Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.

There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.

A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography.

Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demonstrate that ML model generalizability is achievable in medical imaging with the detection of intracranial hemorrhage (ICH) on non-contrast computed tomography (CT) scans serving as the use case. An ML model was trained using 21,784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center. This real world external validation dataset consisted of every unenhanced head CT scan (n = 5965) performed in our emergency department in 2019 without exclusion. The model demonstrated an AUC of 98.4%, sensitivity of 98.8%, and specificity of 98.0%, on the test dataset. On external validation, the model demonstrated an AUC of 95.4%, sensitivity of 91.3%, and specificity of 94.1%. Evaluating the ML model using a real world external validation dataset that is temporally and geographically distinct from the training dataset indicates that ML generalizability is achievable in medical imaging applications.

Intraindividual variation of dose parameters in oncologic CT imaging.

The objective of this study is to identify essential aspects influencing radiation dose in computed tomography [CT] of the chest, abdomen and pelvis by intraindividual comparison of imaging parameters and patient related factors. All patients receiving at least two consecutive CT examinations for tumor staging or follow-up within a period of 22 months were included in this retrospective study. Different CT dose estimates (computed tomography dose index [CTDIvol], dose length product [DLP], size-specific dose estimate [SSDE]) were correlated with patient's body mass index [BMI], scan length and technical parameters (tube current, tube voltage, pitch, noise level, level of iterative reconstruction). Repeated-measures-analysis was initiated with focus on response variables (CTDIvol, DLP, SSDE) and possible factors (age, BMI, noise, scan length, peak kilovoltage [kVp], tube current, pitch, adaptive statistical iterative reconstruction [ASIR]). A univariate-linear-mixed-model with repeated-measures-analysis followed by Bonferroni adjustments was used to find associations between CT imaging parameters, BMI and dose estimates followed by a subsequent multivariate-mixed-model with repeated-measures-analysis with Bonferroni adjustments for significant parameters. A p-value <0.05 was considered statistically significant. We found all dose estimates in all imaging regions were substantially affected by tube current. The iterative reconstruction significantly influenced all dose estimates in the thoracoabdominopelvic scans as well as DLP and SSDE in chest-CT. Pitch factor affected all dose parameters in the thoracoabdominopelvic CT group. These results provide further evidence that tube current has a pivotal role and potential in radiation dose management. The use of iterative reconstruction algorithms can substantially decrease radiation dose especially in thoracoabdominopelvic and chest-CT-scans. Pitch factor should be kept at a level of ≥1.0 in order to reduce radiation dose.

RSNA-STR-ACR Consensus Statement for COVID-19 CT Patterns: Interreader Agreement in 240 Consecutive Patients and Association With RT-PCR Status.

PURPOSE: The aim of this study was to study interreader agreement of the RSNA-STR-ACR (Radiological Society of North America/Society of Thoracic Radiology/American College of Radiology) consensus statement on reporting chest computed tomography (CT) findings related to COVID-19 on a sample of consecutive patients confirmed with reverse transcriptase-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2. MATERIALS AND METHODS: This institutional review board-approved retrospective study included 240 cases with a mean age of 47.6 ± 15.9 years, ranging from 20 to 90 years, who had a chest CT and RT-PCR performed. Computed tomography images were independently analyzed by 2 thoracic radiologists to identify patterns defined by the RSNA-STR-ACR consensus statement, and concordance was determined with weighted κ tests. Also, CT findings and CT severity scores were tabulated and compared. RESULTS: Of the 240 cases, 118 had findings on CT. The most frequent on the RT-PCR-positive group were areas of ground-glass opacities (80.5%), crazy-paving pattern (32.2%), and rounded pseudonodular ground-glass opacities (22.9%). Regarding the CT patterns, the most frequent in the RT-PCR-positive group was typical in 75.9%, followed by negative in 17.1%. The interreader agreement was 0.90 (95% confidence interval, 0.80-0.96) in this group. The CT severity score had a mean difference of -0.07 (95% confidence interval, -0.48 to 0.34) among the readers, showing no significant differences regarding visual estimation. CONCLUSIONS: The RSNA-STR-ACR consensus statement on reporting chest CT patterns for COVID-19 presents a high interreader agreement, with the typical pattern being more frequently associated with RT-PCR-positive examinations.

