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Updated: Jun 14, 2025
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
Published on: December 9, 2010
"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic
Francesco Sanvito1,2, Jingwen Yao1,2, Nicholas S Cho1,2,3,4
1From the UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers (F.S., J.Y., N.S.C., C.R., B.M.E.), University of California Los Angeles, Los Angeles, California.
Dynamic SAGE-EPI enables simultaneous generation of cerebral blood volume and signal recovery DSC data with one acquisition. This synthetic data approach aids in harmonizing multicenter comparisons and assessing acquisition factor impacts on brain tumor imaging biomarkers.
Area of Science:
- Neuroimaging
- Medical Physics
- Radiology
Background:
- Dynamic susceptibility contrast (DSC) perfusion imaging provides critical biomarkers like normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) for brain tumor assessment.
- Current DSC protocols are optimized for either CBV or PSR, leading to acquisition-dependent measurements and complicating comparisons.
- The variability in DSC acquisition factors poses challenges for multicenter studies and consistent clinical application.
Purpose of the Study:
- To develop a method for generating "synthetic" DSC data from dual-echo gradient-echo (GE) DSC datasets using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI).
- To enable simultaneous generation of both CBV-optimized and PSR-optimized DSC data from a single acquisition.
- To assess the impact of acquisition parameters on DSC metrics and facilitate comparisons across heterogeneous external cohorts.
Main Methods:
- Thirty-eight patients with contrast-enhancing brain tumors underwent prospective dynamic SAGE-EPI imaging with a single contrast injection.
- Synthetic DSC curves were generated using Bloch equations applied to dual-echo GE data, allowing for adjustable synthetic acquisition parameters.
- The study involved generating synthetic DSC data with and without simulated preload, and analyzing the impact of acquisition factors.
Main Results:
- Dynamic SAGE-EPI successfully generated both CBV-optimized and PSR-optimized DSC datasets from a single contrast injection.
- PSR computation from guideline-compliant CBV-optimized protocols showed significant rank variations (31%-21%) within the cohort.
- Synthetic DSC facilitated comparisons, showing lower PSR in treatment-naïve glioblastoma compared to primary CNS lymphomas (p<0.0001).
- Acquisition factors significantly impacted PSR and nrCBV, but post-hoc leakage correction mitigated these dependencies.
Conclusions:
- Dynamic SAGE-EPI enables simultaneous generation of CBV- and PSR-optimized DSC data with a single acquisition and contrast injection.
- This unified approach simplifies perfusion protocols for diverse DSC applications in neuro-oncology.
- The synthetic DSC method supports harmonization of perfusion metrics across heterogeneous multicenter datasets, improving data comparability.
