Intravoxel incoherent motion MRI in the brain: Impact of the fitting model on perfusion fraction and lesion differentiability.
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).



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. fIVIMwas 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.



fIVIMdiffered 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 fIVIMwas 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).



The curve-fitting model has an important impact on fIVIMand its capacity to differentiate tissues. fIVIMmay possibly be used to assess microvascular anatomy and is weakly correlated with DCE-MRI kinetic parameters.



2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1187-1199.
University Hospital Bonn/Germany, Department of Radiology, Bonn, Germany.
Vera C
0000-0001-8699-3506
VC
Keil
Philips Healthcare, Hamburg, Germany.
Burkhard
B
Mädler
University Hospital Bonn/Germany, Department of Neuropathology, Center for Pathology, Bonn, Germany.
Gerrit H
GH
Gielen
BG Krankenhaus Bergmannsheil, Bochum/Germany, Bochum, Germany.
Bogdan
B
Pintea
University Hospital Bonn/Germany, Center for Pathology, Bonn, Germany.
Kanishka
K
Hiththetiya
University Hospital Bonn/Germany, Department of Radiology, Bonn, Germany.
Alisa R
AR
Gaspranova
Philips Healthcare, Hamburg, Germany.
Jürgen
J
Gieseke
Ev. Krankenhaus Bielefeld/Germany, Department of Neurosurgery, Bielefeld, Germany.|University Hospital Bonn/Germany, Department of Neurosurgery and Stereotaxy, Bonn, Germany.
Matthias
M
Simon
University Hospital Bonn/Germany, Department of Radiology, Bonn, Germany.
Hans H
HH
Schild
University Hospital Bonn/Germany, Department of Radiology, Bonn, Germany.
Dariusch R
DR
Hadizadeh
D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media
DCE-MRI; IVIM; brain; fitting model; microvessels; perfusion fraction