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Summary

This dataset consists of unenhanced chest CT images of COVID-19 infection at the point of care in an outbreak setting with NIFTI files. The images were retrospectively acquired from a single region after CT of patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.

NIFTI CT images were converted from DICOM images. CT reconstruction algorithm was soft tissue without intravenous contrast.  

Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. This data may be a useful tool and resource for developing algorithms for medical applications in COVID-19, or data analysis challenges for the scientific community.

A multidisciplinary team trained several models using portions of this data set (along with manually annotated images and other data &/or other CT’s). A classification model derived in part from portions of this data (and also from other non-TCIA data) can be found at:  https://doi.org/10.1038/s41467-020-17971-2.  Models partly derived from portions of this data (and also from other data not shown here), may be found at:  https://ngc.nvidia.com/catalog/resources/nvidia:clara:clara_ai_covid19_pipeline.

A web-based research-only model (website for research use only) has CT drag-and-drop functionality. Upload of CT yields a return email that contains results. This model is also partly derived from portions of this data (and also from other non-TCIA data):  https://marketplace.arterys.com/model/nvidiacovidCT.

Acknowledgements

The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:

  • All frontline workers and Peng An, Sheng Xu, Evrim B Turkbey, Stephanie A Harmon, Thomas H Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh,  Gianpaolo Carrafiello, Baris Turkbey, Bradford J Wood.
  • Thanks for leadership support to: John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.
  • Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.

Data Access

Data TypeDownload all or Query/Filter

Images (NIfTI, 13.59 GB)

CT

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

Image Statistics


Modalities

CT

Number of Patients

674

Number of Studies

701

Images Size (GB)13.59

Citations & Data Usage Policy

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


Data Citation

An, P., Xu, S., Harmon, S.A., Turkbey, E.B., Sanford, T.H., Amalou, A., Kassin, M., Varble, N., Blain, M., Anderson,V., Patella, F., Carrafiello, G., Turkbey, B.T., Wood, B.J.: (2020) CT Images in Covid-19 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.gqry-nc81.

Publication Citation

Link to publication below contains AI model that was only partly derived from this data, but also from other data, not present here on TCIA.

Harmon, S.A., Sanford, T.H., Xu, S., Turkbey, E.B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S.M., Bagci, U., Ierardi, A.M., Stellato, E., Plensich, G.G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R.M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B.J., Turkbey, B.(2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications. DOI: https://doi.org/10.1038/s41467-020-17971-2

Acknowledgement

The Multi-national NIH Consortium for CT AI in COVID-19.

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 2020/08/24

Data TypeDownload all or Query/Filter

Images (NIfTI, 13.59 GB)

CT



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