Approach to Acute Traumatic and Nontraumatic Diaphragmatic Abnormalities.

Acute diaphragmatic abnormalities encompass a broad variety of relatively uncommon and underdiagnosed pathologic conditions, which can be subdivided into nontraumatic and traumatic entities. Nontraumatic abnormalities range from congenital hernia to spontaneous rupture, endometriosis-related disease, infection, paralysis, eventration, and thoracoabdominal fistula. Traumatic abnormalities comprise both blunt and penetrating injuries. Given the role of the diaphragm as the primary inspiratory muscle and the boundary dividing the thoracic and abdominal cavities, compromise to its integrity can yield devastating consequences. Yet, diagnosis can prove challenging, as symptoms may be vague and findings subtle. Imaging plays an essential role in investigation. Radiography is commonly used in emergency evaluation of a patient with a suspected thoracoabdominal process and may reveal evidence of diaphragmatic compromise, such as abdominal contents herniated into the thoracic cavity. CT is often superior, in particular when evaluating a trauma patient, as it allows rapid and more detailed evaluation and localization of pathologic conditions. Additional modalities including US, MRI, and scintigraphy may be required, depending on the clinical context. Developing a strong understanding of the acute pathologic conditions affecting the diaphragm and their characteristic imaging findings aids in efficient and accurate diagnosis. Additionally, understanding the appearance of diaphragmatic anatomy at imaging helps in differentiating acute pathologic conditions from normal variations. Ultimately, this knowledge guides management, which depends on the underlying cause, location, and severity of the abnormality, as well as patient factors. ©RSNA, 2024 Supplemental material is available for this article.

Establishing and Leading a 3D Postprocessing Radiology Lab: A Managerial and Leadership Perspective.

The rapid acquisition of larg volumes of thin-section CT images has created a considerable need and interest for 3D postprocessing during the interpretation of medical imaging. As a result of the increasing number of postprocessing applications, requiring diagnostic radiologists to perform postprocessing is no longer realistic. This article is a comprehensive review of medical resources regarding establishing a postprocessing radiology laboratory. Besides, leadership and managerial aspects have been covered through a professional business lens. In large-volume settings, a dedicated 3D postprocessing lab ensures the quality, reproducibility, and efficiency of images. Adequate staffing is necessary to fulfill the postprocessing requirements. Educational and experience requirements for 3D technologists may vary among different running laboratories. To evaluate the establishment and running of a 3D lab, it is beneficial to implement diagnostic radiology cost-effectiveness tools. Although establishing a 3D lab has many benefits, certain challenges should be considered. Outsourcing or offshoring may serve as alternatives for establishing a postprocessing laboratory. Building and operating a 3D lab is a significant change in healthcare facilities, and it is crucial for organizations to be aware of the strong resistance toward alternatives the status quo, known as the status quo trap. The change process has essential steps, and skipping the steps creates an illusion of speed but never produces satisfactory results. The organization should ensure the engagement of all interested parties in the whole process. Moreover, a clear vision and proper communication of the vision are vital, and it is crucial to value small wins and ensure expectation clarity in leading the lab during the process.

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports.

Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.

Assessing tophaceous spinal gout treatment response using dual-energy CT as a point-of-care imaging modality: case report.

We describe a case of chronic tophaceous gout affecting the spine, hands, elbows, feet, and knees in a 67-year-old man with serum urate levels at 549 µmol/L whose response to treatment was successfully mapped using dual-energy computed tomography (DECT). The patient presented with exacerbation of acute-on-chronic lumbar back pain. He had received a diagnosis of gout 3 years prior to this presentation yet was not on any urate-lowering therapy. The patient received febuxostat 80 mg and colchicine 0.3 mg once daily and underwent DECT to assess baseline monosodium urate (MSU) burden. At baseline, MSU deposits were seen in the hands, elbows, feet, knees, and lumbar spine including the left L5-S1 facet joint encroaching onto the neural foramen. After 2.5 years of treatment, serum urate level was within the target range (< 360 µmol/L), and the patient underwent a follow-up DECT that revealed almost full resolution of MSU deposition in the spine, including the MSU-burdened facet joint and neural foramen in the lumbar spine, in addition to all the affected peripheral joints. This case is the first report of radiological evidence of nearly complete resolution of MSU deposits in spinal gout on DECT after urate-lowering therapy treatment, which demonstrates the utility of this imaging modality as a non-invasive investigational point-of-care imaging modality for mapping treatment response and identifying the etiology of back pain in a patient with chronic tophaceous spinal gout.

