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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. This COVID-19 classification model is detailed at:   https://doi.org/10.1038/s41467-020-17971-2.