Summary

This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. This dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.

This Phantom dataset can be used for Feature variability assessment due to CT imaging parameters. These phantom scans can be used to identify a subset of robust radiomic features for future clinical investigations. Using this dataset, the numerical values of radiomic features can be cross-validated by other research groups using their own feature extraction tools.

Acknowledgements



Data Access

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Images (DICOM, 30GB)

Clinical Data (CSV)

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Detailed Description

Image Statistics


Modalities

CT

Number of Patients

251

Number of Studies

251

Number of Series

251

Number of Images

57,839

Images Size (GB)30GB



Citations & Data Usage Policy 


ul Hassan MS, Zhang G, Latifi K, Ullah G, Gillies R, Moros E. Credence Cartridge Radiomics Phantom CT Scans with Controlled Scanning Approach. 2018. (DOI Coming Soon)


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 our data. At this time we are not aware of any manuscripts based on this data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

  1. Shafiq ul Hassan M, Latifi K, Zhang G, Ullah G, Gillies R and Moros E. (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer patients. Scientific Reports.

  2. Shafiq ul Hassan M, Zhang G, Hunt D, Latifi K, Ullah G, Gillies R and Moros E, ‘Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra’, J. Med. Imag. 5(1), 011013 (2017). DOI: 10.1117/1.JMI.5.1.011013

  3. Shafiq ul Hassan M, Zhang G, Latifi K, Ullah G, Hunt D, Balagurunathan Y, Abdullah M, Schabath M, Goldgof D, Mackin D, Court L, Gillies R and Moros E. (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med. Phys. 44(3), p-1050-1062 .

  4. Paul R, Shafiq ul Hassan M, Moros E, Gillies R, Hall L, Goldgof D. (2018) Stability of deep features across CT scanners and Field Of View (FOV) using a physical phantom. Proc SPIE Medical Imaging Conference, February 2018, Texas, USA


Version 1 (Current): Updated

Data TypeDownload all or Query/Filter
Images (DICOM, 30GB)

 

Clinical Data (TXT)