The space radiation environment outside the protection of the Earth’s magnetosphere is severe and difficult to shield against. The cumulative effective dose to astronauts on a typical Mars mission would likely introduce risk exceeding permissible limits for carcinogenesis without innovative strategies for radiation shielding. Damaging cardiovascular and central nervous system effects are also expected in these space environments. There are many potential options for advanced shielding and risk mitigation, but magnetic shielding using superconductors offers several distinct advantages including using the conditions in space to help maintain the superconductor’s critical temperature and lower mass compared to equivalent passive shielding materials. Despite these advantages, the development of magnetic shielding technology has remained primarily in conceptual stages since the introduction of the idea in 1961. Over the last several decades, magnetic shielding has experienced periods of high and low attention by the human spaceflight community, leading to computational tools with single-use or other limitations and a non-uniform distribution of publications on the topic over time. Within the context of technology development and the surrounding space policy environment, this paper reviews and summarizes the available literature on the application of active magnetic shielding for space radiation protection, identifies challenges, and highlights areas for future research.

Background:Reliable estimates of radiation dose to bone marrow are critical to understanding the risk of radiation-induced cancers. Although the medical internal radiation dose phantom is routinely used for dose estimation, bone marrow is not defined in the phantom. Consequently, methods of indirectly estimating bone marrow dose have been implemented based on dose to surrogate volumes or average dose to soft tissue. Methods:In this study, new bone marrow structures were implemented and evaluated to the medical internal radiation dose phantom in geant4, offering improved fidelity. The dose equivalent to the bone marrow was calculated across medical, occupational, and space radiation exposure scenarios, and compared with results using prior indirect estimation methods. Conclusion:Our results show that bone marrow dose may be overestimated by up to a factor of three when using the traditional methods when compared with the improved fidelity medical internal radiation dose method, specifically at clinical x-ray energies.

Reject rate analysis is considered an integral part of a diagnostic radiography quality control (QC) program. A rejected image is a patient radiograph that was not presented to a radiologist for diagnosis and that contributes unnecessary radiation dose to the patient. Reject rates that are either too high or too low may suggest systemic department shortcomings in QC mechanisms. Due to the lack of standardization, reject data often cannot be easily compared between radiography systems from different vendors. The purpose of this report is to provide guidance to help standardize data elements that are required for comprehensive reject analysis and to propose data reporting and workflows to enable an effective and comprehensive reject rate monitoring program. Essential data elements, a proposed schema for classifying reject reasons, and workflow implementation options are recommended in this task group report.

Beginning with the advent of digital radiography systems in 1981, manufacturers of these systems provided indicators of detector exposure. These indicators were manufacturer-specific, and users in facilities with equipment from multiple manufacturers found it a challenge to monitor and manage variations in indicated exposure in routine clinical use. In 2008, a common definition of exposure index (EI) was realized in International Electrotechnical Commission (IEC) International Standard 62494-1 Ed. 1, which also introduced and defined the deviation index (DI), a number quantifying the difference between the detector EI for a given radiograph and the target exposure index (EIT ). An exposure index that differed by a constant from that established by the IEC and the concept of the deviation index also appear in American Association of Physicists in Medicine (AAPM) Report No. 116 published in 2009. The AAPM Report No. 116 went beyond the IEC standard in supplying a table (Table II in the report of TG-116) titled "Exposure Indicator DI Control Limits for Clinical Images," which listed suggested DI ranges and actions to be considered for each range. As the IEC EI was implemented and clinical DI data were gathered, concerns were voiced that the DI control limits published in the report of TG-116 were too strict and did not accurately reflect clinical practice. The charge of task group 232 (TG-232) and the objective of this final report was to investigate the current state of the practice for CR/DR Exposure and Deviation Indices based on AAPM TG 116 and IEC-62494, for the purpose of establishing achievable goals (reference levels) and action levels in digital radiography. Data corresponding to EI and DI were collected from a range of practice settings for a number of body parts and views (adults and pediatric radiographs) and analyzed in aggregate and separately. A subset of radiographs was also evaluated by radiologists based on criteria adapted from the European Guidelines on Quality Criteria for Diagnostic Radiographic Images from the European Commission. Analysis revealed that typical DI distribution was characterized by a standard deviation (SD) of 1.3-3.6 with mean DI values substantially different from 0.0, and less than 50% of DI values fell within the significant action limits proposed by AAPM TG-116 (-1.0 ≤ DI ≤ 1.0). Recommendations stemming from this analysis include targeting a mean DI value of 0.0 and action limits at ±1 and ±2 SD of the DI based on actual DI data of an individual site. EIT values, DI values, and associated action limits should be reviewed on an ongoing basis and optimization of DI values should be a process of continuous quality improvement with a goal of reducing practice variation.

