Faculty Bibliography

Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Here, we report brain imaging features in 11 critically ill COVID-19 patients with persistently depressed mental status who underwent MRI between April 5-25, 2020 at our institution. These features include, 1) Confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients), and 2) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in 7 of 11 patients). We also discuss potential pathogeneses.

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.

BACKGROUND: To develop evidence-based treatment guidelines for Chiari malformation type 1 (CM-1), preoperative prognostic indices capable of stratifying patients for comparative trials are needed. OBJECTIVE: To develop a preoperative Chiari Severity Index (CSI) integrating the clinical and neuroimaging features most predictive of long-term patient-defined improvement in quality of life (QOL) after CM-1 surgery. METHODS: We recorded preoperative clinical (eg, headaches, myelopathic symptoms) and neuroimaging (eg, syrinx size, tonsillar descent) characteristics. Brief follow-up surveys were administered to assess overall patient-defined improvement in QOL. We used sequential sequestration to develop clinical and neuroimaging grading systems and conjunctive consolidation to integrate these indices to form the CSI. We evaluated statistical significance using the Cochran-Armitage test and discrimination using the C statistic. RESULTS: Our sample included 158 patients. Sequential sequestration identified headache characteristics and myelopathic symptoms as the most impactful clinical parameters, producing a clinical grading system with improvement rates ranging from 81% (grade 1) to 58% (grade 3) (P = .01). Based on sequential sequestration, the neuroimaging grading system included only the presence (55% improvement) or absence (74% improvement) of a syrinx >/=6 mm (P = .049). Integrating the clinical and neuroimaging indices, improvement rates for the CSI ranged from 83% (grade 1) to 45% (grade 3) (P = .002). The combined CSI had moderately better discrimination (c = 0.66) than the clinical (c = 0.62) or neuroimaging (c = 0.58) systems alone. CONCLUSION: Integrating clinical and neuroimaging characteristics, the CSI is a novel tool that predicts patient-defined improvement after CM-1 surgery. The CSI may aid preoperative counseling and stratify patients in comparative effectiveness trials.