SummaryThe FDA anthropomorphic thorax phantom with 12 phantom lesions of different sizes (10 and 20 mm in effective diameter), shapes (spherical, elliptical, lobulated, and spiculated), and densities (−630,−10, and +100 HU) was scanned at Columbia University Medical Center on a 64-detector row scanner (LightSpeed VCT, GE Healthcare, Milwaukee, WI). The CT scanning parameters were 120 kVp, 100 mAs, 64x0.625 collimation, and pitch of 1.375. The images were reconstructed with the lung kernel using 1.25 mm slice thickness.
This data set was provided to TCIA for use in the National Cancer Institute's Quantiatitive Imaging Network (QIN) Lung CT Segmentation Challenge. A TCIA Analysis Result dataset was created to enable easy re-use of the complete multi-site challenge data set.
About the NCI QIN
The mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01).
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Third Party Analyses of this Dataset
TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:
Number of Participants
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|Image Size (MB)||127.5|
Supporting Documentation and Metadata
No supporting documentation is available for this collection.
Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:
Zhao, Binsheng. (2015). Data From Lung_Phantom. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.08A1IXOO
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. https://doi.org/10.1007/s10278-013-9622-7
Other Publications Using This Data
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Version 2 (Current): Updated 2020/09/25
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|DICOM Metadata Digest (CSV)|
9/25/2020: Adjusted this table and *.tcia manifest files to remove segmentations built in Multi-site collection of Lung CT data with Nodule Segmentations. DOI: https://doi.org/10.7937/K9/TCIA.2015.1BUVFJR7 . Note that the "Search" will take you to both until you uncheck the "Include third party" button.