Reporting radiographers' interpretation and use of the British Society of Thoracic Imaging's coding system when reporting COVID-19 chest x-rays.

INTRODUCTION: The United Kingdom (UK) has experienced one of the worst initial waves of the COVID-19 pandemic. Clinical signs help guide initial diagnosis, though definitive diagnosis is made using the laboratory technique reverse transcription polymerase chain reaction (RT-PCR). The chest x-ray (CXR) is used as the primary imaging investigation in the United Kingdom (UK) for patients with suspected COVID-19. In some hospitals these CXRs may be reported by a radiographer. METHODS: Retrospective review of CXR reports by radiographers for suspected COVID-19 patients attending the Emergency Department (ED) of a hospital in the UK. Interpretation and use of the British Society of Thoracic Imaging (BSTI) coding system was assessed. Report description and code use were cross-checked. Report and code usage were checked against the RT-PCR result to determine accuracy. Report availability was checked against the availability of the RT-PCR result. A confusion matrix was utilised to determine performance. The data were analysed manually using Excel. RESULTS: Sample size was 320 patients; 54.1% male patients (n = 173), 45.9% female patients (n = 147). The correct code matched report descriptions in 316 of the 320 cases (98.8%). In 299 of the 320 cases (93.4%), the reports were available before the RT-PCR swab result. CXR sensitivity for detecting COVID-19 was 85% compared to 93% for the initial RT-PCR. CONCLUSION: Reporting radiographers can adequately utilise and apply the BSTI classification system when reporting COVID-19 CXRs. They can recognise the classic CXR appearances of COVID-19 and those with normal appearances. Future best practice includes checking laboratory results when reporting CXRs with ambiguous appearances. IMPLICATIONS FOR PRACTICE: Utilisation of reporting radiographers to report CXRs in any future respiratory pandemic should be considered a service-enabling development.

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.

Importance: Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. Objective: To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. Design, Setting, and Participants: This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. Main Outcomes and Measures: The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. Results: The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001). Conclusions and Relevance: These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.

1024-pixel image matrix for chest CT - Impact on image quality of bronchial structures in phantoms and patients.

OBJECTIVES: To compare objective and subjective image quality of bronchial structures between a 512-pixel and a 1024-pixel image matrix for chest CT in phantoms and in patients. MATERIALS AND METHODS: First, a two-size chest phantom was imaged at two radiation doses on a 192-slice CT scanner. Datasets were reconstructed with 512-, 768-, and 1024-pixel image matrices and a sharp reconstruction kernel (Bl64). Image sharpness and normalized noise power spectrum (nNPS) were quantified. Second, chest CT images of 100 patients were reconstructed with 512- and 1024-pixel matrices and two blinded readers independently assessed objective and subjective image quality. In each patient dataset, the highest number of visible bronchi was counted for each lobe of the right lung. A linear mixed effects model was applied in the phantom study and a Welch's t-test in the patient study. RESULTS: Objective image sharpness and image noise increased with increasing matrix size and were highest for the 1024-matrix in phantoms and patients (all, P<0.001). nNPS was comparable among the three matrices. Objective image noise was on average 16% higher for the 1024-matrix compared to the 512-matrix in patients (P<0.0001). Subjective evaluation in patients yielded improved sharpness but increased image noise for the 1024- compared to the 512-matrix (both, P<0.001). There was no significant difference between highest-order visible bronchi (P>0.07) and the overall bronchial image quality between the two matrices (P>0.22). CONCLUSION: Our study demonstrated superior image sharpness and higher image noise for a 1024- compared to a 512-pixel matrix, while there was no significant difference in the depiction and subjective image quality of bronchial structures for chest CT.

