The effect of non-immersive virtual reality radiographic positioning simulation on first-year radiography students' image evaluation performance

INTRODUCTION

Optimal radiographic image quality is critical because it affects the accuracy of the reporter's interpretation. Radiographers have an ethical obligation to obtain quality diagnostic images while protecting patients from unnecessary radiation, including minimizing rejected and repeated images. Repeated imaging due to positioning errors have increased in recent years.

METHODS

This study evaluated the effectiveness of non-immersive virtual reality (VR) simulation on first-year students' evaluation of positioning errors on resultant knee and lumbar spine images. Crossover intervention design was used to deliver radiographic image evaluation instruction through traditional lecture and guided simulation using non-immersive VR to 33 first-year radiography students at a single academic institution located across four geographic program locations. Pre- and post-test knowledge assessments examined participants' ability to recognize positioning errors on multiple-choice and essay questions.

RESULTS

Raw mean scores increased on multiple choice questions across the entire cohort for the knee (M = 0.82, SD = 3.38) and lumbar spine (M = 2.91, SD = 3.69) but there was no significant difference in performance by instructional method (p = 0.60). Essay questions reported very minimal to no raw mean score increases for the knee (M = 0.27, SD = 2.78) and lumbar spine (M = 0.00, SD = 2.55), with no significant difference in performance by instructional method (p = 0.72).

CONCLUSION

Guided simulation instruction was shown to be as effective as traditional lecture. Results also suggest that novice learners better recognize image evaluation errors and corrections from a list of options but have not yet achieved the level of competence needed to independently evaluate radiographic images for diagnostic criteria.

IMPLICATIONS FOR PRACTICE

Non-immersive VR simulation is an effective tool for image evaluation instruction. VR increases access to authentic image evaluation practice by providing a simulated resultant image based off the students' applied positioning skills.

Image reject analysis and rejection causes in digital radiography at a university hospital through estimates of patient doses

The introduction of digital radiography has improved image acquisition. However, rejection of images remains a matter of concern. Reject analysis is part of the quality assurance program in radiology and helps identify potential errors or lack of training. A retrospective study was conducted at the radiology department of a university hospital. The reject rate was calculated both using the number of examinations, $r_n$, and the dose-area product, $r_d$. A reject rate $r_n$ of 3.3% for paediatric units and 4.5% for adults was found. The corresponding values of rd were 4.4 and 8.4%, respectively. The main rejection cause was patient motion, being 50.2% of rejected examinations in adults and 63.7% in children. The contribution of exposure errors was minor, as expected in digital radiography units. A discrepancy between reject rates $r_n$ and $r_d$ was observed, suggesting dosimetric quantities could be considered in reject analysis for further assessment of patient radiation burden.

Reject Analysis in Digital Radiography and Computed Tomography: A Belgian Imaging Department Case Study

Objective

Reject analysis is usually performed in digital radiography (DR) for quality assurance. Data for computed tomography (CT) rejects remains sparse. The aim of this study is to help provide a straightforward benchmark for reject analysis of both DR and CT.

Materials and methods

This retrospective observational study included 107,277 DR and 20,659 CT during 18 months in a tertiary care center. Rejected acquisitions were retrieved by Dose Archiving and Communication System (DACS). The DR and CT reject analysis included reject rates, reasons for rejection and supplementary radiation dose associated with these rejects.

Results

8,904 rejected DR and 514 rejected CT were retrieved. The DR reject rate was 8.3% whereas the CT reject rate was 2.5%. The cumulative effective dose (ED) of DR rejects was 377.3 mSv while the cumulative ED of CT rejects was 1267.4 mSv. The major reason for rejects was positioning for both DR (61%) and CT (44%).

Conclusion

This study helps constitute a simple reproducible method to analyze both DR and CT rejects simultaneously. Although CT rejects are less often monitored than DR rejects, the radiation dose associated with CT rejects is much higher, which emphasizes the need to systematically monitor both DR and CT rejects. Investigating the reasons and the most frequently rejected examinations gives an opportunity for improvement of imaging techniques in cooperation with technologists.

A semi-supervised learning-based quality evaluation system for digital chest radiographs

BACKGROUND

Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential step in medical examinations. However, manual evaluation can be time-consuming, labor-intensive, and prone to interobserver differences, making it less reliable.

PURPOSE

To alleviate the workload of radiographic technologists and enhance the efficiency of radiograph quality evaluation, it is crucial to develop rapid and reliable quality evaluation methods and establish a set of quantitative evaluation standards. To address this, we have proposed a quality evaluation system for digital radiographs that utilizes deep learning techniques to achieve fast and precise evaluation.

METHODS

The evaluation of frontal chest radiograph quality involves assessing patient positioning through semantic segmentation and foreign body detection. For lung, scapula, and clavicle segmentation in digital chest radiographs, a residual connection-based convolutional neural network π-ResUNet, was proposed. Criteria for patient positioning evaluation were established based on the segmentation and manual evaluation results. A convolutional neural network, FasterRCNN, was utilized to detect and localize foreign bodies in digital chest radiographs. To enhance the performance of both neural networks, a semi-supervised learning (SSL) strategy was implemented by incorporating a consistency loss that leverages a large number of unlabeled digital radiographs. We also trained the network using the fully supervised learning (FSL) strategy and compared their performance on the test set. The ChestXRay-14 and object-CXR datasets were used throughout the process.

