Vera C Keil1, Burkhard Mädler2, Gerrit H Gielen3, Bogdan Pintea4, Kanishka Hiththetiya5, Alisa R Gaspranova1, Jürgen Gieseke2, Matthias Simon6,7, Hans H Schild1, Dariusch R Hadizadeh1. 1. University Hospital Bonn/Germany, Department of Radiology, Bonn, Germany. 2. Philips Healthcare, Hamburg, Germany. 3. University Hospital Bonn/Germany, Department of Neuropathology, Center for Pathology, Bonn, Germany. 4. BG Krankenhaus Bergmannsheil, Bochum/Germany, Bochum, Germany. 5. University Hospital Bonn/Germany, Center for Pathology, Bonn, Germany. 6. Ev. Krankenhaus Bielefeld/Germany, Department of Neurosurgery, Bielefeld, Germany. 7. University Hospital Bonn/Germany, Department of Neurosurgery and Stereotaxy, Bonn, Germany.
Abstract
PURPOSE: To investigate the effect of the choice of the curve-fitting model on the perfusion fraction (fIVIM ) 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 (fIVIM ). 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/mm2 ) at 3T. fIVIM 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: fIVIM 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 fIVIM 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 fIVIM and its capacity to differentiate tissues. fIVIM 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.
PURPOSE: To investigate the effect of the choice of the curve-fitting model on the perfusion fraction (fIVIM ) 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 (fIVIM ). 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/mm2 ) at 3T. fIVIM 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: fIVIM 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 fIVIM 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 fIVIM and its capacity to differentiate tissues. fIVIM 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.
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