vendor reported SF (218/228). The ribs protocol is the exception to this finding (n=26). SFs have high utility for establishing EIT values on individual x-ray units and normalizing EI value distributions for quality assurance purposes.

Image quality assessment in mammography of women with cardiac implantable electronic devices: An insight into radiographers' experiences in Australia.

To systematically review and compare reduced-dose and standard-dose computed tomography (CT) protocols for craniocerebral and craniofacial trauma, focusing on image quality and radiation dose optimisation strategies. A systematic review was conducted following the PRISMA 2020 guidelines. Searches were performed across PubMed, EMBASE, Scopus, Web of Science, Cochrane Library, LILACS, and TripDatabase for studies published between November 2024 and January 2025. Observational studies comparing reduced-dose with standard-dose CT protocols were eligible. Two reviewers independently screened titles, abstracts and full texts, with discrepancies resolved by consensus. Data were extracted regarding scanner type, acquisition parameters, dose metrics (CTDIvol, DLP) and image quality assessments. Of 641 records screened, 7 studies met the inclusion criteria. None reported diagnostic accuracy metrics; therefore, a quantitative descriptive synthesis was performed. Across studies, reduced-dose CT protocols achieved 21%-89% dose reduction while maintaining comparable subjective and objective image quality. Iterative reconstruction, spectral shaping and current modulation were the most effective techniques for optimising image quality with lower radiation. Evidence was limited to observational studies without diagnostic reference standards, and heterogeneity in protocol reporting restricted data comparability. Reduced-dose CT protocols demonstrate comparable subjective and objective image quality to standard-dose acquisitions while achieving substantial radiation dose reductions. Techniques such as iterative reconstruction, tube-voltage modulation, spectral shaping and automated exposure control were consistently associated with meaningful dose optimisation. These findings support the clinical feasibility and safety of implementing dose-optimised CT protocols in trauma imaging. Findings support the clinical feasibility and safety of implementing dose-optimised CT protocols for trauma imaging.

This study quantitatively compared image quality and radiation dose between fixed scanning conditions and automatic exposure control (AEC)-applied conditions in ankle CT. The experiments were conducted using a Siemens SOMATOM X.ceed CT scanner and an RSD ankle phantom. The study was conducted with a total of four groups (A-D). Group A applied CARE kV and CARE Dose4D. Groups B and C applied only CARE Dose4D, with varying tube voltages (kVp). Group D was set to the fixed scanning condition. Dose was evaluated using CT dose index volume (CTDIvol), dose length product (DLP), and size-specific dose estimate (SSDE). Image quality was compared by setting region of interset (ROI) using Image J and FIJI programs to measure signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). SNR and CNR were highest in Group A and lowest in Group D. The results showed that SNR and CNR were highest in Group A and lowest in Group D,(p&lt;0.001) and PSNR exhibited a similar trend. SSIM values were greater than 0.999 in all groups, indicating high structural similarity. Radiation dose was lowest in Group D, followed by Group A. The results showed that the AEC-applied condition in ankle CT maintained a radiation dose comparable to that of the fixed scanning condition while demonstrating relatively favorable quantitative image quality metrics. However, as this study was limited to a phantom-based experiment and quantitative image quality evaluation, further patient-based studies are required to confirm its clinical applicability. These findings suggest that AEC may serve as an alternative scanning strategy for balancing radiation dose and image quality in ankle CT.

To assess the currently applied CT image acquisition protocols in lung cancer screening (LCS) and thereby fill a knowledge gap to support guideline development. Through worldwide distribution of an online survey, data on institutional and technical factors regarding CT acquisition protocols in LCS were collected between 06/2024 and 09/2025 on behalf of the SOLACE (Strengthening the screening of lung cancer in Europe) consortium. Global responses were received from 71 LCS institutions across 29 countries (all continents). Responsibility for CT protocol establishment and modification varied among professions (radiologists, radiographers, medical physicists, and manufacturer personnel). Protocol establishment was dominated by radiologists (64 of 115), with only one-third of institutions involving multiple professions. Technical questions were partially answered. Automatic exposure control was implemented in 88% of centers (43 of 49). Reconstructed slice thickness ranged from 0.625 to 1.5 mm, with 1.0 mm being most common (43 of 67). Increment ranged between 0.5 and 1.25 mm. Software support for LCS was used by 90% of respondents (35 of 39), primarily for nodule detection (92%), volumetry (89%), and calculation of volume doubling time (71%). Image reconstruction was dominated by iterative reconstruction with statistical modeling (30) or deep learning support (7), while filtered-back projection was marginally used (4). Lung cancer screening often pushes current device limits, which warrants a multiprofessional establishment of CT protocols. Variability in reconstruction calls for further study on the effects on volumetry. Optimizing protocols remains crucial to balance radiation dose reduction and diagnostic accuracy in guideline development. This international study evaluates current CT image acquisition protocols in lung cancer screening and implications for guidelines, highlighting insufficient multiprofessional engagement for protocol definition and pronounced variability in technical parameters, both of which demand harmonization to inform robust guideline development. Variability of CT acquisition protocols impacts lung cancer screening. International survey results shed light on currently applied protocols. The narrowed knowledge gap supports guideline recommendations and standardization.