RESULTS

By comparing with the manual annotation, the proposed network, trained using the SSL method, achieved a high Dice similarity coefficient (DSC) of 0.96, 0.88, and 0.88 for lung, scapula, and clavicle segmentation, respectively, outperforming the network trained with the FSL method. In addition, for foreign body detection, the proposed SSL method was superior to the FSL method, achieving an AUC (Area under receiver operating characteristic curve, Area under ROC curve) of 0.90 and an FROC (Free-response ROC) of 0.77 on the test dataset.

CONCLUSIONS

The experimental results show that our proposed system is well-suited for radiograph quality evaluation, with the semi-supervised learning method further improving the network's performance. The proposed method can evaluate the quality of a chest radiograph from two aspects-patient positioning and foreign body detection-within 1 s, offering a promising tool in radiograph quality evaluation.

Application of repeat image analysis to radiation therapy imaging modalities as a quality improvement tool for image guided radiotherapy

BACKGROUND

Repeat images contribute to excess patient dose and workflow inefficiencies and can be analyzed to identify potential areas for improvement within a program. Although routinely used in diagnostic imaging, repeat image analysis is not widely used in radiation therapy imaging, despite the role of imaging in the delivery of precise radiation treatments.

PURPOSE

Repeat image analysis was performed for on-board cone beam CT imagers and CT simulators within a radiation therapy department. Both the rate of repeat images and the reasons for the repeat images were analyzed.

METHODS

Data from nine conventional linear accelerators and three CT simulators were analyzed retrospectively over a 5-month period. Repeated images that were not part of the standard of care were considered repeat images. The repeat rate was expressed as the number of repeat scans as percentage of the total number of scans performed. The reasons for the repeats were collected and classified as either patient preparation, patient setup, patient motion, or machine error. These reasons were further classified into sub-categories.

RESULTS

The overall repeat rate across the linear accelerators was 3.3%, with a maximum of 5% on any single machine. The repeat rate for the three CT simulators was 1.5%. The most common reasons for repeat images were patient preparation (incorrect bladder or rectal filling) and patient setup or positioning. Greater positioning challenges led to higher repeat rates on units that treat a large number of breast patients, palliative patients, or pediatric patients.

CONCLUSIONS

Repeat image analysis can be applied within a radiation therapy department. Establishing baseline repeat rates and analyzing reasons for the repeat images can identify opportunities for improvements in terms of patient dose reduction and workflow efficiency for the program. Periodic repeat image analysis also permits monitoring the program for changes and for comparison against rates at other institutions.

Digital radiography reject analysis: A comparison between two radiology departments in New Zealand

INTRODUCTION

Image reject analysis (RA) in direct digital radiography (DDR) is an important quality indicator tool. Analysis of rejected images is a component of quality assurance (QA) programmes, with the overall aim of reducing patient radiation dose. This study aimed to compare differences in image rejection rates (RR) and the reasons for rejection between two radiology departments.

METHODS

A retrospective quantitative descriptive study of images performed across the two radiology departments (RAD 1 and RAD 2) acquired with DDR systems between the beginning of February and the end of May 2021 was undertaken. Collected data included the medical imaging technologist (MIT) selection of image rejection reasons for different anatomic regions and compared between the two radiology departments.

RESULTS

A total of 47,046 images and 29,279 images were acquired at RAD 1 and RAD 2, respectively, with an overall image rejection rate of 7.86% at RAD 1 and 5.91% at RAD 2. The primary reason for image rejections was positioning errors, 79.4% and 77.3% recorded at RAD 1 and RAD 2, respectively. Significant differences were demonstrated between the two radiology departments for image rejection rates and selected reasons for rejection for most anatomical body groups.

CONCLUSION

The implementation of image RA remains a key part of QA in radiology departments utilising DDR systems. This study recommends interventions based on image RRs for examinations taking into consideration the department-specific variations and imaging protocols used.

Quality management, quality assurance, and quality control in medical physics

The historic and ongoing evolution of the practice, technology, terminology, and implementation of programs related to quality in the medical radiological professions has given rise to the interchangeable use of the terms Quality Management (QM), Quality Assurance (QA), and Quality Control (QC) in the vernacular. This White Paper aims to provide clarification of QM, QA, and QC in medical physics context and guidance on how to use these terms appropriately in American College of Radiology (ACR) Practice Parameters and Technical Standards, generalizable to other guidance initiatives. The clarification of these nuanced terms in the radiology, radiation oncology, and nuclear medicine environments will not only boost the comprehensibility and usability of the Medical Physics Technical Standards and Practice Parameters, but also provide clarity and a foundation for ACR's clinical, physician-led Practice Parameters, which also use these important terms for monitoring equipment performance for safety and quality. Further, this will support the ongoing development of the professional practice of clinical medical physics by providing a common framework that distinguishes the various types of responsibilities borne by medical physicists and others in the medical radiological environment. Examples are provided of how QM, QA, and QC may be applied in the context of ACR Practice Parameters and Technical Standards.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks

Purpose

To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

Materials and Methods

A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.

Results

The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively.

Conclusion

This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.

The assessment of image quality and diagnostic value in X-ray images: a survey on radiographers' reasons for rejecting images

BACKGROUND

Assessing the quality of diagnostic images is subjective and influenced by factors such education, skills, and experience of the assessor. This study aims to explore the radiographers' assessments of medical usefulness or rejection of X-ray images in specific cases.