PURPOSE:The study illustrates how a renewed approach to medical physics, Medical Physics 3.0 (MP3.0), can identify performance decrement of digital radiography (DR) systems when conventional Medical Physics 1.0 (MP1.0) methods fail. METHODS:MP1.0 tests included traditional annual tests plus the manufacturer's automated Quality Assurance Procedures (QAP) of a DR system before and after a radiologist's image quality (IQ) complaint repeated after service intervention. Further analysis was conducted using nontraditional MP3.0 tests including longitudinal review of QAP results from a 15-yr database, exposure-dependent signal-to-noise (SNR2 ), clinical IQ, and correlation with the institutional service database. Clinical images were analyzed in terms of IQ metrics by the Duke University Clinical Imaging Physics Group using previously validated software. RESULTS:Traditional metrics did not indicate discrepant system performance at any time. QAP reported a decrease in contrast-to-noise ratio (CNR) after detector replacement, but remained above the manufacturer's action limit. Clinical images showed increased lung noise (Ln), mediastinum noise (Mn), and subdiaphragm-lung contrast (SLc), and decreased lung gray level (Lgl) following detector replacement. After detector recalibration, QAP CNR improved, but did not return to previous levels. Lgl and SLc no longer significantly differed from before detector recalibration; however, Ln and Mn remained significantly different. Exposure-dependent SNR2 documented the detector operating within acceptable limits 9 yr previously but subsequently becoming miscalibrated sometime before four prior annual tests. Service records revealed catastrophic failure of the computer containing the original detector calibration from 11 yr prior. It is likely that the incorrect calibration backup file was uploaded at that time. CONCLUSIONS:MP1.0 tests failed to detect substandard system performance, but MP3.0 methods determined the root cause of the problem. MP3.0 exploits the wealth of data with more sensitive performance indicators. Data analytics are powerful tools whose proper application could facilitate early intervention in degraded system performance.

PURPOSE:The purpose of this study was to determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in a clinical setting unique from the one where the metrics were developed. METHODS:Seventeen gridless AP chest radiographs from a GE Optima portable digital radiography (DR) unit ("sub-standard" images; Group 2) and 17 digital PA chest radiographs ("standard-of-care" images; Group 1) and 15 gridless (non-routine) PA chest radiographs (images with a gross technical error; Group 3) from a Discovery DR unit were chosen for analysis. Group 2 images were acquired with a lower kVp (100 vs 125) and shorter source-to-image distance (127 cm vs 183 cm) and were expected to have lower quality than Group 1 images. Group 3 images were expected to have degraded contrast vs Group 1 images. Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software described and validated previously. Individual image quality was reported in terms of lung gray level, lung detail, lung noise, rib-lung contrast, rib sharpness, mediastinum detail, mediastinum noise, mediastinum alignment, subdiaphragm-lung contrast, and subdiaphragm area. Metrics were compared across groups. To improve precision of means and confidence intervals for routine exams, an additional 66 PA images were acquired, processed, and pooled with Group 1. Three observer studies were conducted to assess whether humans were able to identify images classified by the algorithm as abnormal. RESULTS:Metrics agreed with published Quality Consistency Ranges with three exceptions: higher lung gray level, lower rib-lung contrast, and lower subdiaphragm-lung contrast. Higher (stored) bit depth (14 vs 12) accounted for higher lung gray level values in our images. Values were most internally consistent for Group 1. The most sensitive metric for distinguishing between groups was mediastinum noise, followed closely by lung noise. The least sensitive metrics were mediastinum detail and rib-lung contrast. The algorithm was more sensitive than human observers at detecting suboptimal diagnostic quality images. CONCLUSIONS:The software appears promising for objectively and automatically identifying suboptimal images in a clinical imaging operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.

