Intravoxel incoherent motion MRI in the brain: Impact of the fitting model on perfusion fraction and lesion differentiability. Issue 4 (2nd February 2017)

• Record Type: Journal Article
• Authors: Keil, Vera C.; Mädler, Burkhard; Gielen, Gerrit H.; Pintea, Bogdan; Hiththetiya, Kanishka; Gaspranova, Alisa R.; Gieseke, Jürgen; Simon, Matthias; Schild, Hans H.; Hadizadeh, Dariusch R.
• Is Part Of: Journal of magnetic resonance imaging. Volume 46:Issue 4(2017)
• Page Start: 1187
• Page End: 1199
• Publication Date: 2017-02-02
• Subjects: IVIM -- brain -- fitting model -- perfusion fraction -- microvessels -- DCE‐MRI; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25615
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Purpose: To investigate the effect of the choice of the curve‐fitting model on the perfusion fraction ( f IVIM ) with regard to tissue type characterization, correlation with microvascular anatomy, and dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) parameters. Several curve‐fitting models coexist in intravoxel incoherent motion (IVIM) MRI to derive the ( f IVIM ). Materials and Methods: In all, 29 patients with brain lesions (12 gliomas, 11 meningiomas, three metastases, two gliotic scars, one multiple sclerosis) underwent IVIM‐MRI (32 b ‐values, 0 to 2000 s/mm 2 ) at 3T. f IVIM was determined by classic monoexponential, biexponential, and a novel nonnegative least squares (NNLS) fitting in 352 regions of interest (lesion‐containing and normal‐appearing tissue) and tested their correlation with DCE‐MRI kinetic parameters and microvascular anatomy derived from 57 region of interest (ROI)‐based biopsies and their capacities to differentiate histologically different lesions. Results: f IVIM differed significantly between all three models and all tissue types (monoexponential confidence interval in percent [CI 3.4–3.8]; biexponential [CI 11.21–12.45]; NNLS [CI 2.06–2.60]; all P < 0.001). For all models an increase in f IVIM was associated with a shift to larger vessels and higher vessel area / tissue area ratio (regression coefficient 0.07–0.52; P = 0.04–0.001). Correlation with kinetic parameters derived from DCE‐MRI was usually not significant. Only biexponential fitting allowed differentiation of both gliosis from edema and high‐ from low‐grade glioma (both P < 0.001). Conclusion: The curve‐fitting model has an important impact on f IVIM and its capacity to differentiate tissues. f IVIM may possibly be used to assess microvascular anatomy and is weakly correlated with DCE‐MRI kinetic parameters. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1187–1199.