The standardization of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) has 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 , and for systematic assessment of multi-echo DSC-MRI .
- Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Tomography 2017;3:41–9. doi:10.18383/j.tom.2016.00286.
- Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI using a population-based digital reference object. Am J Neuroradiol 2018. doi:10.3174/ajnr.A5827.
- Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, et al. 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). Tomogr (Ann Arbor, Mich) 2019. doi:10.18383/j.tom.2018.00041.
- Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2020. doi:10.1002/mrm.27914.
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.
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.
The DRO data is separated in to two collections corresponding to two magnetic field strengths (3T and 1.5T). Each collection contains 15 folders corresponding to three TRs (1s, 1.5s, 2s) and five contrast agent dosing schemes (pre none bolus full, pre quarter bolus three-quarter, pre half bolus half, pre quarter bolus full, and pre full bolus full). Each of the 15 folders contains 12 sub-folders representing a combination of three flip angles (30o, 60o, and 90o) and four echo times (20ms, 30ms, 40ms, and 50ms). Within each of the 12 sub-folders a single slice DSC-MRI signal time series of 3 minutes sampled at intervals of the corresponding TR values are given. The slice contains four ROIs described in figure 1. In addition to the DRO data masks for each of the four ROIs are provided.
Figure 1 (top right of page): (A) Represents the tumor region containing 10000 voxels. (B) The corresponding tumor region with no CA leakage contamination (to be used for expected calculations). (C) Representative normal appearing white matter (WM) containing 2000 voxels. (D) Region representing the arterial input function (AIF). For a given parameter combination (TR=1.5s, TE=30ms, Flip angle= 60o, CA dosing = pre none bolus full, and B0= 3T), Figure 2 demonstrate example signal time course for all four ROI.
Figure 2 (below): Example signal time course for a voxel within each of the four ROIs.
- 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.
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Additional Resources for this DatasetThe NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
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|Images Size (GB)||4.5|
The field of view contains four regions of interest (ROI) per image. The settings for
- Static field strength,
- 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
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
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
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
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