PURPOSE To characterize the distribution of the deviation index (DI) in digital radiography practices across the United States. METHODS DI data was obtained from 10 collaborating institutions in the United States between 2012 and 2015. Each institution complied with the requirements of the Institutional Review Board at their site. DI data from radiographs of the body parts chest, abdomen, pelvis and extremity were analyzed for anteroposterior, posteroanterior, lateral, and decubitus views. The DI data was analyzed both in aggregate and stratified by exposure control method, image receptor technology, patient age, and participating site for each body part and view. The number of exposures with DI falling within previously published control limits for DI and descriptive statistics were calculated. RESULTS DI data from 505,930 radiographic exposures was analyzed. The number of exposures with DI falling within published control limits for DI varied from 10 to 20% for adult patients and 10 to 23% for pediatric patients for different body parts and views. Mean DI values averaged over other parameters for radiographs of the abdomen, chest, pelvis, and extremities ranged from 0.3 to 1.0, -0.6 to 0.5, 0.8, and -0.9 to 0.5 for the different adult views and ranged from -1.6 to -0.1, -0.3 to 0.5, -0.1, -0.2 to 1.4 for the different pediatric views, respectively (DI data was solicited only for anteroposterior view of pelvis). Standard deviation values of DI from individual sites ranged from 1.3 to 3.6 and 1.3 to 3.0 for the different adult and pediatric views, respectively. Also of interest was that target exposure indicators varied by up to a factor of 6 between sites for certain body parts and views. CONCLUSION Previously published DI control limits do not reflect the state of clinical practice in digital radiography. Mean DI and target exposure indicators are targets for quality improvement efforts in radiography.

Purpose: To illustrate how performance analytics can identify performance decrement in digital radiography systems. Methods: Subsequent to a radiologist's image quality complaint, four different advanced methods contributed to root cause analysis. Our system was a GE Revolution XQi digital radiography unit. Initially, we reviewed weekly GE Quality Assurance Procedures (QAP) results in a database dating from 2001. Next, we evaluated objective image quality metrics of individual PA Chest radiographs acquired. These images were anonymized, securely transferred, and analyzed by the Duke University Clinical Imaging Physics Group with software previously described1 and validated2. Third, we compared the exposure-dependent SNR2 (NEQ) of the unit with previously established confidence limits3. Finally, we explored our service database to reveal events that might affect detector performance. Results: QAP reported a decrease in CNR reflected in a significant increase in lung noise(Ln), mediastinum noise(Mn), and subdiaphragm-lung contrast(Slc) with a significant decrease in lung grey level(Lgl) after detector replacement. Most change occurred during week 1, before the QAP indicated one-half the ultimate decrease in CNR. After detector recalibration, QAP CNR improved, but was not restored to previous levels. Lgl and Slc were no longer significantly different from before, however Ln and Mn remained significantly different. Exposure-dependent SNR2 show the detector to be operating within limits in October 2006 but subsequently became miscalibrated sometime before acquisition of the 2011–2014 data. Service records revealed catastrophic failure of the Image Detection Controller that contained the 2007 calibration. Traditional metrics did not indicate that the system was performing outside of normal limits. Conclusion: Performance analytics are powerful tools whose proper application could allow early intervention in degraded system performance. The image-quality metrics appear to be highly sensitive to system performance and are reported with every acquisition rather than at arbitrary intervals. Confidence intervals may require customization for individual systems or detectors.

