Unreimbursed Costs of Multidisciplinary Conferences to a Radiology Department: A Prospective Analysis at an Academic Medical Center.

PURPOSE: Multidisciplinary conferences (MDCs) are important for clinical care but are unreimbursed and can be time-consuming for radiologists to prepare for and present. The purpose of this single-center, prospective, survey-based study is to measure the per-conference time and total time radiologists devote to MDCs at a single academic medical center. Secondary objectives are to determine the source of radiologist preparation time, and calculate the per conference and overall radiology departmental costs of MDC participation. METHODS: A prospective survey was performed to capture all radiology preparation and presentation time for MDCs in a 3-month period, which was then annualized. Total cost was calculated on the basis of Association of Administrators in Academic Radiology survey data for nonchair academic radiologist compensation plus a 30% fringe-benefit rate. RESULTS: The survey response rate was 86.9%. A total of 3,358 hours were devoted annually to MDCs, which represents time equivalent to 1.9 full-time equivalents or $1,155,152 in unreimbursed radiology departmental costs. Per-MDC total preparation and presentation time was 2.7 hours, at an annual cost of $46,440 for each weekly MDC. Radiologists used a combination of personal time (49.7%), academic time (42%), and/or clinical time (35.4%) to prepare for MDCs. Radiologists devoted a mean of 47.9 hours (1.2 weeks) of time per annum to MDCs. CONCLUSIONS: Radiologist time devoted to MDCs at the survey institution was substantial, and preparation time was drawn disproportionately from personal and academic time, which may have negative implications for burnout, recruitment and retention, and academic productivity unless it is effectively mitigated.

MR Angiography for Aortic Diseases.

Aortic pathologic conditions represent diverse disorders, including aortic aneurysm, acute aortic syndrome, traumatic aortic injury, and atherosclerosis. Given the nonspecific clinical features, noninvasive imaging is critical in screening, diagnosis, management, and posttherapeutic surveillance. Of the commonly used imaging modalities, including ultrasound, computed tomography, and MR imaging, the final choice often depends on a combination of factors: acuity of clinical presentation, suspected underlying diagnosis, and institutional practice. Further research is needed to identify the potential clinical role and define appropriate use criteria for advanced MR applications such as four-dimenional flow to manage patients with aortic pathologic conditions.

MR Angiography: Contrast-Enhanced Acquisition Techniques.

Contrast-enhanced MR angiography (CE-MRA) is a frequently used MR imaging technique for evaluating cardiovascular structures. In many ways, it is similar to contrast-enhanced computed tomography (CT) angiography, except a gadolinium-based contrast agent (instead of iodinated contrast) is injected. Although the physiological principles of contrast injection overlap, the technical factors behind enhancement and image acquisition are different. CE-MRA provides an excellent alternative to CT for vascular evaluation and follow-up without requiring nephrotoxic contrast and ionizing radiation. This review describes the physical principles, limitations, and technical applications of CE-MRA techniques.

MRA as the Preferred Test for Pulmonary Embolism During the Iodinated Contrast Media Shortage of 2022: A Single-Center Experience.

BACKGROUND. Closure of a GE Healthcare facility in Shanghai, China, in 2022 disrupted the iodinated contrast media supply. Technologic advances have addressed limitations associated with the use of pulmonary MRA for diagnosis of pulmonary embolism (PE). OBJECTIVE. The purpose of this study was to describe a single institution's experience in the use of pulmonary MRA as an alternative to CTA for the diagnosis of PE in the general population during the iodinated contrast media shortage in 2022. METHODS. This retrospective single-center study included all CTA and MRA examinations performed to exclude PE from April 1 through July 31 (18 weekly periods) in 2019 (before the COVID-19 pandemic and contrast media shortage), 2021 (during the pandemic but before the shortage), and 2022 (during both the pandemic and the shortage). From early May through mid-July of 2022, MRA served as the preferred test for PE diagnosis, to preserve iodinated contrast media. CTA and MRA reports were reviewed. The total savings in iodinated contrast media volume resulting from preferred use of MRA was estimated. RESULTS. The study included 4491 examinations of 4006 patients (mean age, 57 ± 18 [SD] years; 1715 men, 2291 women): 1245 examinations (1111 CTA, 134 MRA) in 2019, 1547 examinations (1403 CTA, 144 MRA) in 2021, and 1699 examinations (1282 CTA, 417 MRA) in 2022. In 2022, the number of MRA examinations was four (nine when normalized to a 7-day period) in week 1, and this number increased to a maximum of 63 in week 10 and then decreased to 10 in week 18. During weeks 8-11, more MRA examinations (range, 45-63 examinations) than CTA examinations (range, 27-46 examinations) were performed. In 2022, seven patients with negative MRA underwent subsequent CTA within 2 weeks; CTA was negative in all cases. In 2022, 13.9% of CTA examinations (vs 10.3% of MRA examinations) were reported as having limited image quality. The estimated 4-month savings resulting from preferred use of MRA in 2022, under the assumption of uniform simple linear growth in CTA utilization annually and a CTA dose of 1 mL/kg, was 27 L of iohexol (350 mg I/mL). CONCLUSION. Preferred use of pulmonary MRA for PE diagnosis in the general population helped to conserve iodinated contrast media during the 2022 shortage. CLINICAL IMPACT. This single-center experience shows pulmonary MRA to be a practical substitute for pulmonary CTA in emergency settings.