Underreported and underrecognized: a comprehensive imaging review of breast injury.

Breast injury is commonly encountered yet it remains significantly underreported. Injury to the breast may arise from either primary mechanisms or secondary or iatrogenic mechanisms. Primary mechanisms of breast injury include blunt force, seat-belt, penetrating, and thermal injury. Secondary or iatrogenic mechanisms of breast injury include breast biopsy or intervention as well as operative intervention and cardiopulmonary resuscitation. The severity of breast injury arising from these mechanisms is broad, ranging from breast contusion to avulsion. Sequelae of breast injury include fat necrosis and Mondor's disease. Radiologists play an integral role in the evaluation and management of breast injury both in the acute and non-acute settings. In the acute setting, radiologists must be able to recognize breast injury arising from primary mechanisms or iatrogenic or secondary mechanisms and to identify rare but potentially life-threatening complications promptly to ensure timely, appropriate management. In the non-acute setting, radiologists must be able to discern the sequalae of breast injury from other processes to prevent potentially unnecessary further evaluation and intervention. Nonetheless, though breast injury is commonly encountered there remain few guidelines and a lack of established recommendations for the evaluation and management of breast injury. We provide a comprehensive multi-modality imaging review of breast injury arising in the acute setting as well as the sequela of breast injury arising in the non-acute setting. Moreover, we provide an overview of the management of breast injury.

Dual Energy Computed Tomography Collagen Material Decomposition for Detection of Lumbar Spine Disc Extrusion and Sequestration: A Comparative Study With Greyscale Computed Tomography.

Purpose: To assess value of dual energy computed tomography (DECT) collagen material decomposition algorithm when combined with standard computed tomography (CT) in detection of lumbar disc extrusion and sequestration. Materials and Methods: Retrospective analysis of all patients with acute low back pain who had a diagnosis of lumbar spine disc extrusion and/or sequestration on Magnetic Resonance Imaging (MRI) (reference standard), and had undergone non-contrast DECT of the lumbar spine within 60 days of the MRI. Age and sex-matched control patients (n = 42) were included. Patients were grouped into standard, grey-scale CT only group and standard CT + DECT tendon images group. Two double-blinded radiologists reviewed both groups for presence of extrusion or sequestration. They also rated their diagnostic confidence on Likert 5-point scale. McNemar Chi-square test was used to compare diagnostic accuracy, unpaired t-test to compare reviewers diagnostic confidence, and Cohen's k (kappa) test for interobserver agreement. Results: The combined group showed higher overall sensitivity (96.6% vs 87.2%), specificity (99% vs 95.4%), and diagnostic accuracy (98.7% vs 94.5%) with a lower false positive rate (1.1% vs 4.6%). McNemar Chi-square test confirmed statistical significance (P = .03 and P = .02 for Reviewers R1 and R2, respectively). The mean diagnostic confidence was also significantly higher on combined group (R1: 3.74 ± 1.1 vs 3.47 ± 1.15 (P < .01) and R2: 3.91 ± 1.15 vs 3.72 ± 1.16 [mean ± SD] (P = .02)). Conclusion: Utilizing MRI as a reference standard, DECT tendon application combined with standard CT increases the sensitivity, specificity, and accuracy of detection of lumbar spine disc extrusion and sequestration, when compared to standard CT alone.

Machine Learning Model for Chest Radiographs: Using Local Data to Enhance Performance.

