Partial-volume model for determining white matter and gray matter cerebral blood volume for analysis of gliomas.

PURPOSE

To model the partial voluming of gray matter (GM) and white matter (WM) in perfusion imaging, and to use this model to estimate the cerebral blood volume (CBV) of pure WM and GM, which could then be used to normalize data across patients in preparation for analyzing tumor perfusion.

MATERIALS AND METHODS

Dynamic susceptibility contrast (DSC) perfusion imaging was performed on 20 glioma patients. The perfusion data were registered to the T1 image using rigid-body and non-rigid algorithms. The rCBV for each voxel was computed by gamma-variate fitting and then fit as a linear function of the estimated fractional WM content. The estimated CBV of pure WM was used to normalize across patients, and the resulting tumor CBV values were compared with expectations.

RESULTS

Rigid registration improved the correlation between the fractional WM content and CBV for all patients, with non-rigid registration yielding further improvements for all but two patients. The mean GM-to-WM CBV ratio was estimated at 2.15 +/- 0.33 (mean +/- SD). Voxels that exhibited both T1-Gd contrast enhancement and an abnormal proton spectrum were found to have a CBV 2.53 +/- 0.89 times higher than that in the WM.

CONCLUSION

A partial-volume model is demonstrated for estimating pure WM and GM CBV. It is also shown that the relationship between the tumor CBV as estimated with this model is generally consistent with expectations based on spectroscopy and imaging.