Faculty Bibliography

BACKGROUND:Ecchordosis physaliphora (EP) is a congenital, uniformly asymptomatic, hamartomatous lesion of the primitive notochord. Herein we report, to our knowledge, the first credible case report of unprovoked intra-sphenoidal rupture resulting in recurrent pneumocephalus and cerebrospinal fluid (CSF) leak, definitively captured over serial imaging during clinical and radiologic surveillance. CASE DESCRIPTION/METHODS:A 68-year old woman with Marfan syndrome presented to the Emergency Department with the worst headache of life. Imaging demonstrated extensive pneumocephalus and revealed a small, dorsal midline clival lesion consistent with EP and a trans-sphenoidal defect. Remote imaging encounters confirmed typical EP without pneumocephalus or cortical defect, and an uneventful clinical course years preceding presentation. Over the ensuing months during neurosurgical follow-up, the patient reported recurrent headaches, imbalance, and unprovoked clear rhinorrhea. Further imaging demonstrates an apparently enlarging trans-sphenoidal defect which was managed by endoscopic trans-nasal resection and nasoseptal flap. Pathologic evaluation confirmed the diagnosis of EP and chronic dural defect. CONCLUSIONS:This represents, to our knowledge, the first unambiguous example of spontaneous EP rupture and recurrent pneumocephalus captured over serial imaging. The case further underscores rare, but potentially significant complications of EP and highlights management options. BACKGROUND:. Herein we report, to our knowledge, the first documented spontaneous rupture of EP resulting in recurrent pneumocephalus, credibly captured over serial radiologic surveillance. CLINICAL PRESENTATION/METHODS:A 68 year-old woman with history of hypertension, hyperlipidemia, and Marfan syndrome presented to the Emergency Department reporting the "worst headache of her life" after engaging in an interpersonal dispute the evening preceding presentation.

OBJECTIVE:To investigate the incidence and spectrum of neuroimaging findings and their prognostic role in hospitalized COVID-19 patients in New York City. METHODS:This is a retrospective cohort study of 3218 COVID-19 confirmed patients admitted to a major healthcare system (three hospitals) in New York City between March 1, 2020 and April 13, 2020. Clinical data were extracted from electronic medical records, and particularly data of all neurological symptoms were extracted from the imaging reports. Four neuroradiologists evaluated all neuroimaging studies for acute neuroimaging findings related to COVID-19. RESULTS:14.1% of admitted COVID-19 patients had neuroimaging and this accounted for only 5.5% of the total imaging studies. Acute stroke was the most common finding on neuro-imaging, seen in 92.5% of patients with positive neuro-imaging studies, and present in 1.1% of hospitalized COVID-19 patients. Patients with acute large ischemic and hemorrhagic stroke had much higher mortality risk adjusted for age, BMI and hypertension compared to those COVID-19 patients without neuroimaging. (Odds Ratio 6.02 by LR; Hazard Ratio 2.28 by CRR). CONCLUSIONS:Our study demonstrates acute stroke is the most common neuroimaging finding among hospitalized COVID-19 patients. Detection of an acute stroke is a strong prognostic marker of poor outcome. Our study also highlights the fact there is limited use of neuroimaging in these patients due to multiple logistical constraints.

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+ aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~ 1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation)

Every year in North America, approximately 3 million patients are evaluated for spinal injury. Of blunt trauma patients presenting to the emergency department, 3% to 4% will have a cervical spine injury, and up to 18% will suffer a thoracolumbar spine injury. Failure to identify an unstable spine injury can lead to devastating outcomes.

Spinal dural fistulas (SDAVFs) occasionally arise from the same segmental artery as the radiculomedullary branch to the anterior spinal artery. In such cases, selective fistula embolization that does not endanger the anterior spinal artery is not possible, and surgical fistula disconnection is recommended. We present an exceptional case in which rational embolization strategy of SDAVF was feasible because of separate origins from a common segmental artery pedicle of the ventral radiculomedullary artery and the dorsal radicular artery branch supplying the fistula.