Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition

Author:

Dietrich Olaf1ORCID,Cai Mengfei2,Tuladhar Anil Man2,Jacob Mina A.2,Drenthen Gerald S.3,Jansen Jacobus F. A.34,Marques José P.2,Topalis Johanna1,Ingrisch Michael1,Ricke Jens1,de Leeuw Frank‐Erik2,Duering Marco5,Backes Walter H.3

Affiliation:

1. Department of Radiology, University Hospital LMU Munich Munich Germany

2. Department of Neurology, Donders Center for Medical Neurosciences Radboud University Medical Center Nijmegen The Netherlands

3. Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine Maastricht University Medical Center Maastricht The Netherlands

4. Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

5. Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering University of Basel Basel Switzerland

Abstract

The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation‐dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model‐selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion‐weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b‐values between 0 and 1000 s/mm2 and six non‐collinear diffusion gradient directions for each b‐value. Seven different IVIM‐diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo‐diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi2) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi2 values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine‐parameter IVIM–DTI model (with isotropic perfusion modeling) in normal‐appearing white matter (NAWM), and with an 11‐parameter model (IVIM–DTI with additional pseudo‐diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo‐diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion‐gradient directions.

Publisher

Wiley

Subject

Spectroscopy,Radiology, Nuclear Medicine and imaging,Molecular Medicine

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