DOSESTAT-QC® is a stand-alone automated quality control (QC) system used for daily quality assurance of X-ray equipment in Jönköping Region, Sweden. The software has been implemented for all mammography systems and interventional systems in the region. One of the image analysis included in the DOSESTAT-QC® software is performed in homogenous images and focuses on the standard deviations in mean pixel value (MV) and signal-to-noise ratio (SNR) in the images. Initially, the analyses were performed in 1cm2 regions of interest (ROIs) and the obtained values in each ROI were compared to the corresponding values for the entire image. While MV remained relatively stable over time, fluctuations in SNR together with imprecise localization of pixel errors, especially in the automatic exposure control (AEC) area, highlighted limitations. In this paper, an improved method for image evaluation is presented, which enables precise SNR baseline settings and clear visualization of deviations and dead pixels. Additionally, the adaption and clinical implementation of DOSESTAT-QC® to conventional X-ray systems in the region are described.

Despite recommendations and guidelines on patient contact shielding in X-ray imaging, substantial uncertainties remain in clinical practice, particularly concerning computed tomography (CT) examinations and vulnerable groups such as pediatric and pregnant patients. This study identifies gaps in existing recommendations and offers a comprehensive statement of the actual risks and benefits associated with patient shielding. A systematic literature search was conducted using Google Scholar and PubMed, alongside current national and international guidelines. Our special report focused on patient shielding in projection radiography, interventional radiology, and CT, with special emphasis on vulnerable patient groups sensitive to radiation exposure. Current research lacks robust, evidence-based data comparing the benefits and risks of patient shielding, especially in CT. In projection radiography and interventional radiology, patient shielding offers minimal benefits and may inadvertently increase radiation exposure due to interference with automatic exposure control or necessitate repeated examinations. This issue is particularly addressed in pediatric and pregnant patients. In CT, the benefits and risks are more complex, with substantial research gaps hindering informed decision-making. Traditional and generalized recommendations for patient contact shielding do not adequately account for technological advancements and individual patient needs. The use of patient shielding should be reconsidered on a case-by-case basis, guided by evidence-based research. There is an urgent need for clinical studies to assess the benefits, and in particular the risks in real-world settings, facilitating the development of precise patient-specific guidelines. Question While patient shielding can increase radiation dose due to interference with automatic exposure controls, uncertainties persist regarding patient shielding in X-ray imaging practices. Findings There is marginal evidence of the clinical risks of patient shielding, and urgent needs exist for patient-specific evidence-based shielding guidelines. Clinical relevance By critically evaluating the ambiguous guidelines on patient shielding and highlighting the lack of evidence-based risks of patient shielding, this study argues for individualized, evidence-based practices to improve patient safety in clinical radiology.

Mean glandular dose estimation in population-based breast screening in Queensland, Australia: Retrospective comparison of TG282 and Dance models (2022-2025).

Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased cancer risk; therefore, it is important to estimate suitable exposure factors for each patient prior to radiography. The present study aimed to utilize machine learning (ML) approach to predicting the suitable milliampere-seconds (mAs) and reducing overexposure in patients with metal implants during KUB radiography. A phantom was used to understand the effect of metal implants on radiation exposure during KUB radiography with automatic exposure control (AEC) technique. Subsequently, we retrospectively enrolled 619 subjects, including 56 with metal implants and 563 without, from one hospital (group A) and 323 subjects, including 89 with metal implants and 234 without, from another hospital (group B). All subjects underwent both KUB radiography and physiological examinations on the same day. Data on body parameters and exposure factors were retrieved from hospital database. To train the prediction model, the dataset of group A without metal implants was randomly divided into 80% and 20% for training and testing sets, respectively. Five different ML algorithms were utilized to train the prediction model using 10-fold cross-validation. The correlation coefficients (CC), mean average error (MAE), normalized root mean squared errors (nRMSE), and R-square (R2) were compared to find the optimal model. For external validation, the dataset of group B was randomly separated into 80% and 20% for training and testing sets, respectively. The training sets of both groups were combined for transfer learning, and the testing set of the group B was used to assess the optimal model. Furthermore, the final model was utilized to predict an appropriate mAs for patients with metal implants in both groups. Statistical analysis was performed to understand the differences between datasets, phantom settings, and ML models. Comparisons were considered significance if p<0.05. The phantom experiment demonstrated that the metal plate significantly increased the mAs and reached exposure (REX) values when using AEC technique during KUB radiography. The comparison of patient data showed that the patients with metal implants had significantly higher mAs and REX than those without in both groups. In group A, the ML comparisons showed that the artificial neural network (ANN) model outperformed other ML models in predicting mAs based on the testing set, exhibiting the highest CC of 0.791±0.007 and R2 of 0.6193±0.010. In group B, the external validation based on transfer learning demonstrated that the ANN model achieved the CC of 0.837±0.051 and R2 of 0.823±0.007 in the testing set. For patients with metal implants, the ANN model-predicted mAs was significantly lower than those obtained using AEC technique in both groups. We concluded that the ML approach is suitable for building the model for predicting appropriate mAs and reducing overexposure in patients with metal implants during KUB radiography.