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.

Virtual monoenergetic images and post-processing algorithms effectively reduce CT artifacts from intracranial aneurysm treatment.

To evaluate artifact reduction by virtual monoenergetic images (VMI) and metal artifact reduction algorithms (MAR) as well as the combination of both approaches (VMIMAR) compared to conventional CT images (CI) as standard of reference. In this retrospective study, 35 patients were included who underwent spectral-detector CT (SDCT) with additional MAR-reconstructions due to artifacts from coils or clips. CI, VMI, MAR and VMIMAR (range: 100-200 keV, 10 keV-increment) were reconstructed. Region-of-interest based objective analysis was performed by assessing mean and standard deviation of attenuation (HU) in hypo- and hyperdense artifacts from coils and clips. Visually, extent of artifact reduction and diagnostic assessment were rated. Compared to CI, VMI ≥ 100 keV, MAR and VMIMAR between 100-200 keV increased attenuation in hypoattenuating artifacts (CI/VMI200keV/MAR/VMIMAR200keV, HU: -77.6 ± 81.1/-65.1 ± 103.2/-36.9 ± 27.7/-21.1 ± 26.7) and decreased attenuation in hyperattenuating artifacts (HU: 47.4 ± 32.3/42.1 ± 50.2/29.5 ± 18.9/20.8 ± 25.8). However, differences were only significant for MAR in hypodense and VMIMAR in hypo- and hyperdense artifacts (p < 0.05). Visually, hypo- and hyperdense artifacts were significantly reduced compared to CI by VMI≥140/100keV, MAR and VMIMAR≥100keV. Diagnostic assessment of surrounding brain tissue was significantly improved in VMI≥100keV, MAR and VMIMAR≥100keV. The combination of VMI and MAR facilitates a significant reduction of artifacts adjacent to intracranial coils and clips. Hence, if available, these techniques should be combined for optimal reduction of artifacts following intracranial aneurysm treatment.

In-plane image quality and NPWE detectability index in digital breast tomosynthesis.

A rigorous 2D analysis of signal and noise transfer applied to reconstructed planes in digital breast tomosynthesis (DBT) is necessary for system characterization and optimization. This work proposes a method for assessing technical image quality and system detective quantum efficiency (DQEsys) for reconstructed planes in DBT. Measurements of 2D in-plane modulation transfer function (MTF) and noise power spectrum (NPS) were made on five DBT systems using different acquisition parameters, reconstruction algorithms and plane spacing. This work develops the noise equivalent quanta (NEQ), DQEsys and detectability index (d') calculated using a non-prewhitening model observer with eye filter (NPWE) for reconstructed DBT planes. The images required for this implementation were acquired using a homogeneous test object of thickness 40 mm poly(methyl) methacrylate plus 0.5 mm Al; 2D MTF was calculated from an Al disc of thickness 0.2 mm and diameter 50 mm positioned within the phantom. The radiant contrast of the MTF disc and the air kerma at the system input were used as normalization factors. The NPWE detectability index was then compared to the in-plane contrast-detail (c-d) threshold measured using the CDMAM phantom. The MTF and NPS measured on the different systems showed a strong anisotropy, consistent with the cascaded models developed in the literature for DBT. Detectability indices calculated from the measured MTF and NPS successfully predicted changes in c-d detectability for details between 0.1 mm and 2.0 mm, for DBT plane spacings between 0.5 mm and 10 mm, and for air kerma values at the system input between 157 µGy and 1170 µGy. The linear Pearson correlation between the detectability index and threshold gold thickness of the CDMAM phantom was -0.996. The method implements a parametric means of assessing the technical image quality of reconstructed DBT planes, providing valuable information for optimization of DBT systems.

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing.

