Can dynamic susceptibility contrast magnetic resonance imaging perfusion data be analyzed using a model based on...

Area of Science:

• Neuroimaging
• Physiology
• Biophysics

Background:

• Dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSCE-MRI) analysis typically assumes random blood flow.
• This assumption is based on indicator dilution theory, applied to gray and white matter voxels.
• This study investigates the implications of a directional flow model for DSCE-MRI.

Purpose of the Study:

• To examine the implications of a physiological model of cerebral blood flow.
• To challenge the assumption of random blood flow in DSCE-MRI data sets.
• To explore a directional flow model for cerebral blood flow analysis.

Main Methods:

• A directional flow model was employed to estimate theoretical blood flow velocities in cerebral tissues.
• Mean transit time for net flow (nMTT) was measured for individual pixels.
• nMTT was calculated for each voxel by assessing contrast arrival time differences between adjacent voxels.

Main Results:

• DSCE-MRI data from normal volunteers showed evidence of directional blood flow in large vessels, gray matter, and white matter.
• Measured mean blood flow velocities in gray and white matter were 0.25 ± 0.013 and 0.21 ± 0.014 cm/second, respectively.
• Measured nMTT values for gray and white matter voxels closely matched predicted values from the directional model.

Conclusions:

• A directional model of blood flow offers an alternative method for calculating cerebral blood flow (CBF) from DSCE-MRI data.
• The findings support the existence of directional blood flow in cerebral tissues.
• This directional model provides a more accurate approach to CBF quantification using DSCE-MRI.