RESULTS

Eighty-one radiographers from different countries responded to the questionnaire distributed online at the EFRS research HUB at ECR 2020 (a 15% response rate). Forty-two percent of the respondents practiced in the UK and Ireland. In addition to rejecting or keeping images in the presented 30 cases and giving a main reason for the images rejected, the participants explained their choice using comments, 1176 comments were obtained. Sixty percent of the comments were on kept images. The respondents kept on average 63% of the images. In the "Keep", "Could keep", and "Reject" categories on average 84%, 63% and 43% of images were kept respectively. The most common reasons given for rejecting an image were suboptimal positioning and centering. Potential diagnostic value and radiation protection were indicated as reasons to keep an image perceived as of low quality reported in n = 353 and n = 33 comments respectively.

CONCLUSIONS

There is an agreement internationally on what makes a good quality X-ray image. However, the opinion on medical usefulness of images of low or poor quality compared to image criteria varies. Diagnostic capability and radiation protection was the rationale used for keeping images not fulfilling image criteria. There seems to be a need for diagnostic quality to be included in image assessment in clinical practice.

Clinical application of the optimized X-ray parameter model through analysis of disease risk and image quality when combining the ion chamber of automatic exposure control of digital radiography

OBJECTIVE

To present an optimized examination model by analyzing the risk of disease and image quality according to the combination of the ion chamber of automatic exposure control (AEC) with digital radiography (DR).

METHODS

The X-ray quality was analyzed by first calculating the percentage average error (PAE) of DR. After that, when using AEC, the combination of the ion chambers was the same as the left and centre and right, right and centre, left and centre, centre, right, and left, for a total of six. Accordingly, the entrance surface dose (ESD), risk of disease, and image quality were evaluated. ESD was obtained by attaching a semiconductor dosimeter to the L4 level of the lumbar spine, and then irradiating X-rays to dosimeter centre through average and standard deviation of radiation dose. The calculated ESD was input into the PCXMC 2.0 programme to evaluate disease risk caused by radiation. Meanwhile, image quality according to chamber combination was quantified as the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) through Image J.

RESULTS

X-ray quality of DR used in the experiment was within the normal range of±10. ESD of six ion chamber combinations was 1.363mGy, 0.964mGy, 0.946mGy, 0.866mGy, 0.748mGy, 0.726mGy for lumbar anteroposterior (AP), and the lumbar lateral values were 1.126mGy, 0.209mGy, 0.830mGy, 0.662mGy, 0.111mGy, and 0.250mGy, respectively. Meanwhile, disease risk analyzed through PCXMC 2.0 was bone marrow, colon, liver, lung, stomach, urinary and other tissue cancer, and disease risk showed a tendency to increase in proportion to ESD. SNR and CNR recorded the lowest values when three chambers were combined and did not show proportionality with dose, while showed the highest values when two chambers were combined.

CONCLUSION

In this study, combination of three ion chambers showed the highest disease risk and lowest image quality. Using one ion chamber showed the lowest disease risk, but lower image quality than two ion chambers. Therefore, if considering all above factors, combination of two ion chambers can optimally maintain the disease risk and image quality. Thus, it is considered an optimal X-ray examination parameter.

PATIENT DOSE ASSESSMENT AND OPTIMISATION OF PELVIC RADIOGRAPHY WITH COMPUTED RADIOGRAPHY SYSTEMS

Digital radiography systems can reduce radiation dose, this capability was harnessed to explore dose and image quality (IQ) optimisation strategies. Entrance surface dose (ESD), effective dose (ED) and organ doses were determined by the indirect method for patients undergoing pelvic anteroposterior X-ray examinations with computed radiography systems. The IQ of patients' radiographs was assessed in terms of signal-to-noise ratio (SNR). An anthropomorphic phantom was exposed with varying tube potential (kVp), tube current-time product (mAs), and focus-to-detector distance (FDD) to determine phantom-entrance dose for the optimisation studies. SNR of each phantom radiograph was determined. Patients' mean ESD of 2.38 ± 0.60 mGy, ED of 0.25 ± 0.07 mSv and SNR of 8.5 ± 2.2 were obtained. After optimisation, entrance dose was reduced by 29.2% with 5 cm increment in FDD, and 5 kVp reduction in tube potential. kVp and/or mAs reduction with an increment in FDD reduced entrance dose without adversely compromising radiographic-IQ.

Radiographic and fluoroscopic X-ray systems: Quality control of the X-ray tube and automatic exposure control using theoretical spectra to determine air kerma and dose to a homogenous phantom

PURPOSE

To develop a method to perform quality control (QC) of X-ray tubes and automatic exposure control (AEC) as a part of the QC of the radiographic and fluoroscopic X-ray system. Our aim is to verify the output from the X-ray tube by comparing the measured radiation output, or air kerma, to the theoretical output given the applied exposure settings and geometry, in addition to comparing the measured kV to the nominal kV. The AEC system for fluoroscopic and conventional X-ray systems is assessed by determining the absorbed dose to a homogenous phantom with different thicknesses.