PURPOSE: To develop and assess the performance of a machine learning model which screens chest radiographs for 14 labels, and to determine whether fine-tuning the model on local data improves its performance. Generalizability at different institutions has been an obstacle to machine learning model implementation. We hypothesized that the performance of a model trained on an open-source dataset will improve at our local institution after being fine-tuned on local data. METHODS: In this retrospective, institutional review board approved study, an ensemble of neural networks was trained on open-source datasets of chest radiographs for the detection of 14 labels. This model was then fine-tuned using 4510 local radiograph studies, using radiologists' reports as the gold standard to evaluate model performance. Both the open-source and fine-tuned models' accuracy were tested on 802 local radiographs. Receiver-operator characteristic curves were calculated, and statistical analysis was completed using DeLong's method and Wilcoxon signed-rank test. RESULTS: The fine-tuned model identified 12 of 14 pathology labels with area under the curves greater than .75. After fine-tuning with local data, the model performed statistically significantly better overall, and specifically in detecting six pathology labels (P < .01). CONCLUSIONS: A machine learning model able to accurately detect 14 labels simultaneously on chest radiographs was developed using open-source data, and its performance was improved after fine-tuning on local site data. This simple method of fine-tuning existing models on local data could improve the generalizability of existing models across different institutions to further improve their local performance.

The Role of Dual-Energy CT in Solid Organ Injury.

The liver, spleen, and kidneys are the commonest injured solid organs in blunt and penetrating trauma. The American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) is the most widely accepted system for categorizing traumatic injuries. Grading systems allow clear communication of findings between clinical teams and assign a measurable severity of injury, which directly correlates with morbidity and mortality. The 2018 revised AAST OIS emphasizes reliance on CT for accurate grading; in particular regarding vascular injuries. Dual-Energy CT (DECT) has emerged as a promising tool with multiple clinical applications already demonstrated. In this review article, we summarize the basic principles of CT attenuation to refresh the minds of our readers and we scrutinize DECT's technology as opposed to conventional Single-Energy CT (SECT). This is followed by outlining the benefits of various DECT postprocessing techniques, which authors of this article refer to as the 3Ms (Mapping of Iodine, Material decomposition, and Monoenergetic virtual imaging), in aiding radiologists to confidently assign an OIS as well as problem solve complex injury patterns. In addition, a thorough discussion of changes to the revised AAST OIS focusing on definitions of key terms used in reporting injuries is described.

Association between statins and progression of osteoarthritis features on magnetic resonance imaging in a predominantly pre-radiographic cohort: the Vancouver Longitudinal Study of Early Knee Osteoarthritis (VALSEKO): a cohort study.

BACKGROUND: To evaluate the effect of statin use on osteoarthritis (OA) incidence/progression using magnetic resonance imaging (MRI) in a population-based cohort with predominantly pre-radiographic knee OA. METHODS: A cohort aged 40-79 years with knee pain was recruited using random population sampling and followed for 7 years. Baseline exclusions were inflammatory arthritis, recent knee surgery/injury, and inability to undergo MRI. At baseline, current statin use was ascertained. Baseline and follow-up MRIs were read semi-quantitatively for cartilage damage (grade 0-4, 0/1 collapsed, 6 regions), osteophytes (grade 0-3, 8 regions), bone marrow lesions (BML) (grade 0-3, 6 regions) and effusion (grade 0-3). The primary outcome was cartilage damage incidence/progression, while secondary outcomes were incidence/progression of osteophytes, BML, and effusion, each defined as an increase by ≥1 grade at any region. To ensure population representative samples, sample weights were used. Logistic regression was used to assess the association of statin use at baseline with incidence/progression of MRI outcomes. Analyses were adjusted for sex, age, BMI, and multiple comorbidities requiring statin therapy. RESULTS: Of 255 participants evaluated at baseline, 122 completed the 7-year follow-up. Statin use was not significantly associated with progression of cartilage damage (OR 0.82; 95% CI 0.17, 4.06), osteophytes (OR 3.48; 95% CI 0.40, 30.31), BML (OR 0.61; 95% CI 0.12, 3.02), or effusion (OR 2.38; 95% CI 0.42, 13.63), after adjusting for confounders. CONCLUSION: In this population-based cohort of predominantly pre-radiographic knee OA, statins did not affect MRI incidence/progression of cartilage damage, BML, osteophytes or effusion. Therefore, statin use does not appear to affect people with pre-radiographic stages of knee OA.

Magnetic resonance imaging predictors (cartilage, osteophytes and meniscus) of prevalent and 3-year incident medial and lateral tibiofemoral knee joint tenderness and patellofemoral grind.