Defining the Roles and Responsibilities for the Vice Chair for Academic Affairs/Faculty Development in Radiology.

RATIONALE AND OBJECTIVES: To inform the development of a job description for Vice-Chairs for academic affairs (VCAA), members of the Alliance of Leaders in Academic Affairs in Radiology (ALAAR) were surveyed to better understand their current job responsibilities and how they would ideally allocate their professional time. MATERIALS AND METHODS: Based on a survey of 33 university-affiliated radiology departments and discussion among ALAAR members, the authors developed a detailed job description for the VCAA. The 21-question survey was composed and validated by experts in the field. It was distributed to all members of ALAAR via email with an electronic link and was open for 5 months. Results of the survey were tabulated, and a job description was crafted to represent the foundational roles of academic affairs leaders in radiology. RESULTS: The response rate for institutions represented in ALAAR was 73% (33/45). All participants reported that they practiced in a university-affiliated institution. Faculty size varied from ≤49 (30.3%, 10/33), 50-99 faculty (24.2%, 8/33), and ≥100 faculty members (45.5%, 15/33). Only 24% of survey respondents had a detailed job description at the time of hire. More than 40% attested to significant oversight over faculty development programs (45%), mentorship programs (42%, and promotions (45%). Respondents ideally want increased oversight (defined as >10%) over exit interviews, faculty awards, promotions, onboarding, recruitment and hiring, and wellness programming. CONCLUSION: The aspirational mission of the VCAA is to oversee components of sequential stages in the professional lifecycle of faculty members but a common job description for this role is lacking.

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis. PURPOSE: To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes. METHODS: Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users. The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma. RESULTS: For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is -0.25 mg/mL, and the agreement limits are [-0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [-0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12, 16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When significant errors are present in the estimated material basis images, Deep-QC can capture these errors and provide pixel-wise error maps to inform users whether the DL results are trustworthy. CONCLUSIONS: The Deep-En-Chroma network provides a new pathway to estimating the clinically relevant material basis images from single-kV CT data and the Deep-QC module to inform end-users of the accuracy of the DL material basis images in practice.

Short-, Mid-, and Long-term Strategies to Manage the Shortage of Iohexol.

The recent iohexol shortage has precipitated disruptions in a pharmaceutical supply chain critical to radiologic imaging and has impacted global availability of iodinated contrast media (ICM). The shortage has created a national crisis in radiology departments, curtailing their ability to provide health care to patients who need contrast-enhanced examinations. Radiology departments are familiar with crisis management after more than 2 years of clinical and operational disruptions associated with the COVID-19 pandemic. The iohexol and subsequent ICM shortage has short-term (weeks), midterm (months), and long-term (years) implications. The purpose of this report is to provide strategies for dealing with the shortage in the near term and to discuss long-term issues and potential solutions to supply chain problems impacting radiology departments.

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Quantitative lung perfusion blood volume using dual energy CT-based effective atomic number (Zeff ) imaging.

BACKGROUND: Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Zeff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects. PURPOSE: (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images. METHODS: The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Zeff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Zeff as PB V Z eff ( x ) = a Z eff ß ( x ) + b , where a, b, and ß are three constants. Namely, quantitative PB V Z eff is determined by Zeff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBViodine ) derived from two-material decompositions and the proposed PB V Z eff method. RESULTS: Among patients with pulmonary emboli, the proposed PB V Z eff maps clearly show the perfusion defects while the PBViodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist. CONCLUSION: Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects.

Evaluation for Myocarditis in Competitive Student Athletes Recovering From Coronavirus Disease 2019 With Cardiac Magnetic Resonance Imaging.

