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Chest CT Angiography for Acute Aortic Pathologic Conditions: Pearls and Pitfalls

Ko, Jane P; Goldstein, Jonathan M; Latson, Larry A; Azour, Lea; Gozansky, Elliott K; Moore, William; Patel, Smita; Hutchinson, Barry
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.
PMID: 33646903
ISSN: 1527-1323
CID: 4801202

Coil embolization to successfully treat right ventricular to pulmonary artery conduit injury during transcatheter interventions

Kerstein, Jason S.; Kreutzer, Jacqueline; Gozansky, Elliott K.; Trucco, Sara M.
ISSN: 1058-9813
CID: 4894172

Reply to "Opening Up Resident Education During the Coronavirus Disease (COVID-19) Pandemic and Beyond" [Letter]

Gozansky, Elliott K; Moore, William H
PMID: 32755222
ISSN: 1546-3141
CID: 4565612

Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation

Moore, William; Ko, Jane; Gozansky, Elliott
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.
PMID: 32141963
ISSN: 1536-0237
CID: 4340882

SARS-CoV-2 From the Trenches: A Perspective From New York City

Gozansky, Elliott K; Moore, William H
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.
PMID: 32301630
ISSN: 1546-3141
CID: 4401812

Artificial Intelligence in Cardiothoracic Radiology

Auffermann, William F; Gozansky, Elliott K; Tridandapani, Srini
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.
PMID: 30779669
ISSN: 1546-3141
CID: 4028132

Not black or white, but brown: A common finding explained! [Case Report]

Jones, Matthew; Rodriguez, Yasser; Patel, Sandeep; Gozansky, Elliott; Soman, Prem
PMID: 26797920
ISSN: 1532-6551
CID: 4028112

Erratum to: Not black or white, but brown: A common finding explained!

Jones, Matthew; Rodriguez, Yasser; Patel, Sandeep; Gozansky, Elliott; Soman, Prem
PMID: 26883779
ISSN: 1532-6551
CID: 4028122

Aortic arch dissection: a controversy of classification

Lempel, Jason K; Frazier, Aletta Ann; Jeudy, Jean; Kligerman, Seth J; Schultz, Randall; Ninalowo, Hammed A; Gozansky, Elliott K; Griffith, Bartley; White, Charles S
Aortic dissections originating in the ascending aorta and descending aorta have been classified as type A and type B dissections, respectively. However, dissections with intimal flap extension into the aortic arch between the innominate and left subclavian arteries are not accounted for adequately in the widely used Stanford classification. This gap has been the subject of controversy in the medical and surgical literature, and there is a tendency among many radiologists to categorize such arch dissections as type A lesions, thus making them an indication for surgery. However, the radiologic perspective is not supported by either standard dissection classification or current clinical management. In this special report, the origin of dissection classification and its evolution into current radiologic interpretation and surgical practice are reviewed. The cause for the widespread misconception about classification and treatment algorithms is identified. Institutional review board approval and waiver of informed consent were obtained as part of this HIPAA-compliant retrospective study to assess all aortic dissection studies performed at the University of Maryland Medical Center, Baltimore between 2010 and 2012 to determine the prevalence of arch dissections. Finally, a unified classification system that reconciles imaging interpretation and management implementation is proposed.
PMID: 24617732
ISSN: 1527-1315
CID: 4028102

Mapping the binding of the N-terminal extracellular tail of the CXCR4 receptor to stromal cell-derived factor-1alpha

Gozansky, Elliott K; Louis, John M; Caffrey, Michael; Clore, G Marius
The solution structure of monomeric stromal cell-derived factor-1alpha (SDF-1alpha), the natural ligand for the CXCR4 G-coupled receptor, has been solved by multidimensional heteronuclear NMR spectroscopy. The structure has a characteristic chemokine fold and is in excellent agreement with the individual subunits observed in the crystal structures of dimeric SDF-1alpha. Using various peptides derived from the N-terminal extracellular tail of the CXCR4 receptor, we show that the principal determinants of binding reside in the N-terminal 17 residues of CXCR4, with a major contribution from the first six residues. From 15N/1HN chemical shift pertubation studies we show that the interaction surface on SDF-1alpha is formed by the undersurface of the three-stranded antiparallel beta-sheet bounded by the N-terminal loop on one side and the C-terminal helix on the other. This surface overlaps with but is not identical to that mapped on several other chemokines for the binding of equivalent peptides derived from their respective receptors.
PMID: 15588815
ISSN: 0022-2836
CID: 4028092