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Excerpt

This dataset consists of unenhanced chest CT images of COVID-19 infection , from 652 patients in NIfTI file format. The images were retrospectively acquired at the point of care in an outbreak setting, in NIfTI file format. The images were retrospectively acquired from a single region after CT of patients with Reverse in China after Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.NIfTI CT images were converted from DICOM images. CTs were performed with intravenous contrast and a soft tissue reconstruction algorithm. 

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.  CT exams were performed with intravenous contrast and a soft tissue reconstruction algorithm.  The DICOM images were subsequently converted into NIfTI format. 

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 with drag-and-drop functionality can be found at https://marketplace.arterys.com/model/nvidiacovidCTUpload 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. This COVID-19 classification model is detailed at:  and is detailed at: https://doi.org/10.1038/s41467-020-17971-2.

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Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/Filter

Images (NIfTI, 12.71 GB)

CT

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Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities

CT

Number of Patients

632

Number of Studies

650
Images Size (GB)12.71



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia license 4 international


Info
titleData 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. DOI: https://doi.org/10.7937/tcia.2020.gqry-nc81.


Info
titlePublication 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.


Info
titleAcknowledgement

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


Info
titleTCIA 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.


Localtab
titleVersions

Version 1 (Current): Updated 2020/08/27

Data TypeDownload all or Query/Filter

Images (NIfTI, 12.71 GB)CT