METHOD

This study presents a model to verify the X-ray tube measurement results and a method to determine the dose to a homogenous phantom (Dphantom ). The following input is needed: a parameterized model of the X-ray spectrum, the X-ray tube measurements using a multifunctional X-ray meter, the exposure parameters recorded via imaging of polymethyl methacrylate (PMMA) slabs of different thickness that simulate the patient using AEC, and a parameterized model for calculating the dose to water from Monte Carlo simulations. The output is the entrance surface dose (ESD) and absorbed dose in the phantom, Dphantom (µGy). In addition, the parameterized X-ray spectrum is used to compare theoretical and measured air kerma as a part of the QC of the X-ray tube. To verify the proposed method, the X-ray spectrum provided in this study, SPECTRUM, was compared to two commercially available spectra, SpekCalc and Institute of Physics and Engineering in Medicine (IPEM) 78. The fraction of energy imparted to the homogenous phantom was compared to the imparted fraction calculated by PCXMC.

RESULTS

The spectrum provided in this study was in good agreement with two previously published X-ray spectra. The absolute percentage differences of the spectra varied from 0.05% to 3.9%, with an average of 1.4%, compared to SpekCalc. Similarly, the deviation from IPEM report 78 varied from 0.02% to 2.3%, with an average of 0.74%. The SPECTRUM was parameterized for calculation of the imparted fraction for target angles of 10°, 12°, and 15°, kV (50-150 kV) with the materials Al (2.2-8 mm), Cu (0-1 mm), and any combination of the filters, PMMA and water. The deviation of energy imparted from the results by PCXMC was less than 8% for all measurements across different kV, filtration, and vendors, obtained by using PMMA to record the exposure parameters, while the dose was calculated based on water with same thicknesses as the PMMA.

CONCLUSION

This study presents an accurate and suitable method to perform a part of the QC of fluoroscopic and conventional X-ray systems with respect to the X-ray tube and the associated AEC system. The method is suitable for comparing protocols within and between systems via the absorbed dose.

Digital radiograph rejection analysis during “Coronavirus disease 2019 (COVID-19) pandemic” in a tertiary care public sector hospital in Khyber Pakhtunkhwa Province of Pakistan

Background

Materials and method

Results

Conclusion

Applying a New CT Quality Metric in Radiology: How CT Pulmonary Angiography Repeat Rates Compare Across Institutions

OBJECTIVES

To quantify overall CT repeat and reject rates at five institutions and investigate repeat and reject rates for CT pulmonary angiography (CTPA).

METHODS

In this retrospective study, we apply an automated repeat rate analysis algorithm to 103,752 patient examinations performed at five institutions from July 2017 to August 2019. The algorithm identifies repeated scans for specific scanner and protocol combinations. For each institution, we compared repeat rates for CTPA to all other CT protocols. We used logistic regression and analysis of deviance to compare CTPA repeat rates across institutions and size-based protocols.

RESULTS

Of 103,752 examinations, 1,447 contained repeated helical scans (1.4%). Overall repeat rates differed across institutions (P < .001) ranging from 0.8% to 1.8%. Large-patient CTPA repeat rates ranged from 3.0% to 11.2% with the odds (95% confidence intervals) of a repeat being 4.8 (3.5-6.6) times higher for large- relative to medium-patient CTPA protocols. CTPA repeat rates were elevated relative to all other CT protocols at four of five institutions, with strong evidence of an effect at two institutions (P < .001 for each; odds ratios: 2.0 [1.6-2.6] and 6.2 [4.4-8.9]) and somewhat weaker evidence at the others (P = .005 and P = 0.011; odds ratios: 2.2 [1.3-3.8] and 3.7 [1.5-9.1], respectively). Accounting for size-based protocols, CTPA repeat rates differed across institutions (P < .001).

DISCUSSION

The results indicate low overall repeat rates (<2%) with CTPA rates elevated relative to other protocols. Large-patient CTPA rates were highest (eg, 11.2% at one institution). Differences in repeat rates across institutions suggest the potential for quality improvement.

Are the target exposure index and deviation index used efficiently?

INTRODUCTION

Exposure index (EI) is important to evaluate correct exposure in radiography and thus important for image quality. The purpose of this study was to evaluate whether the target exposure index (EI_T) and deviation index (DI) were used efficiently.

METHODS

Radiography departments in Iceland, using <10 years old equipment, were invited to participate. For each x-ray unit, admin users were asked about the use of EI_T and data was gathered on EI_T for five body parts (BP); lumbar spine, chest, hip, knee and hand. For each of the five BP, 100 examinations from the past year were selected randomly (or all, if < 100). The EI from one predefined view was recorded and the corresponding DI calculated.

RESULTS

A total of ten x-ray units, from four manufacturers and located at eight departments, were included in the study. The departments involved are comprised of a university hospital, smaller hospitals, and miscellaneous private departments. Two departments (25%) had not set EI_T, five (62.5%) used default values and only one had revised EI_T values. In four departments (50%) radiographers favored "acceptable EI range" over DI. The mean EI was significantly different (p < 0.05) from the EI_T in the majority of the five BP, in four out of the six departments that had defined EI_T. In total 30% of images from all departments combined had DI outside the range of -3.0 < DI < +3.0. The standard deviation of DI was from 1.4 to 2.7.

CONCLUSION

The study shows that the EI_T and DI are not used efficiently, regardless of equipment vendor or department characteristics.

IMPLICATIONS FOR PRACTICE

Current recommendations on targeting the mean DI of 0 need to be reinforced. Theoretical knowledge and training need to be improved.

Evaluation of image quality and patient exposure in fluoroscopy using a phantom: Is there any clinical relevance?

