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://wwwXXX or [DOI pubmed PMID].EMBARGOED FOR NOW / WILL PROVIDE WHEN EMBARGO LIFTED. 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 Type | Download all or Query/Filter |
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Images (NIfTI, 13.59 GB) CT | (Download requires the NBIA Data Retriever) |
Clinical data (CSV) |
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Detailed Description
Image Statistics | |
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Modalities | CT |
Number of Patients | 674 |
Number of Studies | 701 |
Images Size (GB) | 13.59 |
Citations & Data Usage Policy
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
Publication under development [in press].
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/mm/dd
Data Type | Download all or Query/Filter |
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Images (NIfTI, 13.59 GB) CT | (Requires NBIA Data Retriever.) |
Clinical Data (CSV) | Link |