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QIN-BRAIN-DSC-MRI

The Cancer Imaging Archive

QIN-BRAIN-DSC-MRI | Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs

DOI: 10.7937/K9/TCIA.2016.5DI84Js8 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Brain Human 49 MR Low & High Grade Glioma 30.76GB Limited, Complete 2019/08/28

Summary

This collection contains MR images of both low and high grade glial brain lesions. Data includes post-contrast T1w images with co-registered volumes of dynamic susceptibility contrast (DSC) MR images in DICOM format. Binary regions of interest are also included, in DICOM format, of the lesion, arterial input function, normal appearing white matter, normal appearing cerebral cortex, and whole brain. The data was provided to help facilitate research activities of the National Cancer Institute's (NCI's) Quantitative Imaging Network (QIN). This collection was supported by Grant U01 CA176110.

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) PAR-11-150.

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 help@cancerimagingarchive.net before accessing the data.

Version 3: Updated

Lifted “Limited Access” embargo.

Title Data Type Format Access Points Subjects Studies Series Images License
Images MR DICOM
Download requires NBIA Data Retriever
49 52 349 116,778 TCIA Restricted

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Kathleen M Schmainda, Melissa A Prah, Jennifer M Connelly, Scott D Rand. (2016). Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs [ Dataset ] . The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2016.5DI84Js8

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection: Medical College of Wisconsin, Milwaukee, Wisconsin. Special thanks to Kathleen Schmainda, Ph.D. and Melissa Prah, Department of Radiology, Division of Imaging Science.

Other Publications Using this Data

TCIA maintains a list of publications that leverage our data. If you have a publication you’d like to add, please contact the TCIA Helpdesk.

Publication Citation

Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC. (2018). Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. American Journal of Neuroradiology, 39(6), 1008–1016. DOI: 10.3174/ajnr.a5675

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. https://doi.org/10.1007/s10278-013-9622-7

Previous Versions

Version 2: Updated 2016/03/29

Added 6 new series, 1 source DSC for each of the 6 MRI platforms.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever

Version 1: Updated 2015/08/25

Title Data Type Format Access Points Studies Series Images License
Images DICOM