OBJECTIVE

To investigate the impact of X-ray preset acquisition protocol settings on fluoroscopy image quality (IQ) and radiation exposure.

MATERIALS & METHODS

A quality control (QC) phantom was imaged with a modern digital C-arm system, using various preset fluoroscopy protocols. IQ was assessed using human observers and in-house software for automated evaluation, based on contrast-to-noise ratios of details and their background. Patient radiation exposure was evaluated using the displayed Incident Air-Kerma and Kerma-Area Product values.

RESULTS

Protocol selection affects radiation exposure by a factor of about 3. IQ evaluation showed that acquisition protocols produce images with quite different characteristics. The visual IQ evaluation method was time consuming and cumbersome. The automated method, utilized the visual IQ evaluation results for calibration of detection thresholds. However, it failed to reproduce these results for all images and details types. In some images, digital image processing created artifacts which affected the pixel value distributions around details in a way that could be handled only by the human vision.

CONCLUSION

Manufacturers provide many preset protocols designated for specific clinical uses, which have large impact on IQ characteristics and radiation exposure. However, protocol settings' selection rationale is essentially a "black box" for the end user. Though QC phantoms are currently used for IQ evaluation, they are not appropriate for drawing firm conclusions concerning the expected performance of each protocol in clinical practice. Currently, there is no consensus on the optimum technical characteristics of preset protocols for specific procedures. More work is needed in this area.

Long-length tomosynthesis and 3D-2D registration for intraoperative assessment of spine instrumentation

PURPOSE

A system for long-length intraoperative imaging is reported based on longitudinal motion of an O-arm gantry featuring a multi-slot collimator. We assess the utility of long-length tomosynthesis and the geometric accuracy of 3D image registration for surgical guidance and evaluation of long spinal constructs.

METHODS

A multi-slot collimator with tilted apertures was integrated into an O-arm system for long-length imaging. The multi-slot projective geometry leads to slight view disparity in both long-length projection images (referred to as 'line scans') and tomosynthesis 'slot reconstructions' produced using a weighted-backprojection method. The radiation dose for long-length imaging was measured, and the utility of long-length, intraoperative tomosynthesis was evaluated in phantom and cadaver studies. Leveraging the depth resolution provided by parallax views, an algorithm for 3D-2D registration of the patient and surgical devices was adapted for registration with line scans and slot reconstructions. Registration performance using single-plane or dual-plane long-length images was evaluated and compared to registration accuracy achieved using standard dual-plane radiographs.

RESULTS

Longitudinal coverage of ∼50-64 cm was achieved with a single long-length slot scan, providing a field-of-view (FOV) up to (40 × 64) cm², depending on patient positioning. The dose-area product (reference point air kerma × x-ray field area) for a slot scan ranged from ∼702-1757 mGy·cm², equivalent to ∼2.5 s of fluoroscopy and comparable to other long-length imaging systems. Long-length scanning produced high-resolution tomosynthesis reconstructions, covering ∼12-16 vertebral levels. 3D image registration using dual-plane slot reconstructions achieved median target registration error (TRE) of 1.2 mm and 0.6° in cadaver studies, outperforming registration to dual-plane line scans (TRE = 2.8 mm and 2.2°) and radiographs (TRE = 2.5 mm and 1.1°). 3D registration using single-plane slot reconstructions leveraged the ∼7-14° angular separation between slots to achieve median TRE ∼2 mm and <2° from a single scan.

CONCLUSION

The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs.

A Multiinstitutional Study on Wasted CT Scans for Over 60,000 Patients

OBJECTIVE. Repeated imaging is an unnecessary source of patient radiation exposure, a detriment to patient satisfaction, and a waste of time and money. Although analysis of rates of repeated and rejected images is mandated in mammography and recommended in radiography, the available data on these rates for CT are limited. MATERIALS AND METHODS. In this retrospective study, an automated repeat-reject rate analysis algorithm was used to quantify repeat rates from 61,102 patient examinations obtained between 2015 and 2018. The algorithm used DICOM metadata to identify repeat acquisitions. We quantified rates for one academic site and one rural site. The method allows scanner-, technologist-, protocol-, and indication-specific rates to be determined. Positive predictive values and sensitivity were estimated for correctly identifying and classifying repeat acquisitions. Repeat rates were compared between sites to identify areas for targeted technologist training. RESULTS. Of 61,102 examinations, 4676 instances of repeat scanning contributed excess radiation dose to patients. Estimated helical overlap repeat rates were 1.4% (95% CI, 1.2-1.6%) for the rural site and 1.1% (95% CI, 1.0-1.2%) for the academic site. Significant differences in rates of repeat imaging required because of bolus tracking (11.6% vs 4.3%; p < 0.001) and helical extension (3.3% vs 1.8%; p < 0.001) were observed between sites. Positive predictive values ranged from 91% to 99% depending on the reason for repeat imaging and site location. Sensitivity of the algorithm was 92% (95% CI, 87-96%). Rates tended to be highest for emergent imaging procedures and exceeded 9% for certain protocols. CONCLUSION. Our multiinstitutional automated quantification of repeat rates for CT provided a useful metric for unnecessary radiation exposure and identification of technologists in need of training.

Optimising default radiographic exposure factors using Deviation Index

INTRODUCTION

Radiographers have a duty to ensure that radiation doses to patients are as low as reasonably achievable. With digital technologies, exposure factors which achieve the optimum balance between image noise and patient dose must be sought. In digital radiography, Deviation Index (DI) values provide the radiographer with feedback on the appropriateness of individual exposures but can also be tracked as part of a departmental quality assurance programme.

