Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging.
Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.
Cyclotron Research Centre, University of Liège, Liège, Belgium.
Elodie D
ED
André
Institute of Neuroscience and Medicine - 4, Juelich, Germany; Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany.
Farida
F
Grinberg
Institute of Neuroscience and Medicine - 4, Juelich, Germany.
Ezequiel
E
Farrher
Institute of Neuroscience and Medicine - 4, Juelich, Germany.
Ivan I
II
Maximov
Institute of Neuroscience and Medicine - 4, Juelich, Germany; Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen University, Aachen, Germany.
N Jon
NJ
Shah
Cyclotron Research Centre, University of Liège, Liège, Belgium.
Christelle
C
Meyer
Cyclotron Research Centre, University of Liège, Liège, Belgium.
Mathieu
M
Jaspar
Cyclotron Research Centre, University of Liège, Liège, Belgium.
Vincenzo
V
Muto
Cyclotron Research Centre, University of Liège, Liège, Belgium; Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
Christophe
C
Phillips
Cyclotron Research Centre, University of Liège, Liège, Belgium.
Evelyne
E
Balteau
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D056324:Diffusion Tensor Imaging / Q000295:instrumentation / Q000379:methods / Q000592:standards*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D066127:White Matter / Q000033:anatomy & histology* / Q000502:physiology