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

Leland S Hu, Z. Kelm, Panagiotis Korfiatis, Amylou Dueck, C. Elrod, B. M. Ellingson, Timothy J Kaufmann, J. M. Eschbacher, J. P. Karis, K. Smith, P. Nakaji, Debra H Brinkmann, D. Pafundi, L. C. Baxter, Bradley J Erickson

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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 ( .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.

Original languageEnglish (US)
Pages (from-to)2242-2249
Number of pages8
JournalAmerican Journal of Neuroradiology
Volume36
Issue number12
DOIs
StatePublished - Dec 1 2015

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ASJC Scopus subject areas

  • Clinical Neurology
  • Radiology Nuclear Medicine and imaging

Cite this

Hu, L. S., Kelm, Z., Korfiatis, P., Dueck, A., Elrod, C., Ellingson, B. M., Kaufmann, T. J., Eschbacher, J. M., Karis, J. P., Smith, K., Nakaji, P., Brinkmann, D. H., Pafundi, D., Baxter, L. C., & Erickson, B. J. (2015). Impact of software modeling on the accuracy of perfusion MRI in glioma. American Journal of Neuroradiology, 36(12), 2242-2249. https://doi.org/10.3174/ajnr.A4451