METHODS

In November 2017, exposure logs were extracted from six digital radiography (DR) x-ray systems, collated and analysed. Five examinations were identified which frequently produced DI values outside the manufacturer's recommended Optimal Range (-3 to +2). Incremental improvements were made to the default exposure settings for these examinations via a cyclical process of modification and re-evaluation. A full data collection exercise was then repeated in April 2019.

RESULTS

At baseline, 10,658 out of 29,637 (36.0%) exposures had DI values outside the manufacturer's recommended Optimal Range, but for some individual examinations the proportion was as high as 547 out of 725 (74.5%). Following multiple optimisation cycles, the overall proportion of examinations outside the Optimal Range had fallen to 7611 out of 26,759 (28.4%). Default milliampere-seconds (mAs) values for these examinations were reduced by between 22% and 50%.

CONCLUSION

A marked reduction in patient doses can be achieved through a departmental programme of DI value monitoring and targeted optimisation of default exposure settings.

IMPLICATIONS FOR PRACTICE

DI values should be routinely monitored as part of routine quality assurance programmes. Radiographers have a responsibility to ensure that they possess a clear understanding of DI values and that appropriate exposure settings are selected for each individual patient.

Peer Review Tool for General Radiography Technologists Improves Image Quality

PURPOSE

Quality improvement is vital to ensure health-care providers meet optimal patient care standards. Within our jurisdiction, accreditation requires image peer review as part of the quality assurance program. We propose a method to improve quality assurance in radiography by implementing a novel software-based peer review system for radiography technologists.

METHODS

This is a retrospective study. A peer review tool was developed in Microsoft Excel and Visual Basic. The tool has 14 image quality criteria, which were selected based on national and international criteria, each containing standardized answers ensuring a common scoring regime. The tool provides data analysis and storage of all peer reviews performed. Radiography supervisors utilized the tool to evaluate image quality of various body parts at 28 hospitals. The tool enabled each Medical Imaging Department to objectively score images at their own hospital. Approximately 2% of all radiographs were randomly chosen for peer review. Additionally, the tool allowed for regional analysis based on hospital, body part, and quality criterion.

RESULTS

Initial findings exposed equipment-related issues such as worn imaging plates, artifacts, and poor exposures, which prompted increased preventative maintenance. Other documented issues included foreign objects, inadequate collimation and centering, and inconsistent usage of lead markers. After identifying quality assurance-related issues, hospitals implemented education, resulting in improved overall image quality scores in subsequent audits.

CONCLUSION

The peer review tool helped identify and correct various issues affecting image quality and ensures our program meets required accreditation standards. Furthermore, staff found utilizing the tool to identify areas for improvement improved collaboration, ongoing education, and support between staff.

Assessment of Image Quality in Digital Radiographs Submitted for Hip Dysplasia Screening

Digital radiography is widely seen to be forgiving of poor exposure technique and to provide consistent high quality diagnostic images. Optimal quality images are however not universal; sub-optimal images are encountered. Evaluators on hip dysplasia schemes encounter images from multiple practices produced on equipment from multiple manufacturers. For images submitted to the Danish Kennel Club for hip dysplasia screening, a range of quality is seen and the evaluators are of the impression that variations in image quality area associated with particular equipment. This study was undertaken to test the hypothesis that there is an association between image quality in digital radiography and the manufacturer of the detector equipment, and to demonstrate the applicability of visual grading analysis (VGA) for image quality evaluation in veterinary practice. Data from 16,360 digital images submitted to the Danish Kennel Club were used to generate the hypothesis that there is an association between detector manufacturer and image quality and to create groups for VGA. Image quality in a subset of 90 images randomly chosen from 6 manufacturers to represent high and low quality images, was characterized using VGA and the results used to test for an association between image quality and system manufacturer. The range of possible scores in the VGA was −2 to +2 (higher scores are better). The range of the VGA scores for the images in the low image quality group (n = 45) was −1.73 to +0.67, (median −1.2). Images in the high image quality group (n = 44) ranged from −1.52 to +0.53, (median −0.53). This difference was statistically significant (p < 0.001). The study shows an association between VGA scores of image quality and detector manufacturer. Possible causes may be that imaging hardware and/or software are not equal in terms of quality, that the level of support sought and given differs between systems, or a combination of the two. Clinicians purchasing equipment should be mindful that image quality can differ across systems. VGA is practical for veterinarians to compare image quality between systems or within a system over time.

Image Quality Assessment of the Digital Radiography Units in Tabriz, Iran: A Phantom Study

Creating a high-quality image with the low patient dose is one of the most important goals in medical X-ray imaging. In this study, the image quality parameters of the digital radiographic units in Tabriz city were considered and compared with the international protocols. The image quality parameters were measured at 11 high workload digital radiography (DR) imaging centers in Tabriz city, and the results were compared to DINN 6868/58 standards. All centers equipped with the direct DR units passed the spatial resolution, low contrast detectability, contrast dynamic range, and noise tests, while the computed radiography (CR) units only could pass the two last tests. The highest spatial resolution was observed 3.2 lp/mm in the DR unit while the lowest one was 1.8 lp/mm in the CR unit. The highest noise was measured to be 0.03 OD that was observed in the DR unit. The most difference between the nominal and measured peak kilovoltage and mAs was 3.1% and 6.8%, respectively. The entrance surface air kerma in all units was obtained <0.63 mGy. The measured half-value layer range was between 2.4 and 3.54 mmAl. The physical parameters of image quality such as spatial resolution, contrast, and noise are robustness quantitative parameters for the assessment of the image quality performance of the units. Therefore, measurement and control of these parameters using two-dimensional phantoms are very critical.

