Summary
The standardization of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume (CBV) inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, a digital reference object (DRO) was developed using physiological and kinetic parameters derived from a patient database, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach. The primary, intended use of the DRO is to validate image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas [1,2]. The DRO datasets have also been used as part of the QIN-challenge titled “DSC-DRO Challenge” to evaluate multisite rCBV consistency [3], and for systematic assessment of multi-echo DSC-MRI [4].
DRO development
To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2 * changes observed across multiple glioblastomas, the DRO’s input parameters were trained using DSC-MRI data from 23 glioblastomas (40,000 voxels). The DRO’s ability to produce reliable signals across combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. To achieve an excellent agreement between the DRO and in vivo data the training and validation process required a DRO consisting of 10 000 unique voxels.
Application
Users can use the DRO to investigate the influence of DSC-MRI acquisition and post-processing methods on CBV accuracy and as a benchmark for perfusion analysis algorithms.
Data Access
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| Data Type | Download all or Query/Filter | License |
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| Images (DICOM, 4.1 GB) |
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| Data description (PDF) |
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Detailed Description
Image Statistics | |
|---|---|
Modalities | MR |
Number of Participants | 3 |
Number of Studies | 31 |
Number of Series | 364 |
Number of Images | 47164 |
| Images Size (GB) | 4.1 |
The field of view contains four regions of interest (ROI) per image. The settings for
- Static field strength,
- TR,
- TE,
- Flip angle,
- and contrast dose
are included in the series description of each synthetic timecourse, as in Table 1 of the publication .
Note, do not sort by filename (chart at left), sort by Acquisition Number (chart at right). It should trace like the image on the right. Osirix, IBNeuro, ImageJ do this natively. If you are using Matlab or python please use DICOM header AcquisitionNumber (0020,0012) to reorder the files; using the file names could lead to a wrong signal profile.
Citations & Data Usage Policy
Data Citation
Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2020). GBM-DSC-MRI-DRO [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2020.RMWVZWIX
Publication Citation
Semmineh, N. B., Stokes, A. M., Bell, L. C., Boxerman, J. L., & Quarles, C. C. (2017). A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. In Tomography (Vol. 3, Issue 1, pp. 41–49). MDPI AG. https://doi.org/10.18383/j.tom.2016.00286
Acknowledgement
This work was performed at the Barrow Neurological Institute, with support from R01 CA158079.
We thank Dr. Kathleen Schmainda (Medical College of Wisconsin) for access to dual-echo data used for validation.
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). https://doi.org/10.1007/s10278-013-9622-7
Additional Publications related to this work:
- Semmineh, N. B., Bell, L. C., Stokes, A. M., Hu, L. S., Boxerman, J. L., & Quarles, C. C. (2018). Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object. In American Journal of Neuroradiology (Vol. 39, Issue 11, pp. 1981–1988). American Society of Neuroradiology (ASNR). https://doi.org/10.3174/ajnr.a5827
- Bell, L. C., Semmineh, N., An, H., Eldeniz, C., Wahl, R., Schmainda, K. M., Prah, M. A., Erickson, B. J., Korfiatis, P., Wu, C., Sorace, A. G., Yankeelov, T. E., Rutledge, N., Chenevert, T. L., Malyarenko, D., Liu, Y., Brenner, A., Hu, L. S., Zhou, Y., … Quarles, C. C. (2019). Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). In Tomography (Vol. 5, Issue 1, pp. 110–117). MDPI AG. https://doi.org/10.18383/j.tom.2018.00041
- Stokes, A. M., Semmineh, N. B., Nespodzany, A., Bell, L. C., & Quarles, C. C. (2019). Systematic assessment of multi‐echo dynamic susceptibility contrast MRI using a digital reference object. In Magnetic Resonance in Medicine (Vol. 83, Issue 1, pp. 109–123). Wiley. https://doi.org/10.1002/mrm.27914
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 the TCIA Helpdesk.
Version 1 (Current): Updated 2020/06/25
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Added new subjects.