The purpose of this study was to evaluate several of the standardized image quality metrics proposed by the American Association of Physics in Medicine (AAPM) Task Group 150. The task group suggested region-of-interest (ROI)-based techniques to measure nonuniformity, minimum signal-to-noise ratio (SNR), number of anomalous pixels, and modulation transfer function (MTF). This study evaluated the effects of ROI size and layout on the image metrics by using four different ROI sets, assessed result uncertainty by repeating measurements, and compared results with two commercially available quality control tools, namely the Carestream DIRECTVIEW Total Quality Tool (TQT) and the GE Healthcare Quality Assurance Process (QAP). Seven Carestream DRX-1C (CsI) detectors on mobile DR systems and four GE FlashPad detectors in radiographic rooms were tested. Images were analyzed using MATLAB software that had been previously validated and reported. Our values for signal and SNR nonuniformity and MTF agree with values published by other investigators. Our results show that ROI size affects nonuniformity and minimum SNR measurements, but not detection of anomalous pixels. Exposure geometry affects all tested image metrics except for the MTF. TG-150 metrics in general agree with the TQT, but agree with the QAP only for local and global signal nonuniformity. The difference in SNR nonuniformity and MTF values between the TG-150 and QAP may be explained by differences in the calculation of noise and acquisition beam quality, respectively. TG-150's SNR nonuniformity metrics are also more sensitive to detector nonuniformity compared to the QAP. Our results suggest that fixed ROI size should be used for consistency because nonuniformity metrics depend on ROI size. Ideally, detector tests should be performed at the exact calibration position. If not feasible, a baseline should be established from the mean of several repeated measurements. Our study indicates that the TG-150 tests can be used as an independent standardized procedure for detector performance assessment.

Purpose:To determine the consistency of digital detector (DR) tests recommended by AAPM TG150 and tests provided by commercially available DirectView Total Quality Tool (TQT).Methods:The DR tests recommended by the TG150 Detector Subgroup[1] were performed on 4 new Carestream DRX‐Revolution and one Carestream DRX1C retrofit of a GE AMX‐4 that had been in service for three years. After detector calibration, flat‐field images plus images of two bar patterns oriented parallel and perpendicular to the A‐C axis, were acquired at conditions recommended by TG150. Raw images were harvested and then analyzed using a MATLAB software previously validated[2,3,4]. Data were analyzed using ROIs of two different dimensions: 1) 128 × 128 ROIs matching the detector electronics; and 2) 256 × 256 ROIs, each including 4 adjacent smaller ROIs. TG150 metrics from 128 × 128 ROIs were compared to TQT metrics, which are also obtained from 128 × 128 ROIs[5].Results:The results show that both TG150 and TQT measurements were consistent among these detectors. Differences between TG150 and TQT values appear systematic. Compared with 128 × 128 ROIs, noise and SNR non‐uniformity were lower with 256 × 256 ROIs, although signal non‐uniformity was similar, indicating detectors were appropriately calibrated for gain and offset. MTF of the retrofit unit remained essentially the same between 2012 and 2015, but was inferior to the new units. The older generator focal spot is smaller (0.75mm vs. 1.2mm), and the SID for acquisition is 182cm as well, so focal spot dimensions cannot explain the difference. The difference in MTF may be secondary to differences in generator X‐ray spectrum or by unannounced changes in detector architecture. Further investigation is needed.Conclusion:The study shows that both TG150 and TQT tests are consistent. The numerical value of some metrics are dependent on ROI size.

Purpose: To determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in our clinical operation. Methods: Seventeen gridless AP Chest radiographs from a GE Optima portable digital radiography (DR) unit (Group 1), seventeen (routine) PA Chest radiographs from a GE Discovery DR unit (Group 2), and sixteen gridless (non‐routine) PA Chest radiographs from the same Discovery DR unit (Group 3) were chosen for analysis. Groups were selected to represent “sub‐standard” (Group 1), “standard‐of‐care” (Group 2), and images with a gross technical error (Group 3). Group 1 images were acquired with lower kVp (90 vs. 125), shorter source‐to‐image distance (127cm vs 183cm) and were expected to have lower quality than images in Group 2. Group 3 was expected to have degraded contrast versus Group 2.This evaluation was approved by the institutional Quality Improvement Assurance Board (QIAB). Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software previously described 1 and validated 2 . Image quality for individual images was reported in terms of lung grey level(Lgl); lung noise(Ln); rib‐lung contrast(RLc); rib sharpness(Rs); mediastinum detail(Md), noise(Mn), and alignment(Ma); subdiaphragm‐lung contrast(SLc); and subdiaphragm area(Sa). Metrics were compared across groups. Results: Metrics agreed with published Quality Consistency Ranges with three exceptions: higher Lgl, lower RLc, and SDc. Higher bit depth (16 vs 12) accounted for higher Lgl values in our images. Values were most internally consistent for Group 2. The most sensitive metric for distinguishing between groups was Mn followed closely by Ln. The least sensitive metrics were Md and RLc. Conclusion: The software appears promising for objectively and automatically identifying substandard images in our operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.