Digital radiography of the sacrum requires precise adjustments of exposure parameters (kV, mA, time) to produce high-quality images while minimizing radiation exposure. This study aims to investigate how these exposure factors affect the quality of sacral images and to recommend optimal settings that align with radiation safety principles such as ALARA. By reviewing the existing literature, it was found that the modification of exposure parameters (kV, mA, time) in digital radiography is essential for achieving optimal image quality while minimizing radiation exposure. The exposure index (EI) serves as an indirect measure of the dose absorbed by the detector, thereby facilitating the implementation of the ALARA principles. Properly orienting the AEC chamber can reduce radiation dose by up to 44% without compromising image quality. Tube voltage and current adjustment enhances image contrast and sharpness. Nonetheless, inconsistent exposure methods and dependence on presets can still lead to dose creep. It is essential to train radiographers, adjust equipment settings, and set Diagnostic Reference Levels (DRLs) to enhance imaging quality and ensure patient safety. In digital radiography, factors such as tube voltage (kV), tube current (mA), and exposure time (s/mAs) significantly affect image quality and patient radiation dose. Adjusting exposure settings according to patient characteristics and exam objectives enhances image quality and reduces radiation exposure, particularly in sensitive areas like the sacrum. Technologies such as Exposure Index (EI), Automatic Exposure Control (AEC), and image analysis software facilitate an objective method that follows the ALARA principle, ensuring patient safety while optimizing diagnostic outcomes.

The demand for bedside radiography is increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in these settings remains heavily reliant on the expertise and experience of radiographers. To address this issue, a novel flat panel detector (FPD) integrated with an automatic exposure control (AEC) system has been developed. This study aims to experimentally evaluate the fundamental performance of this system and clarify its clinical utility, including its potential limitations. The dependency of the AEC performance on object thickness and tube voltage was investigated using acrylic phantoms. To simulate clinical scenarios, the AEC response was examined using a chest phantom. Additionally, the effects of source-to-image distance and oblique X-ray incidence on the AEC performance were also evaluated using a quality-control test device. Our results elucidated the behavior of the exposure index (EI) and image quality under varying tube voltage and object thickness. In clinical conditions, the introduction of the AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both the AEC response and image quality, such as sensor positioning, imaging distance, and beam angle. These findings demonstrate that the AEC-equipped FPD system maintains consistent image quality while effectively reducing the radiation dose under various simulated imaging conditions. Our results also underscore the importance of accounting for environmental factors that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation.

BackgroundThe measurement of computed tomography dose index 100 (CTDI100), which is feasible only through axial scanning, requires that the clinical spiral protocols be replaced with those for axial scanning. The real‐time ionization chamber detects the integral of radiation dose rate profile, enabling the direct verification of the volume CTDI on spiral CT scanning (CTDIvolSpiral).PurposeThis study aimed to develop a direct measurement technique for CTDIvolSpiral and compare its accuracy with that measured using axial scanning (CTDIvolAxial) or that displayed on the console (CTDIvolDisplayed).MethodsA CTDI phantom with a real‐time ionization chamber was placed on the headrest or examination table. CTDI100Axial was measured with following parameters: tube voltage = 120 kV, effective mAs = 100, and rotation time = 1.00 s. The parameters for measuring CTDI100Spiral were set identical to those used for axial scanning, except for rotation times = 0.33, 0.50, and 1.00, pitch = 0.35, 0.50, 0.75, 1.00, 1.25, and 1.50, and the scanning range = 15 cm. CTDI100Spiral was extracted from the integral of radiation dose rate profile. CTDIvolSpiral was subsequently calculated and compared with CTDIvolAxial and CTDIvolDisplayed. Finally, CTDIvolSpiral was measured for 10 clinical protocols and compared with CTDIvolDisplayed.ResultsThe differences between CTDIvolAxial and CTDIvolDisplayed, CTDIvolSpiral and CTDIvolDisplayed, and CTDIvolAxial and CTDIvolSpiral for the head and body phantoms were all < −2.0%. The differences between CTDIvolDisplayed and CTDIvolSpiral for scans on clinical protocols with and without automatic exposure control were < 11.9% and < 10.5%, respectively; these large differences were observed in the dual‐energy twin‐beam protocol. Excluding this protocol yielded differences between the measurements with and without automatic exposure control of < 2.0% and < −3.9%, respectively.ConclusionsThe results showed an excellent agreement between CTDIvolAxial and CTDIvolSpiral, supporting the use of the clinical spiral CT scanning to verify CTDIvolDisplayed using a real‐time ionization chamber.