Evaluating the use of exposure indicators in digital x-ray imaging system: Gauteng South Africa

INTRODUCTION

Exposure indicators (EIs) are the only indicator of correct exposure technique in digital x-ray imaging systems but the use of such indicators remains largely unexplored in a South African setting. With exposure creep in the digital radiography age being a worldwide phenomenon, the study investigated radiographers' familiarity and use of EIs, providing insight into current exposure technique practices in this setting.

METHODS

An explorative and descriptive quantitative study was conducted at 10 randomly selected radiography clinical training facilities in Gauteng, South Africa. The study used a questionnaire consisting of 26 questions based on familiarity with and use of EIs and radiographers' attitude to ionising radiation.

RESULTS

A response of rate of 49.3% was achieved. Results show a low number of respondents (54.3%) had a perfectly correct understanding of the exposure indicator (EI) and only 55.7% of respondents made correct use of the EI.

CONCLUSION

Observable lack of familiarity and use of the EI suggests that improvements could be made to the training radiographers receive on digital imaging systems. Moreover radiographers need to be vigilant against making decisions in digital radiography using knowledge that may relate exclusively to analogue radiography.

Reject analysis in digital radiography: A local study on radiographers and students' attitude in Iran

Reject analysis is as a quality indicator and critical tool for dose and image quality optimization in radiology departments. By reducing image rejection rate (RR), radiation dose to patients can be reduced effectively, yielding increased total cost-effectiveness. The aims of this study were to assess the rate of image rejection at 2 direct digital radiography (DR) departments to find the sources of rejection and to observe how radiology students and radiographers deal with image rejection.

Two radiology departments were surveyed during a 3-month period for all imaging procedures. Type of examination, numbers, and reasons for digital image rejection were obtained by systems and questionnaire. A predefined questionnaire, including 13 causes for rejection, was filled by radiographers and students.

Out of the 14 022 acquired images, 1116 were rejected, yielding an overall RR of 8%. Highest RRs were found for examination of cervical spine and lumbosacral. Positioning errors and improper patient preparation were the main reasons for digital image rejection.

The image RR was small, but there is a need for optimizing radiographic practice, and enhancing radiographer’s knowledge may enhance the performance.

Retrospective evaluation of exposure indicators: a pilot study of exposure technique in digital radiography

INTRODUCTION

Digital radiography lacks visual clues of exposure techniques used to obtain radiographs, therefore manufacturers have included exposure indicators (EIs). EIs provides feedback about exposure techniques used and evaluating EIs will yield much needed information about exposure trends used in digital radiography.

METHODS

A retrospective explorative quantitative study was conducted at nine randomly selected imaging departments in Gauteng, South Africa. Data pertaining to EI was retrospectively collected using quota sampling and compared to manufacturer recommended (MR) standards.

RESULTS

A total of 1422 EIs were collected. 50% of these were within the MR standard. 27% of EI indicated overexposure and 23% indicated underexposure.

CONCLUSIONS

Greater evidence of overexposure was noted in the retrospective analysis of the EI. This pilot study shows the need for further investigation into exposure technique practices in digital radiography and the need for measures to halt the evidenced overexposure.

Uses of Effective Dose: The Good, the Bad, and the Future

Effective dose (E) is a risk-adjusted dosimetric quantity developed by the International Commission on Radiological Protection. It is a key metric for practical management of the risk of stochastic health effects in a comprehensive radiation protection program. The International Commission on Radiological Protection and others have emphasized repeatedly that E is not intended to represent an actual radiation dose and should not be used as a risk-related metric for a specific person or population. The cancer risk uncertainties in the low-dose range and the underlying approximations, simplifications, and sex- and age-averaging used in generating E make it unsuitable for this purpose. However, in practice, medical imaging professionals and authors of peer-reviewed medical publications frequently and incorrectly use E as a surrogate for whole-body dose in order to calculate cancer risk estimates for specific patients or patient populations. This frequent misuse has popularized E for uses for which it was neither designed nor intended. Alternatives to E have been proposed that attempt to account for known age and sex differences in radiation sensitivity. E has also been proposed as a general indicator for communicating radiation risk to patients, if its limitations are kept in mind. Forthcoming guidance from the International Commission on Radiological Protection will likely clarify if, when, and how some form of E may be used as a rough indicator of the risk of a stochastic effect, possibly with some modifications for the substantial variations in risk known to exist with respect to age, sex, and population group.

Current state of practice regarding digital radiography exposure indicators and deviation indices: Report of AAPM Imaging Physics Committee Task Group 232

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 (EI_T ). 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. EI_T 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.

Medical physics 3.0 versus 1.0: A case study in digital radiography quality control

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.

