Versions Compared
Key
- This line was added.
- This line was removed.
- Formatting was changed.
Summary
The standardization and broad-scale integration of DSC-MRI has 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 post-processing algorithms. Users can use the DRO to investigate the influence of DSC-MRI acquisition and post-processing methods on CBV accuracy, and determination of the impact of DSC-MRI methodology choices on sample size requirements and the assessment of treatment response in clinical glioblastoma trials. The DRO datasets are also being 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“DSC-MRI DRO Challenge” and this will be the primary access point the institutes use to download the their site-specific DROs.
Acquisition Protocol:
A dynamic susceptibility contrast (DSC) digital reference object (DRO) was developed in order validate image acquisition and analysis methods for accurately measuring perfusion parameters in glioblastomas. A validated computational approach for modeling DSC-MRI data served as the basis of the DRO, which is expanded using physiological and kinetic parameters derived from in vivo data and unique voxel-wise 3D tissue structures.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
T1T1 and
T2T2* 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
foracross combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training
datasetdata set was validated by comparison
towith in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas
. Each DRO (e.g. ones computed for a specific set of acquisition and contrast agent dosing schemes) contains ~10,000 unique voxels.. 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.
References
[1] 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.
[2] 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.
[3] 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.
[4] 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.
Localtab Group | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|