INTRODUCTION: This study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest. MATERIALS AND METHODS: This study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition. RESULTS: The learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1. DISCUSSION: The measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them. CONCLUSION: Obtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.

CT iterative reconstruction algorithms: a task-based image quality assessment.

PURPOSE: To assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors. MATERIALS AND METHODS: We used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d') was computed to model the detection task of a large mass in the liver (large feature; 120 HU and 25-mm diameter) and a small calcification (small feature; 500 HU and 1.5-mm diameter). RESULTS: With H/SIR, the highest values of d' for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were - 35% for GE, - 62% for Philips, and - 13% for Siemens; for the small feature, corresponding reductions were - 45%, - 78%, and - 14%, respectively. With the Canon system, a potential dose reduction of - 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%. CONCLUSION: This multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d' is highly adapted and robust for an optimization process. KEY POINTS: • The performance of four CT systems was evaluated by using imQuest software to assess noise characteristic, spatial resolution, and lesion detection. • Two task functions were defined to model the detection task of a large mass in the liver and a small calcification. • The advantage of task-based image quality assessment for radiologists is that it does not include only complicated metrics, but also clinically meaningful image quality.

Expert Recommendations on Cardiac Computed Tomography for Planning Transcatheter Left Atrial Appendage Occlusion.

Transcatheter left atrial appendage occlusion is an increasingly used alternative to oral anticoagulation in selected patients with atrial fibrillation. Pre-procedural imaging is a prerequisite to a successful intervention, with transesophageal echocardiography as the current gold standard. However, cardiac computed tomography offers improved imaging with high-quality multiplanar and 3-dimensional reconstructed images. Nevertheless, the lack of a standardized imaging protocol has slowed the adoption of cardiac computed tomography into clinical practice. On the basis of current research and expert consensus, this paper provides a protocol for the preparation, acquisition, and interpretation of cardiac computed tomographic imaging in pre-procedural planning of left atrial appendage occlusion.

External validation and transfer learning of convolutional neural networks for computed tomography dental artifact classification.

Quality assurance of data prior to use in automated pipelines and image analysis would assist in safeguarding against biases and incorrect interpretation of results. Automation of quality assurance steps would further improve robustness and efficiency of these methods, motivating widespread adoption of techniques. Previous work by our group demonstrated the ability of convolutional neural networks (CNN) to efficiently classify head and neck (H&N) computed-tomography (CT) images for the presence of dental artifacts (DA) that obscure visualization of structures and the accuracy of Hounsfield units. In this work we demonstrate the generalizability of our previous methodology by validating CNNs on six external datasets, and the potential benefits of transfer learning with fine-tuning on CNN performance. 2112 H&N CT images from seven institutions were scored as DA positive or negative. 1538 images from a single institution were used to train three CNNs with resampling grid sizes of 643, 1283 and 2563. The remaining six external datasets were used in five-fold cross-validation with a data split of 20% training/fine-tuning and 80% validation. The three pre-trained models were each validated using the five-folds of the six external datasets. The pre-trained models also underwent transfer learning with fine-tuning using the 20% training/fine-tuning data, and validated using the corresponding validation datasets. The highest micro-averaged AUC for our pre-trained models across all external datasets occurred with a resampling grid of 2563 (AUC = 0.91 ± 0.01). Transfer learning with fine-tuning improved generalizability when utilizing a resampling grid of 2563 to a micro-averaged AUC of 0.92 ± 0.01. Despite these promising results, transfer learning did not improve AUC when utilizing small resampling grids or small datasets. Our work demonstrates the potential of our previously developed automated quality assurance methods to generalize to external datasets. Additionally, we showed that transfer learning with fine-tuning using small portions of external datasets can be used to fine-tune models for improved performance when large variations in images are present.

Dose-optimized computed tomography of the cervical spine in patients with shoulder pull-down: Is image quality comparable with a standard dose protocol in an emergency setting?