OBJECTIVE: To identify magnetic resonance imaging (MRI) predictors (cartilage [C], osteophytes [O] and meniscus [M] scores) of prevalent and 3-year incident medial tibiofemoral (MTF) and lateral tibiofemoral (LTF) knee joint tenderness and patellofemoral (PF) grind.  METHODS: Population-based knee pain cohort aged 40-79 was assessed at baseline (N = 255), 3- and 7-year follow-up (N = 108 × 2 = 216). COM scores were measured at 6/8/6 subregions respectively. Age-sex-BMI adjusted logistic models predicted prevalence versus relevant COM predictors (medial, lateral or patellar / trochlear groove scores). Fully adjusted models also included all relevant COM predictors. Binary generalized estimating equations models predicting 3-year incidence were also adjusted for individual follow-up time between cycles. RESULTS: Significant predictors of prevalent MTF tenderness: medial femoral cartilage (fully adjusted odds ratio [aOR] 1.84; 95% confidence interval [CI] 1.11, 3.05), female (aOR = 3.05; 1.67, 5.58), BMI (aOR = 1.53 per 5 units BMI; 1.10, 2.11). Predictors of prevalent LTF tenderness: female (aOR = 2.18; 1.22, 3.90). There were no predictors of prevalent PF grind in the fully adjusted model. However, medial patellar osteophytes was predictive in the age-sex-BMI adjusted model. There were no predictors of 3-year incident MTF tenderness. Predictors of 3-year incident LTF tenderness: female (aOR = 3.83; 1.25, 11.77). Predictors of 3-year incident PF grind: lateral patellar osteophytes (aOR = 4.82; 1.69, 13.77). In the age-sex-BMI adjusted model, patellar cartilage was also a predictor. CONCLUSION: We explored potential MRI predictors of prevalent and 3-year incident MTF/LTF knee joint tenderness and PF grind. These findings could guide preemptive strategies aimed at reducing these symptoms in the present and future (3-year incidence).

The Effect of Mask Wearing on the Accuracy of Radiology Reports in an Academic Hospital Setting.

PURPOSE: In response to the pandemic, some public health agencies recommend the wearing of surgical masks in indoor spaces including radiology common reporting rooms. We aim to demonstrate whether mask wearing may lead to increased errors incidence in radiology reports. MATERIALS AND METHODS: Our prospective studywas conveyed in 2 parts. Firstly, the participants were surveyed if they believed that mask affected dictation. Then participants performed a dictation: they read artificial radiology reports using a commercial voice recognition (VR) system. They performed this task 5 times, each time donning a different mask in random order: a surgical mask, surgical visor, N-95, combination of 2 surgical masks and no mask. Error rates were compared with the Friedman test followed by pairwise Wilcoxon with bootstrapping. Multivariate Poisson regression was performed to test for interaction effects between potential predictors. RESULTS: 52 members of an academic radiology department participatedin the study (January - March 2021) . 65.4% of survey participants did not think or were not sure whether mask wearing could affect dictation process. Treating the no-mask condition as baseline, our study found that mean error rates significantly increased up to 2 times the baseline rate when a surgical mask, surgical visor, N-95 or a combination of 2 masks was donned (p < 0.0001). No significant differences in error rates were found between the different mask types (p > 0.05). Error rates were higher for participants with shorter VR training time (p < 0.0001) or who were non-native English speakers (p < 0.0001). There were no interaction effects between mask type, VR training time or English nativity, suggesting these variables to be independent predictors for error rate. Academic rank did not significantly affect the error rate. CONCLUSION: radiologists underestimate the influence of masks on dictation accuracy. mask wearing may lead to significant increase in dictational errors.

Impact of 24/7 Onsite Emergency Radiology Staff Coverage on Emergency Department Workflow.

