Faculty Bibliography

Coronavirus disease 2019 (COVID-19) is a serious public health crisis and can have neurologic manifestations. This is a retrospective observational case series performed March 1-31, 2020, at New York University Langone Medical Center campuses. Clinical and imaging data were extracted, reviewed, and analyzed. Two hundred forty-two patients with COVID-19 underwent CT or MRI of the brain within 2 weeks after the positive result of viral testing (mean age, 68.7 ± 16.5 years; 150 men/92 women [62.0%/38.0%]). The 3 most common indications for imaging were altered mental status (42.1%), syncope/fall (32.6%), and focal neurologic deficit (12.4%). The most common imaging findings were nonspecific white matter microangiopathy (134/55.4%), chronic infarct (47/19.4%), acute or subacute ischemic infarct (13/5.4%), and acute hemorrhage (11/4.5%). No patients imaged for altered mental status demonstrated acute ischemic infarct or acute hemorrhage. White matter microangiopathy was associated with higher 2-week mortality (P < .001). Our data suggest that in the absence of a focal neurologic deficit, brain imaging in patients with early COVID-19 with altered mental status may not be revealing.

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)

Professional use of social media continues to increase. We analyzed Twitter use of our own American Society of Neuroradiology (ASNR) 2018 annual meeting, reviewing all Twitter posts (3020 tweets from 523 participants) containing the hashtag #ASNR18 from May 21, 2018, to June 12, 2018, extracting the transcripts from Symplur. Then, each tweet was categorized by the role of user, type of tweet, and topic. The dominant user category was neuroradiologist/radiologist (63%). The keynote address, "The Radiology Renaissance: Shaping the Future of Healthcare," presented by Andy DeLao @Cancergeek was the most frequently tweeted topic (10%). Comment on a session was the major type of tweet. When we compared the data with a similar analysis in 2014, our data analysis showed a growth in the use of Twitter in only 4 years.

BACKGROUND AND PURPOSE/OBJECTIVE:Few studies have shown MR imaging features and ADC correlating with molecular markers and survival in patients with glioma. Our purpose was to correlate MR imaging features and ADC with molecular subtyping and survival in adult diffuse gliomas. MATERIALS AND METHODS/METHODS:promoter methylation, and overall survival. RESULTS:wild-type glioma. Other MR imaging features were not statistically significant predictors of survival. CONCLUSIONS:wild-type gliomas.

INTRODUCTION: We hypothesize that surgical decompression for Chiari malformation type 1 (CM-1) is associated with statistically significant decrease in tonsillar pulsatility and that the degree of pulsatility can be reliably assessed regardless of the experience level of the reader. METHODS: An Institutional Review Board (IRB)-approved Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study was performed on 22 children with CM-1 (8 males; mean age 11.4 years) who had cardiac-gated true-FISP sequence and phase-contrast cerebrospinal fluid (CSF) flow imaging as parts of routine magnetic resonance (MR) imaging before and after surgical decompression. The surgical technique (decompression with or without duraplasty) was recorded for each patient. Three independent radiologists with different experience levels assessed tonsillar pulsatility qualitatively and quantitatively and assessed peritonsillar CSF flow qualitatively. Results were analyzed. To evaluate reliability, Fleiss kappa for multiple raters on categorical variables and intra-class correlation for agreement in pulsatility ratings were calculated. RESULTS: After surgical decompression, the degree of tonsillar pulsatility appreciably decreased, confirmed by t test, both qualitatively (p values <0.001, <0.001, and 0.045 for three readers) and quantitatively (amount of decrease/p value for three readers 0.7 mm/<0.001, 0.7 mm/<0.001, and 0.5 mm/0.022). There was a better agreement among the readers in quantitative assessment of tonsillar pulsatility (kappa 0.753-0.834), compared to qualitative assessment of pulsatility (kappa 0.472-0.496) and qualitative assessment of flow (kappa 0.056 to 0.203). Posterior fossa decompression with duraplasty led to a larger decrease in tonsillar pulsatility, compared to posterior fossa decompression alone. CONCLUSION: Tonsillar pulsatility in CM-1 is significantly reduced after surgical decompression. Quantitative assessment of tonsillar pulsatility was more reliable across readers than qualitative assessments of tonsillar pulsatility or CSF flow.