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  • QIBA Anthropomorphic Abdominal Phantom CT Scans (QIBA-CT-Liver-Phantom)
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Summary

 This database contains a collection of three sets of CT scan images acquired from an anthropomorphic abdominal phantom with removable liver inserts. The anthropomorphic phantoms were designed by a group of scientists from FDA and Columbia University Medical Center and custom manufactured by QRM (Moehrendorf, Germany).

Data sets #1 (AP phantom) & #2 (PVP phantom): Two liver inserts, each containing 19 embedded synthetic lesions with known volumes of varying diameter (6–40 mm), shape, contrast (10–65 HU), and density (homogenous and mixed) were designed to have liver parenchyma and lesion CT values simulating arterial phase (AP phantom) and portal venous phase (PVP phantom) imaging, respectively. The two phantoms were scanned using two 64-slice multi-detector helical CT scanners (GE 750HD and Siemens Biograph mCT) across a wide range of abdominal imaging protocols, including three effective mAs (50, 100, 250), two pitches (GE/Siemens: 1.375 and 0.983/1.35 and 1.0), four slice thicknesses (GE/Siemens: 0.625, 1.25, 2.5, and 5 mm/0.6, 1.5, 3, and 5 mm), and two convolution kernels (GE/Siemens: Standard and Soft/B20f and B30f). Two repeated scans were performed for each protocol and for each phantom.

Dataset #3 (IR phantom): One non-uniform liver insert was filled with an in-house made gelatin-based background, and varied concentrations of gelatin, salt, and water were used to mimic normal liver tissue and focal fat at the arterial phase. Ten spherical lesions of five sizes (20, 14, 10, 7, and 5 mm in diameter) and two radiodensities (95 and 110 HU) were placed on the border between the fatty and normal parenchyma. The phantom was imaged with a CT scanner (GE 750HD) across a set of imaging protocols, including three effective mAs (50, 100, 250), three slice thicknesses (1.25, 2.5, and 5 mm) and five reconstruction algorithms (standard, AISR30%, AISR50%, ASIR70%, and VEO). Five repeated scans were performed for each protocol. 

Acknowledgements

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

  • This work was supported in part by a sub-award of RSNA QIBA through NIH Grant No. HHSN268201300071C. This work was also supported, in part, by a Critical Path grant from the U.S. Food and Drug Administration. 

Data Access

Data TypeDownload all or Query/Filter

Images (DICOM, 53.9 GB)

   

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Click the Versions tab for more info about data releases.

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Detailed Description

Image Statistics


Modalities

CT

Number of Patients

3

Number of Studies

5

Number of Series

684

Number of Images

102,022

Images Size (GB)53.9

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

Zhao, B., Li, Q., Liang, Y., Yang, H., Gavrielides, M. A., Schwartz, L. H., Sullivan, D. C., & Petrick, N. A. (2021). QIBA Anthropomorphic Abdominal Phantom CT Scans [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.RMV0-9Y95

PVP and AP Publication Citation

Li, Q., Liang, Y., Huang, Q., Zong, M., Berman, B., Gavrielides, M. A., Schwartz, L. H., Zhao, B., & Petrick, N. (2016). Volumetry of low-contrast liver lesions with CT: Investigation of estimation uncertainties in a phantom study. Medical Physics, 43(12), 6608–6620. https://doi.org/10.1118/1.4967776  

IR Publication Citation

Li, Q., Berman, B. P., Schumacher, J., Liang, Y., Gavrielides, M. A., Yang, H., Zhao, B., & Petrick, N. (2017). The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study. In S. G. Armato & N. A. Petrick (Eds.), Medical Imaging 2017: Computer-Aided Diagnosis. SPIE. https://doi.org/10.1117/12.2255743

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. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

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Version 1 (Current): 2021/04/27

Data TypeDownload all or Query/Filter

Images (DICOM, 53.9 GB)

   

(Download requires the NBIA Data Retriever)




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