Faculty Bibliography

Discrepancy between computed tomography (CT) and transthoracic echocardiography (TTE) regarding pericardial effusion (PEff) size is common, but there is limited data regarding the correlation between these 2 imaging methods. The aim of this study is to examine the real-world concordance of observed PEff size between CT and TTE. We performed a retrospective analysis of all imaging reports available from 2013 to 2019 and identified patients with a PEff who underwent both a chest CT and TTE within a 24-hour period. We evaluated the agreement between CT and TTE in assessing PEff size. Of 1,118 patients included in the study, mean age was 66 (±17 years) and 54% were female. The median time interval between the 2 studies was 9.4 hours (interquartile range 3.5 to 16.6). Patients within a half-grade or full-grade of agreement were 71.9% and 97.2%, respectively. The mean difference in grade of agreement (TTE minus CT) between the 2 imaging methods was -0.1 (±0.6, p <0.0001). CT was more likely to report a higher grade (i.e. larger PEff size) when compared with TTE (261 patients vs 157 patients, p <0.001). The weighted kappa was 0.73 (95% confidence interval 0.69 to 0.76). After excluding patients with trace/no effusion, 42.3% and 94.1% of patients' studies were within a half-grade or full-grade of agreement, respectively. Of the 18 patients who had large discrepancies, 9 patients had loculated effusions, 2 patients had large pleural effusions, and 6 patients had suboptimal TTEs images. In conclusion, TTE and CT showed relatively strong agreement in estimation of PEff size, with CT sizes larger than TTE, on average. Large discrepancies in size may be related to reduced image quality, large pleural effusions, and loculated PEff.

While there have been many descriptions of characteristic motion findings in left bundle branch block (LBBB), there are few published descriptions of such findings in right bundle branch block (RBBB). The purpose of this study was to assess the frequency of particular regional motion findings in cardiac magnetic resonance imaging (CMR) studies of patients with RBBB, compared with normal subjects. We focused on three distinctive motion patterns that can be seen in RBBB during early systole: delayed apex-ward motion of the RV base, "reverse septal flash", and "basal bulge". The presence and relative magnitude of these findings were independently scored by four experienced observers, in 3-chamber and 4-chamber CMR cines, for both normal subjects and patients with RBBB. These motion patterns were found to be strongly associated with the presence of RBBB. While only moderately sensitive, they were quite specific for RBBB, when present. In particular, with ROC analysis, a combined feature set of the findings in the 4-chamber view had an area under the curve of 0.81.This previously undescribed set of RBBB-associated early-systolic regional motion features (delayed apex-ward motion of the RV base, "reverse septal flash", and "basal bulge") is strongly suggestive of RBBB when present, particularly in the 4-chamber view. Although here evaluated with CMR, it is also likely to be associated with RBBB when seen with other cardiac imaging modalities.

OBJECTIVE:This study aimed to determine the prevalence of axillary and subpectoral (SP) lymph nodes after ipsilateral COVID-19 vaccine administration on chest computed tomography (CT). METHODS:Subjects with chest CTs between 2 and 25 days after a first or second vaccine dose, December 15, 2020, to February 12, 2021, were included. Orthogonal measures of the largest axillary and SP nodes were recorded by 2 readers blinded to vaccine administration and clinical details. A mean nodal diameter discrepancy of ≥6 mm between contralateral stations was considered positive for asymmetry. Correlation with the side of vaccination, using a Spearman rank correlation, was performed on the full cohort and after excluding patients with diseases associated with adenopathy. RESULTS:Of the 138 subjects (81 women, 57 men; mean [SD] age, 74.4 ± 11.7 years), 48 (35%) had asymmetrically enlarged axillary and/or SP lymph nodes, 42 (30%) had ipsilateral, and 6 (4%) had contralateral to vaccination ( P = 0.003). Exclusion of 29 subjects with conditions associated with adenopathy showed almost identical correlation, with asymmetric nodes in 32 of 109 (29%) ipsilateral and in 5 of 109 (5%) contralateral to vaccination ( P = 0.002). CONCLUSIONS:Axillary and/or SP lymph nodes ipsilateral to vaccine administration represents a clinical conundrum. Asymmetric nodes were detected at CT in 30% of subjects overall and 29% of subjects without conditions associated with adenopathy, approximately double the prevalence rate reported to the Centers for Disease Control and Prevention by vaccine manufacturers. When interpreting examinations correlation with vaccine administration timing and site is important for pragmatic management.

Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.

Chest CT angiography (CTA) is essential in the diagnosis of acute aortic syndromes. Chest CTA quality can be optimized with attention to technical parameters pertaining to noncontrast imaging, timing of contrast-enhanced imaging, contrast material volume, kilovolt potential, tube-current modulation, and decisions regarding electrocardiographic-gating and ultra-fast imaging, which may affect the accurate diagnosis of acute aortic syndromes. An understanding of methods to apply to address suboptimal image quality is useful, as the accurate identification of acute aortic syndromes is essential for appropriate patient management. Acute aortic syndromes have high morbidity and mortality, particularly when involving the ascending aorta, and include classic aortic dissection, penetrating atherosclerotic ulcer, and acute intramural hematoma. An understanding of the pathogenesis and distinguishing imaging features of acute aortic syndromes and aortic rupture and some less common manifestations is helpful when interpreting imaging examinations. Related entities, such as ulcerated plaque, ulcerlike projections, and intramural blood pools, and mimics, such as vasculitis and aortic thrombus, are important to recognize; knowledge of these is important to avoid interpretive pitfalls. In addition, an awareness of postsurgical aortic changes can be useful when interpreting CTA examinations when patient history is incomplete. The authors review technical considerations when performing CTA, discuss acute aortic syndromes, and highlight diagnostic challenges encountered when interpreting aortic CTA examinations. ^©RSNA, 2021.

Artificial intelligence (AI) is a broad field of computational science that includes many subsets. Today the most widely used subset in medical imaging is machine learning (ML). Many articles have focused on the use of ML for pattern recognition to detect and potentially diagnose various pathologies. However, AI algorithm development is now directed toward workflow management. AI can impact patient care at multiple stages of their imaging experience and assist in efficient and effective scheduling, imaging performance, worklist prioritization, image interpretation, and quality assurance. The purpose of this manuscript was to review the potential AI applications in radiology focusing on workflow management and discuss how ML will affect cardiothoracic imaging.

OBJECTIVE. The response to coronavirus disease (COVID-19) is evolving in New York City. We would like to share our experiences, thoughts, and perspectives on coping with the pandemic. CONCLUSION. This article presents experiences that are meant to help foster discussion as the wave of COVID-19 continues. Thoughtful leadership and careful continuous communication will help us minimize anxiety and frustration during this difficult time.

OBJECTIVE:The goal of this article is to examine some of the current cardiothoracic radiology applications of artificial intelligence in general and deep learning in particular. CONCLUSION/CONCLUSIONS:Artificial intelligence has been used for the analysis of medical images for decades. Recent advances in computer algorithms and hardware, coupled with the availability of larger labeled datasets, have brought about rapid advances in this field. Many of the more notable recent advances have been in the artificial intelligence subfield of deep learning.