As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.

129Xe apparent diffusion coefficient (ADC) MRI offers an alternative to 3He ADC MRI, given its greater availability and lower cost. To demonstrate the feasibility of HP 129Xe ADC MRI, we present results from healthy volunteers (HV), chronic obstructive pulmonary disease (COPD) subjects, and age-matched healthy controls (AMC). The mean parenchymal ADC was 0.036±0.003 cm2/s for HV, 0.043±0.006 cm2/s for AMC, and 0.056±0.008 cm2/s for COPD subjects with emphysema. In healthy individuals, but not the COPD group, ADC decreased significantly in the anterior-posterior direction by ~22% (p = 0.006, AMC; 0.0059, HV), likely due to gravity-induced tissue compression. The COPD group exhibited a significantly larger superior-inferior ADC reduction (~28%) than the healthy groups (~24%) (p = 0.00018 HV; p = 3.45×10-5 AMC), consistent with smoking-related tissue destruction in the superior lung. Superior-inferior gradients in healthy subjects may result from regional differences in xenon concentration. ADC was significantly correlated with pulmonary function tests (FEV1, r=-0.77, p=0.0002; FEV1/FVC, r=-0.78, p=0.0002; DLCO/VA, r=-0.77, p=0.0002), and in healthy groups, increased with age by 0.0002 cm2/s/yr (r=0.56, p=0.02). This study shows 129Xe ADC MRI is clinically feasible, sufficiently sensitive to distinguish HV from subjects with emphysema, and detects age and posture-dependent changes.

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Purpose This retrospective review examines the incidence of pulmonary embolism (PE) during computed tomography pulmonary angiography (CTPA) exams performed in the emergency room setting of a tertiary care center over dominant periods of the ancestral, Delta, and Omicron variants of COVID-19. Materials/methods Demographic information, patient comorbidities and risk factors, vaccination status, and COVID-19 infection status were collected from patient’s charts. Incidence of PE in COVID positive patients was compared between variant waves. Subgroup analysis of vaccination effect was performed. Results CTPA was ordered in 18.3% of COVID-19 positive patients during the ancestral variant period, 18.3% during the Delta period and 17.3% during the Omicron wave. PE was seen in 15.0% of the ancestral COVID-19 variant cohort, 10.6% in the Delta COVID cohort and 9.23% of the Omicron cohort, reflecting a 41% and 60% increased risk of PE with ancestral variants compared to Delta and Omicron periods respectively. The study however was underpowered and the difference in rate of PE did not reach statistically significance ( p = 0.43 and p = 0.22). Unvaccinated patients had an 2.75-fold increased risk of COVID-associated PE during the Delta and Omicron periods ( p = .02) compared to vaccinated or recovered patients. Conclusion Vaccination reduces the risk of COVID-19 associated PE. Patients infected with the Delta and Omicron COVID-19 variants may have a lower incidence of pulmonary embolism, though a larger or multi-institution study is needed to prove definitively.