PURPOSE: Superimposing soft tissue and bony structures in computed tomography (CT) of the cervical spine (C-spine) is a limiting factor in optimizing radiation exposure maintaining an acceptable image quality. Therefore, we assessed image quality of dose-optimized (DO) C-spine CT in patients capable of shoulder pull-down in an emergency setting. METHODS AND MATERIALS: DO-CT (105mAs/120 kVp) of the C-spine in trauma settings was performed in patients with shoulder pull-down if C5 was not superimposed by soft tissue on the lateral topogram, otherwise standard-dose (SD)-CT (195 mAs/120 kVp) was performed. 34 DO (mean age, 68y ±â€¯21; BMI, 24.2 kg/m2 ±â€¯3.2) and 34 SD (mean age 70y ±â€¯19; BMI 25.7 kg/m2 ±â€¯4.4) iterative reconstructed CTs were evaluated at C2/3 and C6/7 by two musculoskeletal radiologists. Qualitative image noise and morphological characteristics of bony structures (cortex, trabeculae) were assessed on a Likert scale. Quantitative image noise was measured and effective dose (ED) was recorded. Parameters were compared using Mann-Whitney-U-test (p < 0.05). RESULTS: At C2/3, DO-CT vs. SD-CT yielded comparable qualitative noise (mean, 1.3 vs. 1.0; p = 0.18) and morphological characteristics, but higher quantitative noise (27.2 ±â€¯8.8HU vs. 19.6 ±â€¯4.5HU; p < 0.001). At C6/7, DO-CT yielded lower subjective noise (1.9; SD-CT 2.2; p = 0.017) and better morphological characteristics with higher visibility scores for cortex (p = 0.001) and trabeculae (p = 0.03). Quantitative noise did not differ (p = 0.24). Radiation dose was 51% lower using DO-CT (EDDO-CT 0.80 ±â€¯0.1 mSv; EDSD-CT 1.63 ±â€¯0.2 mSv; p < 0.001). CONCLUSION: C-spine CT with dose reduction of 51% showed no image quality impairment. Additional pull-down of both shoulders allowed better image quality at lower C-spine segments as compared to a standard protocol.

Greater than the sum of the parts: Impact of radiographer clinical image interpretation.

Radiographer preliminary image evaluation, within strong governance and audit systems, can help reduce diagnostic errors in the emergency setting. Radiographers, clinicians and radiologists should work together as a team to improve patient care and outcomes.

Reducing risk in the emergency department: a 12-month prospective longitudinal study of radiographer preliminary image evaluations.

INTRODUCTION: Innovations are necessary to accommodate the increasing demands on emergency departments whilst maintaining a high level of patient care and safety. Radiographer Preliminary Image Evaluation (PIE) is one such innovation. The purpose of this study was to determine the accuracy of radiographer PIE in clinical practice within an emergency department over 12 months. METHODS: A total of 6290 radiographic examinations were reviewed from 15 January 2016 to 15 January 2017. The range of adult and paediatric examinations incorporated in the review included the appendicular and axial skeleton including the chest and abdomen. Each examination was compared to the radiologist's report this allowed calculated mean sensitivity and specificity values to indicate if the radiographer's PIE was of a true negative/positive or false negative/positive value. Cases of no PIE participation or series' marked as unsure for pathology by the radiographer were also recorded. This allowed mean sensitivity, specificity and diagnostic accuracy to be calculated. RESULTS: The study reported a mean ± 95% confidence level (standard deviation) for sensitivity, specificity, accuracy, no participation and unsure of 71.1% ± 2.4% (6.1), 98.4% ± 0.04% (0.9), 92.0% ± 0.68% (1.9), 5.1% (1.6) and 3.6% (0.14) respectively. CONCLUSIONS: This study has demonstrated that the participating radiographers provided a consistent PIE service while maintaining a reasonably high diagnostic accuracy. This form of image interpretation can complement an emergency referrer's diagnosis when a radiologist's report is unavailable at the time of patient treatment. PIE promotes a reliable enhancement of the radiographer's role with the multi-disciplinary team.