PURPOSE: Assess the impact of 24/7/365 emergency trauma radiology (ETR) coverage on Emergency Department (ED) patient flow in an urban, quaternary-care teaching hospital. METHODS: Patient ED visit and imaging information were extracted from the hospital patient care information system for 2008 to 2018. An interrupted time-series approach with a comparison group was used to study the impact of 24/7/365 ETR on average monthly ED length of stay (ED-LOS) and Emergency Physician to disposition time (EP-DISP). Linear regression models were fit with abrupt and permanent interrupts for 24/7/365 ETR, a coefficient for comparison series and a SARIMA error term; subgroup analyses were performed by patient arrival time, imaging type and chief complaint. RESULTS: During the study period, there were 949,029 ED visits and 739,796 diagnostic tests. Following implementation of 24/7/365 coverage, we found a significant decrease in EP-DISP time for patients requiring only radiographs (-29 min;95%CI:-52,-6) and a significant increase in EP-DISP time for major trauma patients (46 min;95%CI:13,79). No significant change in patient throughput was observed during evening hours for any patient subgroup. For overnight patients, there was a reduction in EP-DISP for patients with symptoms consistent with stroke (-78 min;95%CI:-131,-24) and for high acuity patients who required imaging (-33 min;95%CI:-57,-10). Changes in ED-LOS followed a similar pattern. CONCLUSIONS: At our institution, 24/7/365 in-house ETR staff radiology coverage was associated with improved ED flow for patients requiring only radiographs and for overnight stroke and high acuity patients. Major trauma patients spent more time in the ED, perhaps reflecting the required multidisciplinary management.

Emergency Radiology: Evolution, Current Status, and Future Directions.

Emergency Radiology is a clinical practice and an academic discipline that has rapidly gained increasing global recognition among radiology and emergency/critical care departments and trauma services around the world. As with other subspecialties, Emergency Radiology practice has a unique scope and purpose and presents with its own unique challenges. There are several advantages of having a dedicated Emergency Radiology section, perhaps most important of which is the broad clinical skillset that Emergency Radiologists are known for. This multi-society paper, representing the views of Emergency Radiology societies in Canada and Europe, outlines several value-oriented contributions of Emergency Radiologists and briefly discusses the current state of Emergency Radiology as a subspecialty.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

OBJECTIVES: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. METHODS: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning-based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. RESULTS: Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. CONCLUSIONS: Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. KEY POINTS: • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)-based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments.

The Impact of Implementing 24/7 Cardiac CTA Interpretation in the Emergency Department on Patient Care: Retrospective Analysis of a Single-Center Experience.

OBJECTIVE. The objective of our study was to provide insight on the diagnostic validity of cardiac CTA (CCTA) to identify obstructive coronary artery disease (CAD) and patients who require urgent intervention, compared with those who require same-admission coronary catheterization (CC), and to help elucidate the necessity of a 24/7 CCTA service. MATERIALS AND METHODS. We retrospectively reviewed 658 consecutive CCTA examinations performed of emergency department (ED) patients who presented with acute chest pain from October 1, 2013, to February 28, 2018. Patients were categorized by CAD severity on CCTA. Using same-admission CC as the reference standard, we assessed CCTA's validity to identify obstructive disease using PPV, NPV, sensitivity, and specificity and CCTA's validity to identify patients who require urgent intervention. The added value of the CCTA findings of subendocardial hypoattenuation and wall motion abnormality was evaluated. CCTA examinations were categorized on the basis of the time of day when scanning was performed. RESULTS. The PPV, NPV, and sensitivity of CCTA to diagnose obstructive CAD were 0.87, 0.79, and 0.95, respectively. Nine percent of the scanned patients underwent percutaneous coronary intervention (PCI) or were referred for urgent coronary artery bypass grafting (CABG). The presence of obstructive CAD on CCTA has a PPV of 0.73 to identify patients deemed to be at higher acute coronary syndrome (ACS) risk to warrant urgent PCI or CABG. Wall motion abnormality increased the PPV to 1.0; subendocardial attenuation increased the PPV to 0.9. The NPV and sensitivity were 0.89 and 0.97, respectively. Of the CCTA examinations, 54% were performed outside regular working hours. Of the patients who received urgent interventions, 62% underwent CCTA examinations performed outside regular working hours. CONCLUSION. CCTA provides high correlation with CC, helps identify individuals with high ACS risk, and is further strengthened by functional analysis; 24/7 CCTA service is warranted.

Preventing and Mitigating Radiology System Failures: A Guide to Disaster Planning.