This study aimed to optimize the scan parameters of the Siemens Somatom Scope CT simulator to ensure optimal image quality for the detection of brain tumors. To achieve this, measurements were performed using the Catphan CTP 503 phantom, evaluating metrics such as the contrast-to-noise ratio (CNR), low-contrast visibility (LCV), signal-to-noise ratio (SNR), noise level, and uniformity index (UI). The optimization process involved adjusting scan parameters such as kilovoltage (kV), tube current (mA), and the automatic exposure control system (CareDose4D). The results showed that the optimized protocol (Protocol 2) achieved the highest CNR values—61.41 for polymethylpentene (PMP) and 47.9 for low-density polyethylene (LDPE)—as well as the best LCV and an SNR of 31.2. In addition, it exhibited the lowest noise level (0.3%) and the best uniformity index (0.03). These findings suggest that Protocol 2 may be an effective tool for improving the accuracy of brain structure delineation and other anatomical regions, thereby enhancing radiotherapy treatment planning.

High-resolution computed tomography (HRCT) provides exceptional diagnostic precision but raises significant concerns about thyroid radiation exposure, given the gland’s high radiosensitivity and the increasing global burden of thyroid cancer. Approximately 560,000 new cases of thyroid cancer are diagnosed worldwide each year, with a female-to-male incidence ratio of roughly 3:1 and the highest age-standardized rates in high-income regions. Ionizing radiation is a well-established risk factor, particularly in children and adolescents, where even low doses (&lt;0.2 Gy) can increase lifetime cancer risk. This review critically evaluates methods for quantifying thyroid dose during HRCT, including direct approaches such as thermoluminescent dosimeters (TLDs) and optically stimulated luminescent dosimeters (OSLDs), as well as indirect metrics like the computed tomography dose index (CTDI), dose– length product (DLP), and Monte Carlo simulations. Protective strategies are examined in detail, encompassing hardware-based measures (thyroid collars, bismuth shields), software and algorithmic solutions (automatic exposure control, iterative reconstruction), and imaging protocol optimization tailored to patient size, anatomy, and clinical need. Technological innovations, such as ultra-high-resolution CT and photon-counting detector CT, are discussed for their potential to reduce exposure without compromising diagnostic quality. The review also explores the influence of patient-specific factors, operator expertise, and cost–benefit considerations in implementing protective measures. Emphasis is placed on adhering to the “As Low as Reasonably Achievable” (ALARA) principle, ensuring that diagnostic accuracy is maintained while minimizing avoidable thyroid dose. Adoption of evidence-based protocols, accurate dosimetry, and continuous professional education is essential to enhance radiation safety in HRCT and reduce long-term thyroid health risks.

PURPOSEThe exposure index (EI), the target exposure index (EIT), and the deviation index (DI) have been defined in the IEC Standard 62494‐1 Ed.1 2008‐08. This study investigates the impact of certain acquisition parameters, the imaged anatomy, and the manufacturer's specificities on the EI of radiological images and how these may affect EIT setting procedure.METHODSImages were acquired using two digital radiography (DR) systems of two different manufacturers, using aluminum attenuators and an anthropomorphic phantom. Acquisition parameters like the tube potential (kVp), the tube loading (mAs), the exposure time, the automatic exposure control (AEC) system settings (sensor and dose level selection), the grid (with or without), the additional filtration, the field size, and the imaged anatomy were varied and their effect on the EI was quantified separately for each system.RESULTSEI is linearly related to the incident air kerma (IAK) on the detector as expected (by definition). For constant IAK, EI increases with increasing kVp. While EI in general is reduced in the presence of scatter, this may not always be the case. Under AEC operation, even the exposure time can make a difference. EI is strongly affected by the imaged anatomy in combination with the AEC sensor and field size selections, the examination protocol, and the manufacturer.CONCLUSIONSMany parameters affect the EI calculation apart from IAK. Among them, the most important are the imaged anatomy and the manufacturer. Since the EI calculation is a complex procedure, setting of the EIT values should be done with caution on a per‐examination and manufacturer basis, since the values that apply for one digital system are not always applicable to another. Furthermore, when EI is used as an image quality tool, a DI variation of at least ±2 should be allowed before a possibly meaningful red flag is activated.

In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters. The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version. Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions.