Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization

Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization Sho Maruyama, Hiroki Saitou, Nao Koyama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6867187/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract The demand for bedside radiography has been increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in such settings has relied heavily 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 along with potential limitations. The dependency of 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. The effects of source-to-image distance and oblique X-ray incidence on 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 AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both 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 radiation dose across diverse imaging situations. Nevertheless, our results also underscore the importance of accounting for environmental variables that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation. auto exposure control flat panel detector bedside radiography diagnostic reference levels optimization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Recently, the number of hospitalized patients in Japan has increased. According to a report by the Ministry of Health, Labor and Welfare, as of June 2024, the average daily number of inpatients across hospitals nationwide has reached approximately 1.12 million [ 1 ]. In this context, driven by the dual imperatives of infection control and the needs of critically ill patients with mobility challenges, there has been an increase in the demand for bedside radiography performed using mobile X-ray systems. In diagnostic radiology, optimizing the patient exposure to radiation is recognized as a significant global concern. The International Commission on Radiological Protection (ICRP) introduced the concept of diagnostic reference levels (DRLs) in 1996 [ 2 ], subsequently enhancing its application and evaluation in ICRP publication 135 [ 3 ]. The Japan Network for Research and Information on Medical Exposure (J-RIME) published DRLs in 2015 [ 4 ] and further refined these guidelines in 2020 [ 5 ]. Furthermore, revisions to the Medical Care Act in April 2023 mandated the implementation of DRLs in medical institutions of a certain scale [ 6 , 7 ]. A key technology associated with these optimization efforts is auto exposure control (AEC). This technology automatically adjusts the operation of the X-ray generator based on the patient’s body thickness and the anatomical region being imaged, thereby aiming to reduce radiation exposure while maintaining appropriate image quality [ 8 , 9 ]. The ICRP positions AEC as a key measure for achieving appropriate dose management, ensuring that radiation exposure does not exceed DRLs [ 4 ]. Similarly, J-RIME advocated for the implementation of AEC in the 2020 DRLs to enhance dose optimization in medical settings [ 5 ]. However, conventional AEC systems are external components that are affixed to the detectors of fixed X-ray systems installed in radiography rooms, rendering their application impractical in other settings. Consequently, dose adjustments during imaging procedures in bedside or operating room context using mobile X-ray units have largely depended on the expertise and proficiency of the radiographer, presenting significant challenges. To address this issue, a novel flat panel detector (FPD) with integrated AEC functionality has been developed [ 10 , 11 ]. This innovative system employs a semiconductor sensor within the detector to detect X-rays and automatically terminates X-ray generation upon reaching a predetermined optimal imaging dose. When utilized with mobile X-ray units, this technology facilitates AEC-based dose management even in bedside and operating room scenarios, providing a significant advantage over conventional external AEC systems. Despite its potential, the AEC-equipped FPD is a relatively new technology, and reports on its characteristics and practical utilization are limited. Therefore, this study aims to experimentally evaluate the fundamental performance of the newly developed AEC-equipped FPD and assess its clinical utility, as well as the associated challenges. The objective of this investigation is to clarify the technology’s potential contribution to dose optimization in emerging imaging systems. Materials and Methods Experimental equipment For the FPD system with AEC functionality, we utilized the CXDI-720CW (CANON MEDICAL SYSTEMS, Tochigi, Japan). The FPD features a pixel size of 0.125 mm, an output image matrix size of 2800 × 3408 pixels, and a grayscale depth of 12 bits. X-ray irradiation was conducted using a mobile X-ray imaging system, Mobirex i9 (SHIMADZU CORPORATION, Kyoto, Japan), with a total filtration of 2.5 mm aluminum. The FPD system is equipped with five sensor areas that serve as AEC sensors. These regions are labeled A (top left), B (top right), C (center), D (bottom left), and E (bottom right), as shown in Fig. 1 . Each sensor area is uniformly sized at 560 × 560 pixels (7 cm × 7 cm). For validation purposes, we employed several phantoms as imaging subjects, including polymethyl methacrylate (PMMA), chest phantom, and quality control phantom (1-shot phantom Primus A; IBA, Bayern, Germany). Image analysis was performed using ImageJ version 1.46r and Python version 3.9.12, with statistical analysis conducted using custom-written Python scripts. Fundamental characteristics (Object thickness dependence and tube voltage property) The IEC 60601-2-54:2022 standard defines the evaluation methods for AEC dose control under specified tube voltage and object thickness conditions [ 12 ]. We assessed the basic characteristics of the system according to these guidelines. The geometrical arrangement shown in Fig. 2 (a) is employed, with the tube voltage set at 60, 80, 100, and 120 kV, while varying the PMMA thickness at 10, 15, and 20 cm to determine the exposure index (EI) [ 13 – 15 ] and tube current-time product (mAs). The imaging system applied chest-specific image processing to all acquired images, as per design specifications. The output of the mobile X-ray unit was regulated by milliampere-seconds values that corresponded to the selected tube voltages [ 16 ]. The EI serves as a critical indicator of system sensitivity, reflecting the X-ray dose that reaches the FPD. Given that the transmitted X-ray dose incident on the FPD varies based on the characteristics of the object, the EI facilitates the evaluation of dose adequacy. Standardized under IEC 62494, EI allows for inter-system comparisons and provides a dose indication that is proportional to the incident X-ray quantity [ 17 , 18 ]. To assess the dependence of radiation dose and image quality on object thickness, the signal-difference-to-noise ratio (SdNR) was calculated based on the pixel values in the central (1024 × 1024 pixels) and peripheral areas of the PMMA images. SdNR is determined as follows [ 19 , 20 ]: $$\:SdNR=\:\frac{\left|{I}_{o}-\:{I}_{B}\right|}{{SD}_{B}},\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ where \(\:{I}_{o}\) represents the mean digital value in the central area of the PMMA phantom, \(\:{I}_{B}\) represents the mean digital value in the peripheral (background) area, and \(\:{SD}_{B}\) represents the standard deviation (SD) in the background region. This methodology enabled a comprehensive assessment of the variation of SdNR with changes in object thickness. As per IEC guidelines, a phantom thickness of 15 cm was utilized for evaluating all tube voltage conditions. Therefore, to investigate the tube voltage characteristics, the PMMA thickness was maintained at 15 cm, whereas the tube voltage was varied across four levels (60, 80, 100, and 120 kV). The correlation among EI, mAs, and SdNR was subsequently evaluated. Evaluation of AEC sensor selection response for clinical applications (dup: abstract ?) The five AEC sensors integrated into the FPD can be configured using various combinations of the following modes: “non” (unused), “single” (individual), “or”, and “and.” To assess the variation in the AEC response characteristics based on the selected sensor(s), we measured the EI and exposure time while acquiring images of a chest phantom using the geometric setup shown in Fig. 2 (a). In this validation step, the PMMA phantom was substituted with a chest phantom to approximate human anatomical structures more closely. The tube voltage was at 85 kV, a value commonly employed in clinical practice. Under non-AEC conditions, the exposure parameters were fixed at 160 mA and 160 ms. When the AEC was activated, the system automatically adjusted the mAs. The irradiation time was recorded, as, with a constant tube voltage and phantom thickness, the tube current remains fixed, allowing variations in exposure to be represented by time variations. Subsequently, to examine the impact of sensor configuration on image quality, we selected two sensor combinations based on their contrasting response characteristics: one configuration that yielded the highest EI (“C” sensor alone) and another that produced the lowest EI (“D or E” sensors). The image quality under these two AEC settings was compared with that obtained without AEC. To facilitate the evaluation of image contrast, a cylindrical rod with a diameter of 1 cm was affixed to the chest phantom to facilitate the assessment of image contrast. The low-contrast object-specific contrast-to-noise ratio (CNR LO ) was subsequently calculated [ 21 – 23 ] (Fig. 3 ). CNR LO serves as an indicator of the image frequency characteristics associated with to the size of the structure, defined as follows: $$\:{CNR}_{\text{L}\text{O}}\left(\stackrel{-}{u}\right)=\:\frac{{ROI}_{\text{M}}-{ROI}_{\text{B}}}{\sqrt{NPS\left(\stackrel{-}{u}\right)}},\:\:\:\left(2\right)$$ where \(\:\stackrel{-}{u}\) represents the spatial frequency [cycles/mm] corresponding to the object size \(\:d\) [mm] and is calculated as the reciprocal of \(\:d\) . NPS represents the noise power spectrum, which is an indicator of noise characteristics. In this study, the NPS was derived using the radial frequency method [ 24 , 25 ], which involved projecting the two-dimensional spectra obtained from 128 × 128 pixel regions into a one dimensional format. The final NPS utilized in the CNR LO calculations was determined as the average of three separate measurements. \(\:{ROI}_{\text{M}}\) represents the mean digital value within the region of interest (ROI) placed on the cylindrical rod, and \(\:{ROI}_{\text{B}}\) represents the average digital value from the three background ROIs. For statistical comparison of CNR LO values, unpaired t-tests were conducted between the following conditions: “non” versus “C” and “non” versus “D or E.” Distance dependence of AEC response for clinical applications A one-shot phantom was utilized to assess the response characteristics of the AEC system at varying SID. The distances evaluated were 100, 120, and 150 cm. The tube voltage was maintained at 85 kV, with the mAs automatically regulated by the AEC system. Given the material composition and dimensions of the phantom, the AEC sensor was positioned at the center, designated as “C.” The EI and exposure time were recorded by calculating the CNR LO for each acquisition. For this analysis, cylindrical rods embedded at depths of 1.7, 2.4, 3.4, and 4.0 mm within the one-shot phantom, are shown in Fig. 4 (a). The mean pixel value within each rod was defined as \(\:{ROI}_{\text{M}}\) . To calculate \(\:{ROI}_{\text{B}}\) (mean background value) and \(\:NPS\left(\stackrel{-}{u}\right)\) , three 128 × 128 pixel regions were selected within the phantom, ensuring they did not overlap with the low-contrast rods. These measurements were used to compute CNR LO for each imaging distance. Dunn’s test, a nonparametric method for multiple group comparisons, was applied to evaluate the statistical significance of the CNR LO variation with imaging distance. X-ray incident angle dependence of AEC response for clinical applications In this section, we evaluated the response characteristics of the AEC system under varying X-ray incident angles on the FPD. The angles were systematically adjusted as follows: 2.5° and 5° along the longitudinal direction, and 3° and 6° along the lateral direction. The longitudinal direction corresponds to the vertical axis (top to bottom) as shown in Fig. 1 , whereas the lateral direction corresponds to the horizontal axis (left to right). These angles were achieved by tilting the FPD while maintaining a fixed X-ray tube. The overall geometric configuration, irradiation conditions, and selected AEC sensor configurations were consistent with those described in Section 2.4. The EI and exposure time were recorded during the acquisition of the one-shot phantom images. Furthermore, we calculated the system contrast transfer function (SCTF) using the resolution chart region of the phantom. As shown in Fig. 4 (b), ROIs were established on each frequency area of the chart to measure the mean digital value \(\:m\) and SD \(\:\sigma\:\) . The SCTF was computed using the SD-based method, using Equations (3) to (5): $$\:{M}_{0}=\frac{\sqrt{2}}{\pi\:}\left|{m}_{a}-{m}_{t}\right|,\:\:\:\:\:\left(3\right)$$ $$\:{\sigma\:}^{2}=\frac{\left({\sigma\:}_{a}^{2}+{\sigma\:}_{t}^{2}\right)}{2},\:\:\:\:\left(4\right)$$ $$\:SCTF\left(f\right)=\frac{\sqrt{{\sigma\:}_{f}^{2}-{\sigma\:}^{2}}}{{M}_{0}},\:\:\:\:\:\:\left(5\right)$$ where, \(\:{M}_{0}\) ​ represents the baseline contrast value calculated from \(\:{m}_{a}\) ​ (the mean digital value of the chart’s masked region) and \(\:{m}_{t}\) ​ (the mean value of the X-ray transparent region). The variance \(\:{\sigma\:}^{2}\) represents the baseline image noise, derived from the SDs \(\:{\sigma\:}_{a}\) ​ and \(\:{\sigma\:}_{t}\) ​ of the masked and transparent regions, respectively. The SCTF at frequency \(\:f\) , denoted as \(\:SCTF\left(f\right)\) , serves as an index reflecting the capability of the imaging system to preserve the contrast at that frequency. It was calculated using \(\:{\sigma\:}_{f}\) ​ (SD in the frequency region of the chart) and the baseline SD \(\:\sigma\:\) . Dunn’s test, a nonparametric method for multiple group comparisons, was applied to evaluate the statistical significance of the SCTF variation with incident angle. Each frequency component was analyzed. Results Fundamental characteristics (Object thickness dependence and tube voltage property) The variations in EI, mAs, and SdNR for different PMMA thicknesses are shown in Fig. 5 . Notably even with a consistent object thickness, EI varied depending on the applied tube voltage. The maximum mAs was recorded at a PMMA thickness of 15 cm and tube voltage of 60 kV. Overall, the SdNR decreased with increasing object thickness. To examine the characteristics related to the tube voltage, the changes in EI and mAs at a fixed PMMA thickness of 15 cm are shown in Fig. 6 . These data correspond to the subset of Fig. 5 with a PMMA thickness of 15 cm, displayed to showcase the response to tube voltage variation. As the tube voltage increased, both EI and mAs decreased. Notably, at 100 kV, the variation in EI depending on the AEC sensor selection was significant, whereas mAs remained consistent across the sensor configurations. Evaluation of AEC sensor selection response for clinical applications The EI and irradiation time for each AEC sensor configuration are shown in Fig. 7 . Notably, the use of AEC resulted in both lower EI and reduced exposure time compared with conditions without AEC. Specifically, when sensors D or E were selected, the EI decreased to 32% of the non-AEC value ( \(\:p\:=\:7\:\times\:\:{10}^{-10}<0.05\) ), whereas the exposure time decreased to 43% ( \(\:p\:=\:0.04<0.05\) ), indicating statistically significant differences. Regarding image quality, the CNR LO was measured at 70.4 without AEC, compared with 43.8 when utilizing sensors D or E, with a significant difference observed ( \(\:p\:=\:0.02<0.05\) ). Distance dependence of AEC response for clinical applications The variations in the EI and irradiation time when the SID is modified is shown in Fig. 8 . When the distance was increased from 100 to 150 cm, the EI increased by 1.1 times ( \(\:p=0.01<0.05\) ), whereas the irradiation time increased twofold ( \(\:p=0.01<0.05\) ). However, when 120 cm as utilized as the reference, no statistically significant differences in EI or irradiation time were observed between 100 and 150 cm. The results of CNR LO measurements using low-contrast rods in the one-shot phantom are shown in Fig. 9 . The CNR LO values decreased as the imaging distance increased, particularly for rods positioned at shallower depths. At a distance of 150 cm, a significant reduction in CNR LO was observed across all rod diameters ( \(\:p=0.04<0.05\) ). In contrast, no significant difference was observed between the 100 and 120 cm treatments. X-ray incident angle dependence of AEC response for clinical applications The variations in EI and exposure time resulting from variations in the X-ray incident angle to the FPD are shown in Fig. 10 (a). When compared with the baseline angle of 0°, a 5° tilt in the longitudinal direction resulted in a 1.1-fold increase in the EI ( \(\:p=0.01<0.05\) ) and a 1.06-fold increase in the irradiation time ( \(\:p=0.01<0.05\) ). Conversely, no statistically significant differences were observed in the lateral direction within the tested angular range. The variation in the SCTF as a function of the X-ray incident angle is shown in Fig. 10 (b). In the region of low spatial frequency (≤ 2.2 LP/mm), both longitudinal and lateral angle changes resulted in an approximate 10% decrease in SCTF, with the longitudinal direction demonstrating statistically significant differences ( \(\:p=0.03<0.05\) ). Furthermore, within this low-frequency range, an increasing in the angle along the longitudinal direction results in a more pronounced difference compared with that in the lateral direction ( \(\:p=0.02<0.05\) ). In contrast, in the high spatial frequency region (≥ 3.4 LP/mm), measurement variability increased, and no statistically significant differences were observed. Discussion Conventional AEC systems externally attached to FPDs operate by positioning a dose sensor either in front of or behind the detector. Once the measured dose reaches a predefined reference level, the sensor transmits a termination signal to the generator timer [ 26 ]. Conversely, the newly developed AEC function integrated within the FPD utilizes semiconductor sensors embedded in each pixel, facilitating similar control through wireless communication without external components. This study specifically focused on an AEC-integrated FPD, evaluating its fundamental performance and operational characteristics under conditions that simulate its clinical use, aiming to elucidate its role in dose optimization. Fundamental characteristics The X-ray system employed in this research has a nominal minimum irradiation time of 1 ms; however, no exposures shorter than this duration were recorded during the testing phase. Consequently, the fluctuations in the EI observed in Figs. 5 and 6 are not attributed to the switching latency of the FPD. Instead, these variations likely result from inadequate synchronization between the AEC system and mAs settings. Ideally, in AEC-controlled imaging, the dose delivered to the detector —reflected in the EI—should remain consistent, irrespective of variations (i.e., the EI) should remain constant, regardless of changes in other imaging conditions. According to IEC 61223-3-8, acceptance testing mandates users assume responsibility for validating imaging parameters upon the installation of equipment [ 27 ]. The significant variations in EI observed in response to changes in the tube voltage in this study underscore the significance of precise parameter calibration by the user at the time of acceptance testing. The IEC standard specifies that contrast adjustment should be based on conditions of a 15 cm PMMA thickness at 80 kV [ 27 ]. As shown in Fig. 5 , the EI values recorded at 100 and 120 kV were approximately 30% lower than this reference, indicating a substantial tube-voltage-dependent variation in the detector-incident dose. To maintain a consistent image quality through AEC, the finding that the SdNR at 60 kV surpassed that at the reference 80 kV suggests that AEC tuning may facilitate a reduction in radiation dose (lower mAs) under low-kV conditions. Conversely, under the same tube voltage, the EI demonstrated minimal dependence on the object thickness, indicating that the AEC system demonstrates reliable baseline performance. From an image quality perspective, the SdNR displayed the most significant variation across object thicknesses at 120 kV. This variation may reflect the sensitivity of the cesium iodide scintillator utilized in the FPD, which archives peak detection efficiency at an effective energy of approximately 36 keV. Consequently, to optimize the AEC system of this FPD effectively, careful adjustment of the mAs settings for each tube voltage is required. Evaluation of AEC sensor selection response for clinical applications As shown in Fig. 7 , use of AEC can reduce the EI by over 50% compared with imaging conducted without AEC. Previously, during the era of screen/film systems and computed radiography, which required development and reading processes, radiographers performing mobile X-ray imaging often faced the challenge of repeatedly traveling between hospital wards and the image reading rooms. To mitigate the effort and delays associated with retaking images owing to underexposure, examinations were frequently conducted using fixed exposure settings, irrespective of patient size. This practice hindered progress toward effective dose optimization. However, as demonstrated in this study, the application of AEC significantly reduced EI values and facilitated appropriate dose adjustment, contributing to a significant reduction in patient radiation exposure. A comparative analysis of the CNR LO between the AEC and non-AEC conditions revealed significant changes in image quality associated with reduced X-ray output. However, the baseline CNR LO observed without AEC does not represent the minimum threshold for lesion detection. Therefore, this difference did not indicate a clinically significant degradation in image quality or diagnostic capability. Furthermore, this study demonstrated that the EI varied depending on the selection of AEC sensors (Fig. 7 ). In the context of chest phantom imaging, the use of sensors “D or E,” which are positioned at the lower lung fields, resulted in the lowest EI. Conversely, sensor “C,” located centrally, yielded the highest EI. The positioning of sensor C near the spine and myocardium contrasts with placement of sensors D and E at the lower margins of the lung fields. This indicates that the location of the AEC sensor is influenced by the anatomical differences in X-ray attenuation. To minimize exposure dose, the sensor should be positioned in areas characterized by low attenuation, such as the peripheral lung fields represented by sensors D and E. However, given the variability in lung morphology among individuals [ 28 ], standardizing imaging conditions may be more effectively achieved utilizing a centrally located sensor like “C,” which is less susceptible to variations in patient anatomy. Ultimately, the selection of the sensor should consider the anatomical targets, anticipated workflow, and the need to balance dose reduction and clinical image quality. Further research is necessary to establish optimal sensor selection practices. While the IEC 60601-2-54 standard recommends the use of AEC for all radiographic procedures [ 12 ], its adoption in extremity and pediatric imaging remains limited. This hesitance can be attributed largely to concerns regarding potential overexposure when the anatomical region does not align with the AEC sensor, leading to inadequate control. For example, in hand imaging, patients typically position their hands directly on the FPD beside the radiographic table. Instead of forcing alignment with the central sensor “C,” selecting a sensor that corresponds to the actual anatomical location facilitates more comfortable patient positioning. Additionally, aligning the X-ray tube with the selected sensor can help minimize the degradation of spatial resolution that may occur owing to oblique X-ray incidence. Distance dependence of AEC response for clinical applications Changes in EI and exposure time were observed during evaluation of AEC response under varying SID. However, no statistically significant differences were detected when 120 cm was used as the reference. While precise distance measurement during bedside radiography poses challenges, practical deviations in SID should be within approximately ± 30 cm. Therefore, the influence of SID variability on AEC performance can be minimized by appropriately defining a reference distance in clinical protocols. In contrast, the results indicated a significant decrease in CNR LO at 150 cm. This decline appears to stem from increased image noise, attributed to non-uniform pixel values. Visual inspection of the images further revealed marked structural non-uniformity. As the imaging distance increases, the corresponding irradiation time increases, potentially prolonging the active duration of the detector and resulting in the emergence of electrical or structural noise. Previous studies analyzing the physical characteristics of similar detectors have indicated that the effects of structural noise become more pronounced at higher incident radiation doses [ 25 ]. In conjunction with our current findings, these objective observations suggest that imaging systems with built-in AEC may possess inherent structural limitations. Therefore, to ensure a proper AEC performance, imaging should be conducted under conditions that allow the detector to operate within its optimal operational range [ 29 ]. X-ray incident angle dependence of AEC response for clinical applications In the evaluation of the AEC response under varying X-ray incident angles, a significant increase in the EI was observed when the angle was tilted in the longitudinal direction. Additionally, the SCTF, an indicator of spatial resolution, decreased by approximately 10% in both the longitudinal and lateral directions when compared with the baseline measurement at 0°. This increase in EI is believed to stem from the angular dependence of the semiconductor-based sensor [ 30 ]. Specifically, oblique X-ray incidence diminished the effective detector volume exposed to radiation, necessitating longer exposure times and consequently resulting in an elevated EI. The smaller impact observed in the lateral direction, as opposed to that in the longitudinal direction may be attributed to the intrinsic structure of the X-ray detection panel. However, this hypothesis remains unverified in the present study. Furthermore, an investigation into the anode heel effect revealed no differences between the longitudinal and lateral directions. In bedside imaging, the FPD is typically positioned between the patient and bedding, necessitating independent adjustment of X-ray beam’s incident angle, separate from the orientation of the X-ray tube. Achieving perpendicular alignment between the FPD and incident beam presents particular challenges during seated examinations. Under these conditions, the integration of AEC with the FPD requires meticulous consideration of the effects of oblique incidence. Misalignment not only has the potential to disrupt AEC functionality but may also lead to degradation in spatial resolution owing to the relative positioning of anatomical structures. These observations underscore the critical need for heightened attention to angular alignment and tilt when utilizing AEC-enabled FPD systems, as compared with conventional systems. Furthermore, routine assessment of geometric misalignment or focal deviation is imperative in clinical practice [ 31 ]. Conclusion In this study, we investigated a novel FPD system equipped with built-in AEC functionality. Our comprehensive evaluation focused on its fundamental performance and operational characteristics under simulated clinical conditions. The assessment of the basic characteristics elucidated the behavior of the EI and SdNR in response to variations in the tube voltage and object thickness. Notably, we observed that sensitivity differences related to tube voltage significantly influenced exposure optimization. Under simulated clinical conditions scenarios, the implementation of the AEC resulted in a substantial reduction in EI, thereby affirming its role in effective dose management. Furthermore, we identified several factors that impact both AEC response and image quality, including the selection of the AEC sensor position, source-to-image distance, and X-ray incident angle. These objective findings indicate that, when utilized appropriately, the AEC function integrated into the new FPD system can enhance radiation dose reduction while stabilizing image quality across diverse imaging environments, including bedside radiography. However, environmental variations that may impact dose control and image characteristics should be considered, underscoring the necessity for practical adjustments in clinical operations. Further optimization of AEC applications tailored to individual patient body types and postures, coupled with a deeper understanding of the FPD structure and sensor sensitivity characteristics, is anticipated to advance the safety and quality of diagnostic imaging. Declarations Ethics approval This article does not contain any studies with human participants performed by any of the authors. Funding This research did not receive any specific grant from funding agencies. Competing Interests The detector and mobile X-ray device used in this study were loaned by CANON Medical Systems. Author Contributions All authors contributed to the study conception and design, as well as to the preparation of materials, data collection, and analysis. The first draft of the manuscript was written by Sho Maruyama, and all authors provided feedback on previous versions of the manuscript. All authors read and approved the final version of the manuscript.. References Ministry of Health, Labour and Welfare in Japan (2024) Hospital report (estimated figures for June 2024). https://www.mhlw.go.jp/toukei/saikin/hw/byouin/m24/06.html . Accessed 26 May 2025 International Commission on Radiological Protection (2007) The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. Ann ICRP 37:2–4 International Commission on Radiological Protection (2017) Diagnostic reference levels in medical imaging. ICRP Publication 135. Ann ICRP 46(1) Japan Network for Research and Information on Medical Exposure (2015) Diagnostic reference levels based on latest surveys in Japan-Japan DRLs 2015-. J-RIME Report Kanda R, Akahane M, Koba Y et al (2021) Developing diagnostic reference levels in Japan. 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IEC 60336:2020 Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Editorial decision: Minor revisions 29 Oct, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor invited by journal 03 Jul, 2025 Editor assigned by journal 11 Jun, 2025 First submitted to journal 10 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6867187","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513566728,"identity":"8b12e5f7-d1e8-4e33-b07e-5e941173f687","order_by":0,"name":"Sho 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1","display":"","copyAsset":false,"role":"figure","size":49777,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the FPD showing the location of AEC sensors labeled A to E (Provided by CANON MEDICAL SYSTEMS)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/f01061939d944faaf23c2113.png"},{"id":91837898,"identity":"ea4bc0f4-6287-4ae4-b661-dd7fc4bb6a29","added_by":"auto","created_at":"2025-09-22 09:41:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78755,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the experimental setup. The source-to-image distance (SID) was fixed at 100 cm for configuration (a) and varied at 100, 120, and 150 cm in configuration (b). A phantom was placed on the detector in both scenarios\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/f1bd611e1e54f6167ed4a648.png"},{"id":91835963,"identity":"b5c361d7-a19b-4b5c-83b9-154ab5aec6c3","added_by":"auto","created_at":"2025-09-22 09:33:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":238628,"visible":true,"origin":"","legend":"\u003cp\u003eRegion selection for low-contrast detectability analysis. The left panel presents a chest radiograph featuring the target rod utilized for the CNR\u003csub\u003eLO\u003c/sub\u003e evaluation. The second panel highlights the placement of the target rod (arrow), whereas the third panel delineates the background ROIs represents by solid circles. The rightmost panel specifies the size of the analysis ROI (128 × 128 pixels) employed for NPS calculation\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/5acf1008f905f2bcddcee0ef.png"},{"id":91835958,"identity":"d7a29e9d-3205-4556-94b5-c68257e50614","added_by":"auto","created_at":"2025-09-22 09:33:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":547879,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Overview of the one-shot phantom used to assess the response characteristics of the AEC at SIDs of 100, 120, and 150 cm. The low-contrast rods utilized for the CNR\u003csub\u003eLO\u003c/sub\u003e calculations are represented as magnified images. (b) Overview of the same phantom utilized to assess the response of the AEC under varying X-ray incidence angles to the FPD. The magnified image represents a view of the resolution chart, with ROIs defined for calculating the SCTF\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/97fe71531ce23fd31f26a4b0.png"},{"id":91838861,"identity":"f2063065-1ba6-4fc9-afca-dc48623399e5","added_by":"auto","created_at":"2025-09-22 09:49:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":206778,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of (a) EI, (b) mAs, and (c) SdNR as a function of PMMA thickness. Each line represents a different tube voltage condition (60, 80, 100, and 120 kV). The individual markers indicate the selected AEC sensor positions (sensors a–e). The shaded areas surrounding each line represent the 95% confidence intervals, illustrating the variability in AEC sensitivity across different sensor positions for each voltage condition\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/1e68a1a86d37e33579d128c7.png"},{"id":91837901,"identity":"db5d0473-0455-493a-a177-65191d741d87","added_by":"auto","created_at":"2025-09-22 09:41:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117848,"visible":true,"origin":"","legend":"\u003cp\u003eTube voltage dependence of (a) EI and (b) mAs at a PMMA thickness of 15 cm. The data presented here are derived from the 15 cm subset of the data shown in the preceding figure, highlighting the variability in AEC response as a function of tube voltage. The blue lines represent the mean values across the AEC sensors, whereas red markers represent individual sensor measurements. The shaded regions denote the 95% confidence intervals\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/1e9e9d20e62c661c760c05f1.png"},{"id":91842045,"identity":"57c12379-e372-45ec-9d0c-ae8c52ff5d8a","added_by":"auto","created_at":"2025-09-22 09:57:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":45766,"visible":true,"origin":"","legend":"\u003cp\u003eEI (blue bars) and irradiation time (red line, right axis) for each selected combination of AEC sensors. The “non” condition represents acquisition without AEC, whereas all other labels represent the specific combinations of active AEC sensors. Error bars represent the SD across repeated measurements, with the irradiation time plotted on the secondary vertical axis\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/eeb21d2cf2962983ce601c74.png"},{"id":91838862,"identity":"c8d5cf41-d3bc-4c6a-bcb2-1e3bc913ea61","added_by":"auto","created_at":"2025-09-22 09:49:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":30294,"visible":true,"origin":"","legend":"\u003cp\u003eEI (blue bars) and irradiation time (red line, right axis) as a function of the SID. The data reflect the changes in AEC response when the SID was set to 100, 120, and 150 cm. The error bars represent the SDs across repeated measurements\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/19118e9664b2b060db6bfe20.png"},{"id":91837919,"identity":"2ba00f55-9e08-4000-b740-3bd69947ba95","added_by":"auto","created_at":"2025-09-22 09:41:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":27350,"visible":true,"origin":"","legend":"\u003cp\u003eCNR\u003csub\u003eLO\u003c/sub\u003e measurements obtained using low-contrast rods. Each bar represents the mean CNR\u003csub\u003eLO\u003c/sub\u003e under different SID conditions, whereas error bars represent 2SD based on repeated measurements\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eX-ray incident angle dependence of AEC response for clinical applications\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/8e4aa63c7c6ca3b94431f02e.png"},{"id":91835983,"identity":"81b5fe07-2916-4ed3-b33c-421cefabee15","added_by":"auto","created_at":"2025-09-22 09:33:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":84769,"visible":true,"origin":"","legend":"\u003cp\u003e(a) EI (blue bars) and irradiation time (red line, right axis) under varying X-ray incidence angles to the FPD. The error bars represent the SD across repeated measurements. (b) SCTF curves under different X-ray incidence angles. The different colors represent short- and long-axis inclinations, whereas the error bars represent 2SD\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/3ee741e1421b39a460769ca2.png"},{"id":101690810,"identity":"705d5121-b9f7-4acb-b3b7-f37df10e41e9","added_by":"auto","created_at":"2026-02-02 16:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2293368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6867187/v1/1ddaceae-39d9-4918-b08b-c554798ed02d.pdf"}],"financialInterests":"","formattedTitle":"Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecently, the number of hospitalized patients in Japan has increased. According to a report by the Ministry of Health, Labor and Welfare, as of June 2024, the average daily number of inpatients across hospitals nationwide has reached approximately 1.12\u0026nbsp;million [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this context, driven by the dual imperatives of infection control and the needs of critically ill patients with mobility challenges, there has been an increase in the demand for bedside radiography performed using mobile X-ray systems.\u003c/p\u003e\u003cp\u003eIn diagnostic radiology, optimizing the patient exposure to radiation is recognized as a significant global concern. The International Commission on Radiological Protection (ICRP) introduced the concept of diagnostic reference levels (DRLs) in 1996 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], subsequently enhancing its application and evaluation in ICRP publication 135 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Japan Network for Research and Information on Medical Exposure (J-RIME) published DRLs in 2015 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and further refined these guidelines in 2020 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, revisions to the Medical Care Act in April 2023 mandated the implementation of DRLs in medical institutions of a certain scale [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA key technology associated with these optimization efforts is auto exposure control (AEC). This technology automatically adjusts the operation of the X-ray generator based on the patient\u0026rsquo;s body thickness and the anatomical region being imaged, thereby aiming to reduce radiation exposure while maintaining appropriate image quality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The ICRP positions AEC as a key measure for achieving appropriate dose management, ensuring that radiation exposure does not exceed DRLs [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, J-RIME advocated for the implementation of AEC in the 2020 DRLs to enhance dose optimization in medical settings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, conventional AEC systems are external components that are affixed to the detectors of fixed X-ray systems installed in radiography rooms, rendering their application impractical in other settings. Consequently, dose adjustments during imaging procedures in bedside or operating room context using mobile X-ray units have largely depended on the expertise and proficiency of the radiographer, presenting significant challenges.\u003c/p\u003e\u003cp\u003eTo address this issue, a novel flat panel detector (FPD) with integrated AEC functionality has been developed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This innovative system employs a semiconductor sensor within the detector to detect X-rays and automatically terminates X-ray generation upon reaching a predetermined optimal imaging dose. When utilized with mobile X-ray units, this technology facilitates AEC-based dose management even in bedside and operating room scenarios, providing a significant advantage over conventional external AEC systems.\u003c/p\u003e\u003cp\u003eDespite its potential, the AEC-equipped FPD is a relatively new technology, and reports on its characteristics and practical utilization are limited. Therefore, this study aims to experimentally evaluate the fundamental performance of the newly developed AEC-equipped FPD and assess its clinical utility, as well as the associated challenges. The objective of this investigation is to clarify the technology\u0026rsquo;s potential contribution to dose optimization in emerging imaging systems.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental equipment\u003c/h2\u003e\u003cp\u003eFor the FPD system with AEC functionality, we utilized the CXDI-720CW (CANON MEDICAL SYSTEMS, Tochigi, Japan). The FPD features a pixel size of 0.125 mm, an output image matrix size of 2800 \u0026times; 3408 pixels, and a grayscale depth of 12 bits. X-ray irradiation was conducted using a mobile X-ray imaging system, Mobirex i9 (SHIMADZU CORPORATION, Kyoto, Japan), with a total filtration of 2.5 mm aluminum. The FPD system is equipped with five sensor areas that serve as AEC sensors. These regions are labeled A (top left), B (top right), C (center), D (bottom left), and E (bottom right), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each sensor area is uniformly sized at 560 \u0026times; 560 pixels (7 cm \u0026times; 7 cm).\u003c/p\u003e\u003cp\u003eFor validation purposes, we employed several phantoms as imaging subjects, including polymethyl methacrylate (PMMA), chest phantom, and quality control phantom (1-shot phantom Primus A; IBA, Bayern, Germany). Image analysis was performed using ImageJ version 1.46r and Python version 3.9.12, with statistical analysis conducted using custom-written Python scripts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFundamental characteristics (Object thickness dependence and tube voltage property)\u003c/h3\u003e\n\u003cp\u003eThe IEC 60601-2-54:2022 standard defines the evaluation methods for AEC dose control under specified tube voltage and object thickness conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We assessed the basic characteristics of the system according to these guidelines. The geometrical arrangement shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) is employed, with the tube voltage set at 60, 80, 100, and 120 kV, while varying the PMMA thickness at 10, 15, and 20 cm to determine the exposure index (EI) [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and tube current-time product (mAs). The imaging system applied chest-specific image processing to all acquired images, as per design specifications. The output of the mobile X-ray unit was regulated by milliampere-seconds values that corresponded to the selected tube voltages [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The EI serves as a critical indicator of system sensitivity, reflecting the X-ray dose that reaches the FPD. Given that the transmitted X-ray dose incident on the FPD varies based on the characteristics of the object, the EI facilitates the evaluation of dose adequacy. Standardized under IEC 62494, EI allows for inter-system comparisons and provides a dose indication that is proportional to the incident X-ray quantity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo assess the dependence of radiation dose and image quality on object thickness, the signal-difference-to-noise ratio (SdNR) was calculated based on the pixel values in the central (1024 \u0026times; 1024 pixels) and peripheral areas of the PMMA images. SdNR is determined as follows [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SdNR=\\:\\frac{\\left|{I}_{o}-\\:{I}_{B}\\right|}{{SD}_{B}},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{o}\\)\u003c/span\u003e\u003c/span\u003e represents the mean digital value in the central area of the PMMA phantom, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{B}\\)\u003c/span\u003e\u003c/span\u003e represents the mean digital value in the peripheral (background) area, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SD}_{B}\\)\u003c/span\u003e\u003c/span\u003e represents the standard deviation (SD) in the background region. This methodology enabled a comprehensive assessment of the variation of SdNR with changes in object thickness.\u003c/p\u003e\u003cp\u003eAs per IEC guidelines, a phantom thickness of 15 cm was utilized for evaluating all tube voltage conditions. Therefore, to investigate the tube voltage characteristics, the PMMA thickness was maintained at 15 cm, whereas the tube voltage was varied across four levels (60, 80, 100, and 120 kV). The correlation among EI, mAs, and SdNR was subsequently evaluated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eEvaluation of AEC sensor selection response for clinical applications (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003eThe five AEC sensors integrated into the FPD can be configured using various combinations of the following modes: \u0026ldquo;non\u0026rdquo; (unused), \u0026ldquo;single\u0026rdquo; (individual), \u0026ldquo;or\u0026rdquo;, and \u0026ldquo;and.\u0026rdquo; To assess the variation in the AEC response characteristics based on the selected sensor(s), we measured the EI and exposure time while acquiring images of a chest phantom using the geometric setup shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a). In this validation step, the PMMA phantom was substituted with a chest phantom to approximate human anatomical structures more closely. The tube voltage was at 85 kV, a value commonly employed in clinical practice. Under non-AEC conditions, the exposure parameters were fixed at 160 mA and 160 ms. When the AEC was activated, the system automatically adjusted the mAs. The irradiation time was recorded, as, with a constant tube voltage and phantom thickness, the tube current remains fixed, allowing variations in exposure to be represented by time variations.\u003c/p\u003e\u003cp\u003eSubsequently, to examine the impact of sensor configuration on image quality, we selected two sensor combinations based on their contrasting response characteristics: one configuration that yielded the highest EI (\u0026ldquo;C\u0026rdquo; sensor alone) and another that produced the lowest EI (\u0026ldquo;D or E\u0026rdquo; sensors). The image quality under these two AEC settings was compared with that obtained without AEC. To facilitate the evaluation of image contrast, a cylindrical rod with a diameter of 1 cm was affixed to the chest phantom to facilitate the assessment of image contrast. The low-contrast object-specific contrast-to-noise ratio (CNR\u003csub\u003eLO\u003c/sub\u003e) was subsequently calculated [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). CNR\u003csub\u003eLO\u003c/sub\u003e serves as an indicator of the image frequency characteristics associated with to the size of the structure, defined as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{CNR}_{\\text{L}\\text{O}}\\left(\\stackrel{-}{u}\\right)=\\:\\frac{{ROI}_{\\text{M}}-{ROI}_{\\text{B}}}{\\sqrt{NPS\\left(\\stackrel{-}{u}\\right)}},\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{u}\\)\u003c/span\u003e\u003c/span\u003e represents the spatial frequency [cycles/mm] corresponding to the object size \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e\u003c/span\u003e [mm] and is calculated as the reciprocal of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e\u003c/span\u003e. NPS represents the noise power spectrum, which is an indicator of noise characteristics. In this study, the NPS was derived using the radial frequency method [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which involved projecting the two-dimensional spectra obtained from 128 \u0026times; 128 pixel regions into a one dimensional format. The final NPS utilized in the CNR\u003csub\u003eLO\u003c/sub\u003e calculations was determined as the average of three separate measurements. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ROI}_{\\text{M}}\\)\u003c/span\u003e\u003c/span\u003e represents the mean digital value within the region of interest (ROI) placed on the cylindrical rod, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ROI}_{\\text{B}}\\)\u003c/span\u003e\u003c/span\u003e represents the average digital value from the three background ROIs. For statistical comparison of CNR\u003csub\u003eLO\u003c/sub\u003e values, unpaired t-tests were conducted between the following conditions: \u0026ldquo;non\u0026rdquo; versus \u0026ldquo;C\u0026rdquo; and \u0026ldquo;non\u0026rdquo; versus \u0026ldquo;D or E.\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDistance dependence of AEC response for clinical applications\u003c/h3\u003e\n\u003cp\u003eA one-shot phantom was utilized to assess the response characteristics of the AEC system at varying SID. The distances evaluated were 100, 120, and 150 cm. The tube voltage was maintained at 85 kV, with the mAs automatically regulated by the AEC system. Given the material composition and dimensions of the phantom, the AEC sensor was positioned at the center, designated as \u0026ldquo;C.\u0026rdquo;\u003c/p\u003e\u003cp\u003eThe EI and exposure time were recorded by calculating the CNR\u003csub\u003eLO\u003c/sub\u003e for each acquisition. For this analysis, cylindrical rods embedded at depths of 1.7, 2.4, 3.4, and 4.0 mm within the one-shot phantom, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (a). The mean pixel value within each rod was defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ROI}_{\\text{M}}\\)\u003c/span\u003e\u003c/span\u003e. To calculate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ROI}_{\\text{B}}\\)\u003c/span\u003e\u003c/span\u003e (mean background value) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NPS\\left(\\stackrel{-}{u}\\right)\\)\u003c/span\u003e\u003c/span\u003e, three 128 \u0026times; 128 pixel regions were selected within the phantom, ensuring they did not overlap with the low-contrast rods. These measurements were used to compute CNR\u003csub\u003eLO\u003c/sub\u003e for each imaging distance. Dunn\u0026rsquo;s test, a nonparametric method for multiple group comparisons, was applied to evaluate the statistical significance of the CNR\u003csub\u003eLO\u003c/sub\u003e variation with imaging distance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eX-ray incident angle dependence of AEC response for clinical applications\u003c/h3\u003e\n\u003cp\u003eIn this section, we evaluated the response characteristics of the AEC system under varying X-ray incident angles on the FPD. The angles were systematically adjusted as follows: 2.5\u0026deg; and 5\u0026deg; along the longitudinal direction, and 3\u0026deg; and 6\u0026deg; along the lateral direction. The longitudinal direction corresponds to the vertical axis (top to bottom) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, whereas the lateral direction corresponds to the horizontal axis (left to right). These angles were achieved by tilting the FPD while maintaining a fixed X-ray tube. The overall geometric configuration, irradiation conditions, and selected AEC sensor configurations were consistent with those described in Section 2.4.\u003c/p\u003e\u003cp\u003eThe EI and exposure time were recorded during the acquisition of the one-shot phantom images. Furthermore, we calculated the system contrast transfer function (SCTF) using the resolution chart region of the phantom. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (b), ROIs were established on each frequency area of the chart to measure the mean digital value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\)\u003c/span\u003e\u003c/span\u003e and SD \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e. The SCTF was computed using the SD-based method, using Equations (3) to (5):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{M}_{0}=\\frac{\\sqrt{2}}{\\pi\\:}\\left|{m}_{a}-{m}_{t}\\right|,\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\sigma\\:}^{2}=\\frac{\\left({\\sigma\\:}_{a}^{2}+{\\sigma\\:}_{t}^{2}\\right)}{2},\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:SCTF\\left(f\\right)=\\frac{\\sqrt{{\\sigma\\:}_{f}^{2}-{\\sigma\\:}^{2}}}{{M}_{0}},\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{0}\\)\u003c/span\u003e\u003c/span\u003e​ represents the baseline contrast value calculated from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}_{a}\\)\u003c/span\u003e\u003c/span\u003e​ (the mean digital value of the chart\u0026rsquo;s masked region) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ (the mean value of the X-ray transparent region). The variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e represents the baseline image noise, derived from the SDs \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{a}\\)\u003c/span\u003e\u003c/span\u003e​ and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e​ of the masked and transparent regions, respectively. The SCTF at frequency \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\)\u003c/span\u003e\u003c/span\u003e, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SCTF\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e, serves as an index reflecting the capability of the imaging system to preserve the contrast at that frequency. It was calculated using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{f}\\)\u003c/span\u003e\u003c/span\u003e​ (SD in the frequency region of the chart) and the baseline SD \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e. Dunn\u0026rsquo;s test, a nonparametric method for multiple group comparisons, was applied to evaluate the statistical significance of the SCTF variation with incident angle. Each frequency component was analyzed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eFundamental characteristics (Object thickness dependence and tube voltage property)\u003c/h2\u003e\u003cp\u003eThe variations in EI, mAs, and SdNR for different PMMA thicknesses are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Notably even with a consistent object thickness, EI varied depending on the applied tube voltage. The maximum mAs was recorded at a PMMA thickness of 15 cm and tube voltage of 60 kV. Overall, the SdNR decreased with increasing object thickness.\u003c/p\u003e\u003cp\u003eTo examine the characteristics related to the tube voltage, the changes in EI and mAs at a fixed PMMA thickness of 15 cm are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. These data correspond to the subset of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e with a PMMA thickness of 15 cm, displayed to showcase the response to tube voltage variation. As the tube voltage increased, both EI and mAs decreased. Notably, at 100 kV, the variation in EI depending on the AEC sensor selection was significant, whereas mAs remained consistent across the sensor configurations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of AEC sensor selection response for clinical applications\u003c/h3\u003e\n\u003cp\u003eThe EI and irradiation time for each AEC sensor configuration are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Notably, the use of AEC resulted in both lower EI and reduced exposure time compared with conditions without AEC. Specifically, when sensors D or E were selected, the EI decreased to 32% of the non-AEC value (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:=\\:7\\:\\times\\:\\:{10}^{-10}\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e), whereas the exposure time decreased to 43% (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:=\\:0.04\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e), indicating statistically significant differences. Regarding image quality, the CNR\u003csub\u003eLO\u003c/sub\u003e was measured at 70.4 without AEC, compared with 43.8 when utilizing sensors D or E, with a significant difference observed (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:=\\:0.02\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDistance dependence of AEC response for clinical applications\u003c/h2\u003e\u003cp\u003eThe variations in the EI and irradiation time when the SID is modified is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. When the distance was increased from 100 to 150 cm, the EI increased by 1.1 times (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.01\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e), whereas the irradiation time increased twofold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.01\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). However, when 120 cm as utilized as the reference, no statistically significant differences in EI or irradiation time were observed between 100 and 150 cm.\u003c/p\u003e\u003cp\u003eThe results of CNR\u003csub\u003eLO\u003c/sub\u003e measurements using low-contrast rods in the one-shot phantom are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The CNR\u003csub\u003eLO\u003c/sub\u003e values decreased as the imaging distance increased, particularly for rods positioned at shallower depths. At a distance of 150 cm, a significant reduction in CNR\u003csub\u003eLO\u003c/sub\u003e was observed across all rod diameters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.04\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). In contrast, no significant difference was observed between the 100 and 120 cm treatments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eX-ray incident angle dependence of AEC response for clinical applications\u003c/h2\u003e\u003cp\u003eThe variations in EI and exposure time resulting from variations in the X-ray incident angle to the FPD are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(a). When compared with the baseline angle of 0\u0026deg;, a 5\u0026deg; tilt in the longitudinal direction resulted in a 1.1-fold increase in the EI (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.01\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e) and a 1.06-fold increase in the irradiation time (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.01\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). Conversely, no statistically significant differences were observed in the lateral direction within the tested angular range.\u003c/p\u003e\u003cp\u003eThe variation in the SCTF as a function of the X-ray incident angle is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e(b). In the region of low spatial frequency (\u0026le;\u0026thinsp;2.2 LP/mm), both longitudinal and lateral angle changes resulted in an approximate 10% decrease in SCTF, with the longitudinal direction demonstrating statistically significant differences (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.03\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). Furthermore, within this low-frequency range, an increasing in the angle along the longitudinal direction results in a more pronounced difference compared with that in the lateral direction (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.02\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). In contrast, in the high spatial frequency region (\u0026ge;\u0026thinsp;3.4 LP/mm), measurement variability increased, and no statistically significant differences were observed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConventional AEC systems externally attached to FPDs operate by positioning a dose sensor either in front of or behind the detector. Once the measured dose reaches a predefined reference level, the sensor transmits a termination signal to the generator timer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Conversely, the newly developed AEC function integrated within the FPD utilizes semiconductor sensors embedded in each pixel, facilitating similar control through wireless communication without external components. This study specifically focused on an AEC-integrated FPD, evaluating its fundamental performance and operational characteristics under conditions that simulate its clinical use, aiming to elucidate its role in dose optimization.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFundamental characteristics\u003c/h2\u003e\u003cp\u003eThe X-ray system employed in this research has a nominal minimum irradiation time of 1 ms; however, no exposures shorter than this duration were recorded during the testing phase. Consequently, the fluctuations in the EI observed in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e are not attributed to the switching latency of the FPD. Instead, these variations likely result from inadequate synchronization between the AEC system and mAs settings. Ideally, in AEC-controlled imaging, the dose delivered to the detector \u0026mdash;reflected in the EI\u0026mdash;should remain consistent, irrespective of variations (i.e., the EI) should remain constant, regardless of changes in other imaging conditions. According to IEC 61223-3-8, acceptance testing mandates users assume responsibility for validating imaging parameters upon the installation of equipment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The significant variations in EI observed in response to changes in the tube voltage in this study underscore the significance of precise parameter calibration by the user at the time of acceptance testing.\u003c/p\u003e\u003cp\u003eThe IEC standard specifies that contrast adjustment should be based on conditions of a 15 cm PMMA thickness at 80 kV [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the EI values recorded at 100 and 120 kV were approximately 30% lower than this reference, indicating a substantial tube-voltage-dependent variation in the detector-incident dose. To maintain a consistent image quality through AEC, the finding that the SdNR at 60 kV surpassed that at the reference 80 kV suggests that AEC tuning may facilitate a reduction in radiation dose (lower mAs) under low-kV conditions.\u003c/p\u003e\u003cp\u003eConversely, under the same tube voltage, the EI demonstrated minimal dependence on the object thickness, indicating that the AEC system demonstrates reliable baseline performance. From an image quality perspective, the SdNR displayed the most significant variation across object thicknesses at 120 kV. This variation may reflect the sensitivity of the cesium iodide scintillator utilized in the FPD, which archives peak detection efficiency at an effective energy of approximately 36 keV. Consequently, to optimize the AEC system of this FPD effectively, careful adjustment of the mAs settings for each tube voltage is required.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of AEC sensor selection response for clinical applications\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, use of AEC can reduce the EI by over 50% compared with imaging conducted without AEC. Previously, during the era of screen/film systems and computed radiography, which required development and reading processes, radiographers performing mobile X-ray imaging often faced the challenge of repeatedly traveling between hospital wards and the image reading rooms. To mitigate the effort and delays associated with retaking images owing to underexposure, examinations were frequently conducted using fixed exposure settings, irrespective of patient size. This practice hindered progress toward effective dose optimization. However, as demonstrated in this study, the application of AEC significantly reduced EI values and facilitated appropriate dose adjustment, contributing to a significant reduction in patient radiation exposure. A comparative analysis of the CNR\u003csub\u003eLO\u003c/sub\u003e between the AEC and non-AEC conditions revealed significant changes in image quality associated with reduced X-ray output. However, the baseline CNR\u003csub\u003eLO\u003c/sub\u003e observed without AEC does not represent the minimum threshold for lesion detection. Therefore, this difference did not indicate a clinically significant degradation in image quality or diagnostic capability.\u003c/p\u003e\u003cp\u003eFurthermore, this study demonstrated that the EI varied depending on the selection of AEC sensors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the context of chest phantom imaging, the use of sensors \u0026ldquo;D or E,\u0026rdquo; which are positioned at the lower lung fields, resulted in the lowest EI. Conversely, sensor \u0026ldquo;C,\u0026rdquo; located centrally, yielded the highest EI. The positioning of sensor C near the spine and myocardium contrasts with placement of sensors D and E at the lower margins of the lung fields. This indicates that the location of the AEC sensor is influenced by the anatomical differences in X-ray attenuation. To minimize exposure dose, the sensor should be positioned in areas characterized by low attenuation, such as the peripheral lung fields represented by sensors D and E. However, given the variability in lung morphology among individuals [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], standardizing imaging conditions may be more effectively achieved utilizing a centrally located sensor like \u0026ldquo;C,\u0026rdquo; which is less susceptible to variations in patient anatomy. Ultimately, the selection of the sensor should consider the anatomical targets, anticipated workflow, and the need to balance dose reduction and clinical image quality. Further research is necessary to establish optimal sensor selection practices.\u003c/p\u003e\u003cp\u003eWhile the IEC 60601-2-54 standard recommends the use of AEC for all radiographic procedures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], its adoption in extremity and pediatric imaging remains limited. This hesitance can be attributed largely to concerns regarding potential overexposure when the anatomical region does not align with the AEC sensor, leading to inadequate control. For example, in hand imaging, patients typically position their hands directly on the FPD beside the radiographic table. Instead of forcing alignment with the central sensor \u0026ldquo;C,\u0026rdquo; selecting a sensor that corresponds to the actual anatomical location facilitates more comfortable patient positioning. Additionally, aligning the X-ray tube with the selected sensor can help minimize the degradation of spatial resolution that may occur owing to oblique X-ray incidence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDistance dependence of AEC response for clinical applications\u003c/h2\u003e\u003cp\u003eChanges in EI and exposure time were observed during evaluation of AEC response under varying SID. However, no statistically significant differences were detected when 120 cm was used as the reference. While precise distance measurement during bedside radiography poses challenges, practical deviations in SID should be within approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;30 cm. Therefore, the influence of SID variability on AEC performance can be minimized by appropriately defining a reference distance in clinical protocols.\u003c/p\u003e\u003cp\u003eIn contrast, the results indicated a significant decrease in CNR\u003csub\u003eLO\u003c/sub\u003e at 150 cm. This decline appears to stem from increased image noise, attributed to non-uniform pixel values. Visual inspection of the images further revealed marked structural non-uniformity. As the imaging distance increases, the corresponding irradiation time increases, potentially prolonging the active duration of the detector and resulting in the emergence of electrical or structural noise. Previous studies analyzing the physical characteristics of similar detectors have indicated that the effects of structural noise become more pronounced at higher incident radiation doses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In conjunction with our current findings, these objective observations suggest that imaging systems with built-in AEC may possess inherent structural limitations. Therefore, to ensure a proper AEC performance, imaging should be conducted under conditions that allow the detector to operate within its optimal operational range [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eX-ray incident angle dependence of AEC response for clinical applications\u003c/h2\u003e\u003cp\u003eIn the evaluation of the AEC response under varying X-ray incident angles, a significant increase in the EI was observed when the angle was tilted in the longitudinal direction. Additionally, the SCTF, an indicator of spatial resolution, decreased by approximately 10% in both the longitudinal and lateral directions when compared with the baseline measurement at 0\u0026deg;. This increase in EI is believed to stem from the angular dependence of the semiconductor-based sensor [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Specifically, oblique X-ray incidence diminished the effective detector volume exposed to radiation, necessitating longer exposure times and consequently resulting in an elevated EI. The smaller impact observed in the lateral direction, as opposed to that in the longitudinal direction may be attributed to the intrinsic structure of the X-ray detection panel. However, this hypothesis remains unverified in the present study. Furthermore, an investigation into the anode heel effect revealed no differences between the longitudinal and lateral directions.\u003c/p\u003e\u003cp\u003eIn bedside imaging, the FPD is typically positioned between the patient and bedding, necessitating independent adjustment of X-ray beam\u0026rsquo;s incident angle, separate from the orientation of the X-ray tube. Achieving perpendicular alignment between the FPD and incident beam presents particular challenges during seated examinations. Under these conditions, the integration of AEC with the FPD requires meticulous consideration of the effects of oblique incidence. Misalignment not only has the potential to disrupt AEC functionality but may also lead to degradation in spatial resolution owing to the relative positioning of anatomical structures. These observations underscore the critical need for heightened attention to angular alignment and tilt when utilizing AEC-enabled FPD systems, as compared with conventional systems. Furthermore, routine assessment of geometric misalignment or focal deviation is imperative in clinical practice [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we investigated a novel FPD system equipped with built-in AEC functionality. Our comprehensive evaluation focused on its fundamental performance and operational characteristics under simulated clinical conditions. The assessment of the basic characteristics elucidated the behavior of the EI and SdNR in response to variations in the tube voltage and object thickness. Notably, we observed that sensitivity differences related to tube voltage significantly influenced exposure optimization. Under simulated clinical conditions scenarios, the implementation of the AEC resulted in a substantial reduction in EI, thereby affirming its role in effective dose management. Furthermore, we identified several factors that impact both AEC response and image quality, including the selection of the AEC sensor position, source-to-image distance, and X-ray incident angle.\u003c/p\u003e\u003cp\u003eThese objective findings indicate that, when utilized appropriately, the AEC function integrated into the new FPD system can enhance radiation dose reduction while stabilizing image quality across diverse imaging environments, including bedside radiography. However, environmental variations that may impact dose control and image characteristics should be considered, underscoring the necessity for practical adjustments in clinical operations. Further optimization of AEC applications tailored to individual patient body types and postures, coupled with a deeper understanding of the FPD structure and sensor sensitivity characteristics, is anticipated to advance the safety and quality of diagnostic imaging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies.\u003c/p\u003e\u003cp\u003eCompeting Interests\u003c/p\u003e\u003cp\u003eThe detector and mobile X-ray device used in this study were loaned by CANON Medical Systems.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design, as well as to the preparation of materials, data collection, and analysis. The first draft of the manuscript was written by Sho Maruyama, and all authors provided feedback on previous versions of the manuscript. All authors read and approved the final version of the manuscript..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMinistry of Health, Labour and Welfare in Japan (2024) Hospital report (estimated figures for June 2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mhlw.go.jp/toukei/saikin/hw/byouin/m24/06.html\u003c/span\u003e\u003cspan address=\"https://www.mhlw.go.jp/toukei/saikin/hw/byouin/m24/06.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 26 May 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Commission on Radiological Protection (2007) The 2007 Recommendations of the International Commission on Radiological Protection. ICRP Publication 103. 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IEC 60336:2020\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"auto exposure control, flat panel detector, bedside radiography, diagnostic reference levels, optimization","lastPublishedDoi":"10.21203/rs.3.rs-6867187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6867187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe demand for bedside radiography has been increasing due to critical clinical needs, including infection control and the limited mobility of severely ill patients. However, radiation dose adjustment in such settings has relied heavily 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 along with potential limitations.\u003c/p\u003e\u003cp\u003eThe dependency of 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. The effects of source-to-image distance and oblique X-ray incidence on AEC performance were also evaluated using a quality-control test device.\u003c/p\u003e\u003cp\u003eOur 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 AEC system significantly reduced EI, confirming its potential for effective dose management. Multiple factors were identified that influence both AEC response and image quality, such as sensor positioning, imaging distance, and beam angle.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that the AEC-equipped FPD system maintains consistent image quality while effectively reducing radiation dose across diverse imaging situations. Nevertheless, our results also underscore the importance of accounting for environmental variables that affect dose control and image characteristics, highlighting the need for practical adjustment in routine clinical operation.\u003c/p\u003e","manuscriptTitle":"Fundamental performance and clinical usefulness of a new AEC-equipped flat panel detector for dose optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:33:43","doi":"10.21203/rs.3.rs-6867187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-10-29T22:33:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-15T03:24:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T09:38:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2025-07-03T08:21:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-11T09:48:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2025-06-10T22:41:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30643eb9-3e26-4e1d-8f75-d7e3b3cfbb08","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-6867187","link":"https://doi.org/10.1007/s13246-026-01705-7","journal":{"identity":"physical-and-engineering-sciences-in-medicine","isVorOnly":false,"title":"Physical and Engineering Sciences in Medicine"},"publishedOn":"2026-01-27 15:58:31","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-09-22 09:33:43","video":"","vorDoi":"10.1007/s13246-026-01705-7","vorDoiUrl":"https://doi.org/10.1007/s13246-026-01705-7","workflowStages":[]},"version":"v1","identity":"rs-6867187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6867187","identity":"rs-6867187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}