Disaster planning is a core facet of modern health care practice. Owing to complex infrastructure requirements, radiology departments are vulnerable to system failures that may occur in isolation or during a disaster event when the urgency for and volume of imaging examinations increases. Planning for systems failures helps ensure continuity of service provision and patient care during an adverse event. Hazards to which a radiology department is vulnerable can be identified by applying a systematic approach with recognized tools such as the Hazard, Risk, and Vulnerability Analysis. Potential critical weaknesses within the department are highlighted by the Failure Mode and Effects Analysis tool. Recognizing the potential latent conditions and active failures that may impact systems allows implementation of strategies to prevent failure or to build resilience and mitigate the effects if they happen. Inherent system resilience to an adverse event can be estimated, and the ability of a department to operate during a disaster and the subsequent recovery can be predicted. The main systems at risk in a radiology department are staff, structure, stuff (supplies and/or equipment), and software, although individual issues and solutions within these are department specific. When medical imaging or examination interpretation needs cannot be met in the radiology department, the use of portable imaging modalities and teleradiology can augment the disaster response. All phases of disaster response planning should consider both sustaining operations and the transition back to normal function. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

Virtual monochromatic reconstructions of dual energy CT in abdominal trauma: optimization of energy level improves pancreas laceration conspicuity and diagnostic confidence.

PURPOSE: Pancreatic injury is associated with significant morbidity and mortality. Pancreatic lacerations can be challenging to identify as the pancreas is not scanned at peak enhancement in most trauma CT protocols. This study qualitatively and quantitively assessed pancreatic lacerations with virtual monoenergetic dual-energy CT (DE CT) to establish an optimal energy level for visualization of pancreatic lacerations. METHODS: Institutional review board approval was obtained. We retrospectively examined 17 contrast-enhanced CT studies in patients with blunt trauma with MRCP, ERCP, or surgically proven pancreatic lacerations. All studies were performed in our Emergency Department from 2016 to 2019 with a 128 slice dual-source DE CT scanner. Conventional 120 kVp and noise-optimized virtual monoenergetic imaging (VMI) datasets were created. VMI energy levels were constructed from 40 to 100 keV in 10 keV increments and analyzed quantitatively and qualitatively. Pancreatic laceration attenuation, background parenchymal attenuation, and noise were calculated. Qualitative assessment was performed by two independent readers. RESULTS: The optimal CNR for the assessment of pancreatic lacerations was observed at VMI-40 in comparison with standard reconstructions and the remaining VMI energy levels (p = 0.001). Readers reported improved contrast resolution, diagnostic confidence, and laceration conspicuity at VMI at 40 keV (p = 0.016, p = 0.002, and p = 0.0012 respectively). However, diagnostic acceptability and subjective noise were improved on conventional polyenergentic images (p = 0.0006 and p = 0.001 respectively). CONCLUSION: Dual energy CT at VMI-40 maximizes the CNR of pancreatic laceration, improves diagnostic confidence, and increases laceration conspicuity.

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attempted to discover efficient approaches to enhance the provision of quality patient care. There are increasing expectations for radiology practices to deliver a dedicated emergency radiology service providing 24/7/365 on-site attending radiologist coverage. Emergency radiologists (ERs) are pressed to meet the demand of increased imaging volume, provide accurate reports, maintain a lower proportion of discrepancy rate, and with a rapid report turnaround time of finalized reports. Thus, rendering the radiologists overburdened. The demand for an increased efficiency in providing quality care to acute patients has led to the emergence of artificial intelligence (AI) in the field. AI can be used to assist emergency and trauma radiologists deal with the ever-increasing imaging volume and workload, as AI methods have typically demonstrated a variety of applications in medical image analysis and interpretation, albeit most programs are in a training or validation phase. This article aims to offer an evidence-based discourse about the evolving role of artificial intelligence in assisting the imaging pathway in an emergency and trauma radiology department. We hope to generate a multidisciplinary discourse that addresses the technical processes, the challenges in the labour-intensive process of training, validation and testing of an algorithm, the need for emphasis on ethics, and how an emergency radiologist's role is pivotal in the execution of AI-guided systems within the context of an emergency and trauma radiology department. This exploratory narrative serves the present-day health leadership's information needs by proposing an AI supported and radiologist centered framework depicting the work flow within a department. It is suspected that the use of such a framework, if efficacious, could provide considerable benefits for patient safety and quality of care provided. Additionally, alleviating radiologist burnout and decreasing healthcare costs over time.

