Purpose To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations...

There has been extensive interest in promoting gender equality within radiology, a predominately male field. In this study, our aim was to quantify gender representation in neuroradiology faculty rankings and determine any related factors that may contribute to any such disparity. We evaluated the academic and administrative faculty members of neuroradiology divisions for all on-line listed programs in the US and Canada. After excluding programs that did not fulfill our selection criteria, we generated a short list of 85 US and 8 Canadian programs. We found 465 faculty members who met the inclusion criteria for our study. We used Elsevier's SCOPUS for gathering the data pertaining to the publications, H-index, citations, and tenure of the productivity of each faculty member. Gender disparity was insignificant when analyzing academic ranks. There are more men working in neuroimaging relative to women (χ2 = 0.46; P = .79). However, gender disparity was highly significant for leadership positions in neuroradiology (χ2 = 6.76; P = .009). The median H-index was higher among male faculty members (17.5) versus female faculty members (9). Female faculty members have odds of 0.84 compared with male faculty members of having a higher H-index, adjusting for publications, citations, academic ranks, leadership ranks, and interaction between gender and publications and gender and citations (9). Neuroradiology faculty members follow the same male predominance seen in many other specialties of medicine. In this study, issues such as mentoring, role models, opportunities to engage in leadership/research activities, funding opportunities, and mindfulness regarding research productivity are explored.

Identifying reasons for the low proportion of female and underrepresented minority radiology residency applicants, despite a highly diverse pool of medical students, would be key to increasing thei... Background There have been growing efforts nationally and institutionally toward diversity in radiology. Purpose To analyze sex and racial and ethnic diversity over time (2010–2019) for the various...

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. “Time is brain” is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

BACKGROUND AND PURPOSE: The long-term history and management of unruptured intracranial aneurysms is not well understood. Our aim was to determine current practice patterns in the management of unruptured intracranial aneurysms, especially regarding imaging surveillance for conservatively managed aneurysms of this type. MATERIALS AND METHODS: An on-line survey was designed to examine physician practice and preference regarding the management of small unruptured intracranial aneurysms (≤7 mm in diameter). The survey was circulated to members of the American Society of Neuroradiology. Participation was voluntary, and all responses were anonymous. RESULTS: A total of 227 individual survey responses were obtained and included in the analysis with 54.6% (124/227) from diagnostic neuroradiologists (practicing >50% neuroradiology) and one-third (29%) from neurointerventional radiologists. One hundred seventy-three of 227 responded that routine, periodic imaging surveillance would be appropriate for conservatively managed unruptured intracranial aneurysms, and 84% of respondents recommended surveillance frequency of at least once a year. Fifty-nine percent favored indefinite, life-long follow-up for small unruptured intracranial aneurysms, and a similar number of respondents favored noncontrast MR angiography for aneurysm follow-up. Significant heterogeneity was found in size measurements used to assess aneurysms and criteria used to define growth on surveillance imaging. CONCLUSIONS: The natural history of intracranial aneurysms is not well-understood. A large proportion of incidentally detected, unruptured aneurysms are small (<7 mm). The survey results show significant heterogeneity in practice even among neuroradiologists and underlies the need to standardize imaging practice. Further studies are needed to assess the optimal frequency and duration of surveillance imaging for unruptured intracranial aneurysms. The criteria used to measure aneurysms and define growth on imaging also need to be standardized.

BACKGROUND AND PURPOSE: Neuropsychiatric systemic lupus erythematosus refers to central and peripheral nervous system involvement, which may occur secondary to antineuronal antibodies crossing the blood-brain barrier that preferentially target cells in the hippocampus leading to abnormal hypermetabolism and atrophy. Thus, we hypothesized that alterations in BBB permeability, detected on dynamic contrast-enhanced MR imaging, occur in the hippocampus in patients with systemic lupus erythematosus before development of neuropsychiatric systemic lupus erythematosus. MATERIALS AND METHODS: Six patients with systemic lupus erythematosus without neuropsychiatric systemic lupus erythematosus and 5 healthy controls underwent dynamic contrast-enhanced MR imaging with postprocessing into BBB permeability parameters (K^trans and Ve) and CBF. Standardized methods selected ROI sampling of the abnormal brain regions detected on FDG-PET. The mean and SD of K^trans, Ve, and CBF were calculated. Linear regression and nonparametric Spearman rank correlation analyses of K^trans and Ve with CBF were performed. Dynamic contrast-enhanced curves and the area under the curve were generated for each brain region. Student t test comparisons were performed. RESULTS: Quantitative data revealed that patients with systemic lupus erythematosus have statistically increased K^trans (P < .001) and Ve (P < .001) compared with controls. In patients with systemic lupus erythematosus, statistically significant positive correlations were seen between K^trans (P < .001) and Ve (P < .001) with CBF. Furthermore, the mean area under the curve revealed statistically increased BBB permeability in the hippocampus (P = .02) compared with other brain regions in patients with systemic lupus erythematosus compared with controls. CONCLUSIONS: These initial findings are proof-of-concept to support the hypothesis that patients with systemic lupus erythematosus have increased BBB permeability, specifically in the hippocampus, compared with other brain regions. These findings may advance our understanding of the underlying pathophysiology affecting the brain in autoimmune diseases.