Importance: The utility of cardiac magnetic resonance imaging (MRI) as a screening tool for myocarditis in competitive student athletes returning to training after recovering from coronavirus disease 2019 (COVID-19) infection is unknown. Objective: To describe the prevalence and severity of cardiac MRI findings of myocarditis in a population of competitive student athletes recovering from COVID-19. Design, Setting, and Participants: In this case series, an electronic health record search was performed at our institution (University of Wisconsin) to identify all competitive athletes (a consecutive sample) recovering from COVID-19, who underwent gadolinium-enhanced cardiac MRI between January 1, 2020, and November 29, 2020. The MRI findings were reviewed by 2 radiologists experienced in cardiac imaging, using the updated Lake Louise criteria. Serum markers of myocardial injury and inflammation (troponin-I, B-type natriuretic peptide, C-reactive protein, and erythrocyte sedimentation rate), an electrocardiogram, transthoracic echocardiography, and relevant clinical data were obtained. Exposures: COVID-19 infection, confirmed using reverse transcription-polymerase chain reaction testing. Main Outcomes and Measures: Prevalence and severity of MRI findings consistent with myocarditis among young competitive athletes recovering from COVID-19. Results: A total of 145 competitive student athletes (108 male and 37 female individuals; mean age, 20 years; range, 17-23 years) recovering from COVID-19 were included. Most patients had mild (71 [49.0%]) or moderate (40 [27.6%]) symptoms during the acute infection or were asymptomatic (24 [16.6%]). Symptoms were not specified or documented in 10 patients (6.9%). No patients required hospitalization. Cardiac MRIs were performed a median of 15 days (range, 11-194 days) after patients tested positive for COVID-19. Two patients had MRI findings consistent with myocarditis (1.4% [95% CI, 0.4%-4.9%]). Of these, 1 patient had marked nonischemic late gadolinium enhancement and T2-weighted signal abnormalities over multiple segments, along with an abnormal serum troponin-I level; the second patient had 1-cm nonischemic mild late gadolinium enhancement and mild T2-weighted signal abnormalities, with normal laboratory values. Conclusions and Relevance: In this case series study, based on MRI findings, there was a low prevalence of myocarditis (1.4%) among student athletes recovering from COVID-19 with no or mild to moderate symptoms. Thus, the utility of cardiac MRI as a screening tool for myocarditis in this patient population is questionable.

Why physics in medicine?

Despite its crucial role in the development of new medical imaging technologies, in clinical practice, physics has primarily been involved in technical evaluation of technologies. However, this narrow role is no longer adequate. New trajectories in medicine call for a stronger role for physics in the clinic. The movement towards evidence-based, quantitative, and value-based medicine requires physicists to play a more integral role in delivering innovative precision care through the intentional clinical application of physical sciences. There are three aspects of this clinical role: technology assessment based on metrics as they relate to expected clinical performance, optimized use of technologies for patient-centered clinical outcomes, and retrospective analysis of imaging operations to ensure attainment of expectations in terms of quality and variability. These tasks fuel the drive towards high-quality, consistent practice of medical imaging that is patient-centered, evidence-based, and safe. While this particular article focuses on imaging, this trajectory and paradigm is equally applicable to the multitudes of the applications of physics in medicine.

Clinical outcomes after magnetic resonance angiography (MRA) versus computed tomographic angiography (CTA) for pulmonary embolism evaluation.

PURPOSE: To compare patient outcomes following magnetic resonance angiography (MRA) versus computed tomographic angiography (CTA) ordered for suspected pulmonary embolism (PE). METHODS: In this IRB-approved, single-center, retrospective, case-control study, we reviewed the medical records of all patients evaluated for PE with MRA during a 5-year period along with age- and sex-matched controls evaluated with CTA. Only the first instance of PE evaluation during the study period was included. After application of our exclusion criteria to both study arms, the analysis included 1173 subjects. The primary endpoint was major adverse PE-related event (MAPE), which we defined as major bleeding, venous thromboembolism, or death during the 6 months following the index imaging test (MRA or CTA), obtained through medical record review. Logistic regression, chi-square test for independence, and Fisher's exact test were used with a p < 0.05 threshold. RESULTS: The overall 6-month MAPE rate following MRA (5.4%) was lower than following CTA (13.6%, p < 0.01). Amongst outpatients, the MAPE rate was lower for MRA (3.7%) than for CTA (8.0%, p = 0.01). Accounting for age, sex, referral source, BMI, and Wells' score, patients were less likely to suffer MAPE than those who underwent CTA, with an odds ratio of 0.44 [0.24, 0.80]. Technical success rate did not differ significantly between MRA (92.6%) and CTA (90.5%) groups (p = 0.41). CONCLUSION: Within the inherent limitations of a retrospective case-controlled analysis, we observed that the rate of MAPE was lower (more favorable) for patients following pulmonary MRA for the primary evaluation of suspected PE than following CTA.

Why Physics in Medicine?

Despite its crucial role in the development of new medical imaging technologies, in clinical practice, physics has primarily been involved in the technical evaluation of technologies. However, this narrow role is no longer adequate. New trajectories in medicine call for a stronger role for physics in the clinic. The movement toward evidence-based, quantitative, and value-based medicine requires physicists to play a more integral role in delivering innovative precision care through the intentional clinical application of physical sciences. There are three aspects of this clinical role: technology assessment based on metrics as they relate to expected clinical performance, optimized use of technologies for patient-centered clinical outcomes, and retrospective analysis of imaging operations to ensure attainment of expectations in terms of quality and variability. These tasks fuel the drive toward high-quality, consistent practice of medical imaging that is patient centered, evidence based, and safe. While this particular article focuses on imaging, this trajectory and paradigm is equally applicable to the multitudes of the applications of physics in medicine.

Bits and bytes: the future of radiology lies in informatics and information technology.

Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology. KEY POINTS: • Clinical decision support ensures consistent and appropriate resource utilization. • Big data enables correlation of health information across multiple domains. • Data mining advances the quality of medical decision-making. • Business analytics allow radiologists to maximize the benefits of imaging resources.