Image quality and radiation dose in planar imaging - Image quality figure of merits from the CDRAD phantom.

PURPOSE: A contrast-detail phantom such as CDRAD is frequently used for quality assurance, optimization of image quality, and several other purposes. However, it is often used without considering the uncertainty of the results. The aim of this study was to assess two figure of merits (FOM) originating from CDRAD regarding the variations of the FOMs by dose utilized to create the x-ray image. The probability of overlapping (assessing an image acquired at a lower dose as better than an image acquired at a higher dose) was determined. METHODS: The CDRAD phantom located underneath 12, 20, and 26 cm PMMA was imaged 16 times at five dose levels using an x-ray system with a flat-panel detector. All images were analyzed by CDRAD Analyser, version 1.1, which calculated the FOM inverse image quality figure (IQFinv ) and gave contrast detail curves for each image. Inherent properties of the CDRAD phantom were used to derive a new FOM h, which describes the size of the hole with the same diameter and depth that is just visible. Data were analyzed using heteroscedastic regression of mean and variance by dose. To ease interpretation, probabilities for overlaps were calculated assuming normal distribution, with associated bootstrap confidence intervals. RESULTS: The proportion of total variability in IQFinv , explained by the dose (R2 ), was 91%, 85%, and 93% for 12, 20, and 26 cm PMMA. Corresponding results for h were 91%, 89%, and 95%. The overlap probability for different mAs levels was 1% for 0.8 vs 1.2 mAs, 5% for 1.2 vs 1.6 mAs, 10% for 1.6 vs 2.0 mAs, and 10% for 2.0 mAs vs 2.5 mAs for 12 cm PMMA. For 20 cm PMMA, it was 0.5% for 10 vs 16 mAs, 13% for 16 vs 20 mAs, 14% for 20 vs 25 mAs, and 14% for 25 vs 32 mAs. For 26 cm PMMA, the probability varied from 0% to 6% for various mAs levels. Even though the estimated probability for overlap was small, the 95% confidence interval (CI) showed relatively large uncertainties. For 12 cm PMMA, the associated CI for 0.8 vs 1.2 mAs was 0.1-3.2%, and the CI for 1.2 vs 1.6 mAs was 2.1-7.8%. CONCLUSIONS: Inverse image quality figure and h are about equally related to dose level. The FOM h, which describes the size of a hole that should be seen in the image, may be a more intuitive FOM than IQFinv . However, considering the probabilities for overlap and their confidence intervals, the FOMs deduced from the CDRAD phantom are not sensitive to dose. Hence, CDRAD may not be an optimal phantom to differentiate between images acquired at different dose levels.

A study on fluoroscopic images in exposure reduction techniques - Focusing on the image quality of fluoroscopic images and exposure images.

The quality of the present day fluoroscopic images is sufficiently high for use as exposure images depending on the environment where the fluoroscopic images are recorded. In some facilities which use fluoroscopic images as exposure images they are recorded with a radiological x-ray diagnostic device equipped with a fluoroscopic storage function. There are, however, cases where fluoroscopic images cannot be used as exposure images because the quality of the fluoroscopic image cannot be assured in the environment where the fluoroscopic images are recorded. This poses problems when stored fluoroscopic images are used in place of exposure images without any clearly established standard. In the present study, we establish that stored fluoroscopic images can be used as exposure images by using gray values obtained from profile curves. This study finds that replacement of stored fluoroscopic images with exposure images requires 20.1 or higher gray scale value differences between the background and signal, using a 20 cm thick acrylic phantom (here an adult abdomen as representing the human body) as the specific geometry. This suggests the conclusion that the gray value can be considered a useful index when using stored fluoroscopic images as exposure images.