Method for automatic detection of defective ultrasound linear array transducers based on uniformity assessment of clinical images - A case study

The purpose of the present study was to test an idea of and describe a concept of a novel method of detecting defects related to horizontal nonuniformities in ultrasound equipment. The method is based on the analysis of ultrasound images collected directly from the clinical workflow. In total over 31000 images from three ultrasound scanners from two vendors were collected retrospectively from a database. An algorithm was developed and applied to the images, 150 at a time, for detection of systematic dark regions in the superficial part of the images. The result was compared with electrical measurements (FirstCall) of the transducers, performed at times when the transducers were known to be defective. The algorithm made similar detection of horizontal nonuniformities for images acquired at different time points over long periods of time. The results showed good subjective visual agreement with the available electrical measurements of the defective transducers, indicating a potential use of clinical images for early and automatic detection of defective transducers, as a complement to quality control.

Development of a tool to aid the radiologic technologist using augmented reality and computer vision

This technical innovation describes the development of a novel device to aid technologists in reducing exposure variation and repeat imaging in computed and digital radiography. The device consists of a color video and depth camera in combination with proprietary software and user interface. A monitor in the x-ray control room displays the position of the patient in real time with respect to automatic exposure control chambers and image receptor area. The thickness of the body part of interest is automatically displayed along with a motion indicator for the examined body part. The aim is to provide an automatic measurement of patient thickness to set the x-ray technique and to assist the technologist in detecting errors in positioning and motion before the patient is exposed. The device has the potential to reduce the incidence of repeat imaging by addressing problems technologists encounter daily during the acquisition of radiographs.

Educational Module Intervention for Radiographers to Reduce Repetition Rate of Routine Digital Chest Radiography in Makkah Region of Saudi Arabia Tertiary Hospitals: Protocol of a Quasi-Experimental Study

Background

Repetition of an image is a critical event in any radiology department. When the repetition rate of routine digital chest radiographs is high, radiation exposure of staff and patients is increased. In addition, repetition consumes the equipment’s life span, thus affecting the annual budget of the department.

Objective

The aim of this study is to determine the impact of a printed educational module on reducing the repetition rate of routine digital chest radiography among radiographers in Makkah Region tertiary hospitals.

Methods

A quasi-experimental time series with a control group will be conducted in Makkah Region tertiary hospitals for 8 months starting in the second quarter of 2017. Four hospitals out of 5 in the region will be selected; 2 of them will be selected as the control group and the other 2 as the intervention group. Stratification and a simple random sampling technique will be used to sample 56 radiographers in each group. Pre- and postintervention assessments will be conducted to determine the radiographer knowledge, motivation, and skills and repetition rate of chest radiographs. Radiographs of the chest performed by sampled radiographers in the selected hospitals will be collected for 2 weeks before and after the intervention. A piloted questionnaire will be distributed and collected by a researcher in both groups. One-way multivariate analysis of variance and 2-way repeated multivariate analysis of variance will be used to analyze the data.

Results

It is expected that the repetition rate in the intervention group will decline after implementing the intervention and the change will be statistically significant (P<.05). Furthermore, it is expected that the knowledge, motivation, and skill levels in the intervention group will increase significantly among radiographers after implementation of the intervention (P<.05). Meanwhile, knowledge, motivation, and skills in the control group will not change.

Conclusions

A quasi-experimental time series with a control will be conducted to investigate the effect of printed educational material in reducing the repetition rate of routine digital chest radiographs among radiographers in tertiary hospitals in the Makkah Region of Saudi Arabia.

Evaluation of digital radiography practice using exposure index tracking

Some digital radiography (DR) detectors and software allow for remote download of exam statistics, including image reject status, body part, projection, and exposure index (EI). The ability to have automated data collection from multiple DR units is conducive to a quality control (QC) program monitoring institutional radiographic exposures. We have implemented such a QC program with the goal to identify outliers in machine radiation output and opportunities for improvement in radiation dose levels. We studied the QC records of four digital detectors in greater detail on a monthly basis for one year. Although individual patient entrance skin exposure varied, the radiation dose levels to the detectors were made to be consistent via phototimer recalibration. The exposure data stored on each digital detector were periodically downloaded in a spreadsheet format for analysis. EI median and stan-dard deviation were calculated for each protocol (by body part) and EI histograms were created for torso protocols. When histograms of EI values for different units were compared, we observed differences up to 400 in average EI (representing 60% difference in radiation levels to the detector) between units nominally cali-brated to the same EI. We identified distinct components of the EI distributions, which in some cases, had mean EI values 300 apart. Peaks were observed at the current calibrated EI, a previously calibrated EI, and an EI representing computed radiography (CR) techniques. Our findings in this ongoing project have allowed us to make useful interventions, from emphasizing the use of phototimers instead of institutional memory of manual techniques to improvements in our phototimer calibration. We believe that this QC program can be implemented at other sites and can reveal problems with radiation levels in the aggregate that are difficult to identify on a case-by-case basis.

Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography

Reject rate analysis has been part of radiography departments' quality control since the days of screen-film radiography. In the era of digital radiography, one might expect that reject rate analysis is easily facilitated because of readily available information produced by the modality during the examination procedure. Unfortunately, this is not always the case. The lack of an industry standard and the wide variety of system log entries and formats have made it difficult to implement a robust multivendor reject analysis program, and logs do not always include all relevant information. The increased use of digital detectors exacerbates this problem because of higher reject rates associated with digital radiography compared with computed radiography. In this article, the authors report on the development of a unified database for vendor-neutral reject analysis across multiple sites within an academic institution and share their experience from a team-based approach to reduce reject rates.