Impact of software modeling on the accuracy of perfusion MRI in glioma

Abstract

BACKGROUND AND PURPOSE: Relative cerebral blood volume, as measured by T2z.ast;-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2z.ast;-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength. MATERIALS AND METHODS: This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose-and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV < 1.75, and percentage of voxels with relative CBV < 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman ( < =0.94), percentage<1.75 (r=0.93, < =0.91), mean (r=0.87, < =0.86), and mode (r=0.78, .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, < = 0.58, P = .001). CONCLUSIONS: Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.