Impact of 24/7/365 Attending Radiologist Coverage on the Turnaround Time in an Emergency and Trauma Radiology Department.

OBJECTIVE: To study the impact of 24/7/365 attending radiologist coverage on the turnaround time (TAT) of trauma and nontrauma cases in an emergency and trauma radiology department. PATIENTS AND METHODS: This was a retrospective chart review in which TAT of patients coming to the emergency department between 2 periods: (1) December 1, 2012, to September 30, 2013, and (2) January 1, 2017, to January 30, 2018, and whose reports were read by an attending emergency and trauma radiologist was noted. RESULTS: The 24/7/365 radiology coverage was associated with a significant reduction in TAT of computed tomography reports, and the time reduction was comparable between trauma and nontrauma cases. In adjusted models, the extension of radiology coverage was associated with an average of 7.83 hours reduction in overall TAT (95% confidence interval [CI]: 7.44-8.22) for reports related to trauma, in which 2.73 hours were due to reduction in completion to transcription time (TC; 95% CI: 2.53-2.93), and 5.10 hours were due to reduction in transcription to finalization time (TF; 95% CI: 4.75-5.44). For reports related to nontrauma cases, 24/7/365 coverage was associated with an average of 6.07 hours reduction in overall TAT (95% CI: 3.54-8.59), 2.91 hours reduction in TC (95% CI: 1.55-4.26), and 3.16 hours reduction in TF (95% CI: 0.90-5.42). CONCLUSION: Our pilot study demonstrates that the implementation of on-site 24/7/365 attending emergency radiology coverage at a tertiary care center was associated with a reduced TAT for trauma and nontrauma patients imaging studies. Although the magnitude and precision of estimates were slightly higher for trauma cases as compared to nontrauma cases. Trauma examinations stand to benefit the most from 24/7/365 attending level radiology coverage.

Impact of 24/7/365 Attending Radiologist Coverage on the Turnaround Time of Trauma-Related Imaging Studies Being Finalized Within 48 Hours in an Emergency Radiology Department, Offering a Perspective from a Tertiary Care Hospital.

OBJECTIVE: To offer an evidence-based account of the effect of 24/7/365 attending radiologist coverage on the turnaround time (TAT) of trauma-related radiographs finalized within 48 hours of exam completion, drawing data from an emergency radiology department of a tertiary care hospital in Vancouver, British Columbia. MATERIALS AND METHODS: This was a retrospective chart review, where TATs of imaging studies for a sample of trauma patients, who had visited the emergency department of the Vancouver General Hospital between two time periods, January 1 to September 30, 2013, and January 1 to September 30, 2017, were noted. RESULTS: In models adjusted for patient's age, sex, and seasonality, the 24/7/365 attending radiologist coverage was associated with an average of 19.1 (95% confidence interval [CI]: 18.7-19.4) hours of reduction in time from exam completion to report finalization by an attending radiologist. Approximately 11.3 (95% CI: 18.7-19.4) hours was due to reduction in time from exam completion to preliminary diagnosis of reports. When the impact of the increased number of radiology staff in 2017 was removed in the analysis, the overall TAT was reduced by 13.3 (95% CI: 13.0-13.6) hours and the time from exam completion to preliminary report was reduced by 7.8 (95% CI: 7.6-8.1) hours. LIMITATION: Since we have used a simple random sample (SRS) for this research, this study does not describe the burden of reports that are finalized in the emergency and trauma radiology department during the given time periods. CONCLUSION: Our pilot study demonstrates that the implementation of 24/7/365 attending radiology coverage significantly reduces TAT for finalized radiology reports of all modalities of trauma imaging studies in an emergency and trauma radiology department. POLICY IMPLICATION: This research serves the contemporary health-care administration, policymaking information needs by providing the evidence for significantly reduced TAT of finalized radiology reports from a Canadian perspective.