Annotated Clinical MRIs and Linked Metadata of Patients with Acute Stroke, Baltimore, Maryland, 2009-2019 (ICPSR 38464)

Version Date: May 16, 2022 View help for published

Principal Investigator(s): View help for Principal Investigator(s)
Andreia V. Faria, Johns Hopkins University

https://doi.org/10.3886/ICPSR38464.v1

Version V1 ()

  • V5 [2022-12-12]
  • V4 [2022-12-07] unpublished
  • V3 [2022-12-05] unpublished
  • V2 [2022-10-19] unpublished
  • V1 [2022-05-16] unpublished

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This is a collection of 2,888 clinical MRIs of patients admitted at a National Stroke Center, over ten years, with clinical diagnosis of acute or early subacute stroke. The collection includes diverse MRI modalities and protocols. The infarct core was manually defined in the diffusion weighted images; the images are provided in native subject space and in standard space (MNI), in Neuroimaging Informatics Technology Initiative (NIfTI) format. The data format and organization follows Brain Imaging Data Structure (BIDS) guidelines. The collection includes diverse metadata, comprised of demographic information, basic clinical profile (NIH Stroke Scale/Score (NIHSS), hospitalization duration, blood pressure at admission, BMI, and associated health conditions), and expert description of the acute lesion. This resource provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

Faria, Andreia V. Annotated Clinical MRIs and Linked Metadata of Patients with Acute Stroke, Baltimore, Maryland, 2009-2019. [distributor], 2022-05-16. https://doi.org/10.3886/ICPSR38464.v1

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2009-01-01 -- 2019-12-31
2009-01-01 -- 2019-12-31
  1. There is no dataset file for this fast release. The data will be released separately as an update when it is available.

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The purpose of the study was to provide high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

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clinical MRIs of patients admitted at a National Stroke Center

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2022-05-16

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