Premium
Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T : Reproducibility and Quality of Fit
Author(s) -
Kasa Loxlan W.,
Haast Roy A.M.,
Kuehn Tristan K.,
Mushtaha Farah N.,
Baron Corey A.,
Peters Terry,
Khan Ali R.
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27408
Subject(s) - reproducibility , kurtosis , human connectome project , white matter , diffusion imaging , effective diffusion coefficient , diffusion mri , pearson product moment correlation coefficient , nuclear medicine , medicine , nuclear magnetic resonance , mathematics , statistics , magnetic resonance imaging , computer science , physics , radiology , neuroscience , functional connectivity , biology
Background Diffusion kurtosis imaging (DKI) quantifies the non‐Gaussian diffusion of water within tissue microstructure. However, it has increased fitting parameters and requires higher b‐values. Evaluation of DKI reproducibility is important for clinical purposes. Purpose To assess the reproducibility in whole‐brain high‐resolution DKI at varying b‐values. Study Type Retrospective. Subjects and Phantoms In all, 44 individuals from the test–retest Human Connectome Project (HCP) database and 12 3D‐printed phantoms. Field Strength/Sequence Diffusion‐weighted multiband echo‐planar imaging sequence at 3T and 9.4T. magnetization‐prepared rapid acquisition gradient echo at 3T for in vivo structural data only. Assessment From HCP data with b‐values = 1000, 2000, 3000 s/mm 2 (dataset A), two additional datasets with b‐values = 1000, 3000 s/mm 2 (dataset B) and b‐values = 1000, 2000 s/mm 2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in white matter (WM) and gray matter (GM) based on whole‐brain and regions of interest (ROIs). Statistical Tests DKI reproducibility was assessed using the within‐subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate the presence of systematic biases. Repeated measures analysis of variance was used for statistical comparison. Results Datasets A and B exhibited lower DKI CoVs (<20%) compared to C (<50%) in both WM and GM ROIs (all P < 0.05). This effect varies between DKI and DTI parameters ( P < 0.005). Whole‐brain fitting residuals were consistent across datasets ( P > 0.05), but lower residuals in dataset B were detected for the WM ROIs ( P < 0.001). A similar trend was observed for the phantom data CoVs (<7.5%) at varying fiber orientations for datasets A and B. Finally, dataset C was characterized by higher residuals across the different fiber crossings ( P < 0.05). Data Conclusion The study demonstrates that high reproducibility can still be achieved within a reasonable scan time, specifically dataset B, supporting the potential of DKI for aiding clinical tools in detecting microstructural changes.