Synopsis

Perfusion and functional MRI, yielding markers such as cerebral blood volume, cerebrovascular reactivity, and functional connectivity, play an important role in the diagnosis and treatment of brain tumors. However, a major limitation is that collection of all this information requires separate scans and, in some cases, separate visits. Here we applied a novel iVas-MRI technique that provides quantitative assessment of multiple hemodynamic parameters and functional connectivity in a single scan of 9 minutes. It was found that the multi-parametric maps can reliably differentiate tumor from normal tissue and can further predict tumor grade.

Introduction

Methods

Patients: Six brain tumor patients (4 males, age 24-81yo) were scanned on 3T MRI (Philips), after IRB-approved informed consent was obtained.

iVas-MRI: A concomitant CO₂/O₂ challenge was performed while BOLD images were continuously collected⁵. The timing of the CO₂ and O₂ paradigms was optimized previously and is illustrated in Fig. 1, allowing CO₂ and O₂ levels to be modulated independently without interference from each other. The BOLD sequence used the following parameters: TR/TE=1550/21ms, 3.2×3.2x3.5mm³, scan duration=9.3min. As a comparison, breath-hold BOLD was performed to obtain an independent CVR measure⁴.

Data processing: Following the analysis method described previously⁵, BOLD images and physiological recordings of end-tidal (Et) CO₂ and O₂ traces were used to calculate maps of CVR (based on BOLD signal change to EtCO₂ change), CBV (based on BOLD signal change to EtO₂ change), and BAT (based on the time lag between physiological recordings and BOLD signal). The residual BOLD time course after factoring out the CO₂ and O₂ influences was used to estimate FC maps⁶. Ten FC networks were obtained in each patient.

Statistical analysis: Regions-of-interest (ROI) were manually drawn on tumor and contralateral healthy tissue. The ROI masks were applied to the CVR, CBV, and BAT maps to obtain regional values. For FC, the number of voxels that were significant (Z>0.4) in any of the ten FC networks was calculated for tumor and contralateral ROIs separately, and this number is referred to as voxel-count. Parametric values were compared between tumor and contralateral ROIs using paired t-tests. Correlation between tumor grade and CBV was examined. The relationship between CVR and FC voxel-count was also investigated.

Results and Discussion

Fig. 2a shows iVas parametric maps along with anatomical images from one patient with high-grade tumor (glioblastoma, WHO IV) and another patient with low-grade tumor (astrocytoma, WHO II). It can be seen that tumor regions exhibit a diminished CVR regardless of tumor grade (blue arrows). CBV in the tumor region is also lower than control region in general. However, high-grade tumor reveals ring-shaped elevation in CBV (green arrows). Note that our CBV technique is not susceptible to vascular leakage (because hemoglobin does not leak out unlike Gd). Therefore, the CBV elevation is attributed to neovascularization in the high-grade tumor.

Fig. 2b summarizes ROI results in all patients (N=6). Both CBV and CVR decreased significantly, while BAT increased significantly (p=0.02) in tumor compared to the normal-appearing contralateral region.

Next, we examined the correlation between CBV and tumor grade. A strong correlation between CBV and tumor grades was observed (r=0.83; p=0.01), as shown in Fig. 3, but this relationship was only observed when normalizing the tumor CBV with the contralateral value.

Finally, we compared FC voxel-count between tumor and contralateral region (Fig. 2b). There was no significant difference in FC voxel-count. However, we observed a significant (p<0.0001) association between FC voxel-count and CVR values (Fig. 4). That is, regions with low CVR tend to have a smaller number of voxels detected in FC networks, consistent with the notion that vascular response is the basis of the fMRI signal.

As an exploratory analysis, we compared CVR measured by iVas-MRI with that measured by breath-hold in Fig. 5. A significant correlation was observed between these two measures (p=0.003).

Conclusion

Acknowledgements

References

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