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  • Low-Dose CT Images of Healthy Cohort (Healthy-Total-Body CTs)


This data set includes low-dose whole body CT images and tissue segmentations of thirty healthy adult research participants who underwent PET/CT imaging on the uEXPLORER total-body PET/CT system at UC Davis. Participants included in this study were healthy adults, 18 years of age or older, who were able to provide informed written consent. The participants' age, gender, weight, height, and body mass index are also provided.

Fifteen participants underwent PET/CT imaging at three timepoints during a 3-hour period (0 minutes, 90 minutes, and 180 minutes) after PET radiotracer injection, while the remaining 15 participants were imaged at six timepoints during a 12-hour period (additionally at 360 minutes, 540 minutes, and 720 minutes). The imaging timepoint is indicated in the Series Description DICOM tag, with a value of either 'dyn', '90min', '3hr', '6hr', '9hr', or '12hr', corresponding to the delay after PET tracer injection. CT images were acquired immediately before PET image acquisition. Currently, only CT images are included in the data set from either three or six timepoints. The tissue segmentations include 37 tissues consisting of 13 abdominal organs, 20 different bones, subcutaneous and visceral fat, skeletal and psoas muscle. Segmentations were automatically generated at the 90 minute timepoint for each participant using MOOSE, an AI segmentation tool for whole body data. The segmentations are provided in NIFTI format and may need to be re-oriented to correctly match the CT image data in DICOM format.

The uEXPLORER CT scanner is an 80-row, 160 slice CT scanner typically used for anatomical imaging and attenuation correction for PET/CT. The CT scan obtained at 90 minutes was performed with 140 kVp and an average of 50 mAs for all subjects. At all other time-points (0 minutes, 180 minutes, etc.) the CT scan was obtained with 140 kVp and an average of 5 mAs. CT images were reconstructed into a 512x512x828 image matrix with 0.9766x0.9766x2.344 mm3 voxel size.

A key is provided along with the segmentations download in the Data Access table which details the organ values.


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

  • Funding for this work was provided by NIH grant R01 CA206187, which is supported by NCI, NIBIB and the Office of the Director, and by R01 CA249422.

Data Access

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to before accessing the data.

Data TypeDownload all or Query/FilterLicense

Images, (DICOM, 59 GB)


(Download requires NBIA Data Retriever)

Segmentations (NIfTI) & Segmentation Organ Values spreadsheet (zip, 81.5 MB)

Clinical Data (.xlsx, 14 KB)

Detailed Description

Image Statistics

Radiology Image Statistics



Number of Patients


Number of Studies


Number of Series


Number of Images

Images Size (GB)59

Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Selfridge, A. R., Spencer, B., Shiyam Sundar, L. K., Abdelhafez, Y., Nardo, L., Cherry, S. R., & Badawi, R. D. (2023). Low-Dose CT Images of Healthy Cohort (Healthy-Total-Body CTs) (Version 1) [Data set]. The Cancer Imaging Archive.

Publication Citation

Sundar, L. K. S., Yu, J., Muzik, O., Kulterer, O. C., Fueger, B., Kifjak, D., Nakuz, T., Shin, H. M., Sima, A. K., Kitzmantl, D., Badawi, R. D., Nardo, L., Cherry, S. R., Spencer, B. A., Hacker, M., & Beyer, T. (2022). Fully Automated, Semantic Segmentation of Whole-Body18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence. In Journal of Nuclear Medicine (Vol. 63, Issue 12, pp. 1941–1948). Society of Nuclear Medicine.

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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC.

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 TCIA's Helpdesk.

Version 1 (Current): Updated 2023/09/21

Data TypeDownload all or Query/FilterLicense

Images, (DICOM, 59 GB)


(Download requires the NBIA Data Retriever)

Segmentations (NIfTI) & Organ Value spreadsheet (zip) 

Clinical Data (.xlsx)

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