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  • Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population (COVID-19-AR)
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Radiology imaging  is playing an increasingly vital role in the diagnosis of COVID-19 patients and determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses.  All too frequently rural populations are underrepresented in such public collections. In fact, the literature demonstrates there is very limited data on COVID-19 outcomes in rural populations, while it is well established that such populations have differentially high expression of key comorbidities.  Similarly, while the number of genomes of the SARS-COV-2 virus are rapidly growing in public repositories, few samples represent the variants expressed in rural populations.  This gap in available data is of particular importance given that the southern United States, as of July 2020, is the most rapidly expanding  COVID-19 hot spot on earth. We have published a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19. Each patient is described by a limited set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings. These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the Genbank repository. We believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the global research community.


We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • The University of Arkansas for Medical Sciences (UAMS) Translational Research Institute, Department of Radiology, Department of Biomedical Informatics and Department of Surgery, Little Rock, Arkansas, USA.

TCIA COVID-19 Datasets

Additional datasets and information about TCIA efforts to support COVID-19 research can be found here.

Data Access

Data TypeDownload all or Query/Filter

Images (DICOM, 19.0 GB)


(Download requires the NBIA Data Retriever)

Clinical data (CSV)

Viral Genomes Genbank repository (web)

   accession no. MT766907: USA/AR-UAMS001/2020

   accession no. MT766908: USA/AR-UAMS002/2020

Click the Versions tab for more info about data releases.

Please contact  with any questions regarding usage.

Detailed Description

Image Statistics



Number of Patients


Number of Studies


Number of Series


Number of Images


Images Size (GB)19.0

Citations & Data Usage Policy

Users of this data must abide by the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Desai, S., Baghal, A., Wongsurawat, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G.D., Nolan, T., Powell, T., Sexton, K., Bennett, W., Prior, F. (2020). Data from Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population [Data set]. The Cancer Imaging Archive. DOI:

Publication Citation

Data Descriptor in review.


This project has been funded in whole or in part with federal funds from the National Center for Advancing Translational Sciences  UL1 TR003107 and  the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F. 

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 1 (Current): Updated 2020/07/13

Data TypeDownload all or Query/Filter

Images (DICOM, 19.0 GB)

Clinical Data (CSV)
 Viral Genomes Genbank repository (web)

   accession no. MT766907: USA/AR-UAMS001/2020

   accession no. MT766908: USA/AR-UAMS002/2020

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