Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

• Published in: Journal of magnetic resonance imaging Vol. 53; no. 4; pp. 1175 - 1187
• Main Authors: Kasa, Loxlan W., Haast, Roy A.M., Kuehn, Tristan K., Mushtaha, Farah N., Baron, Corey A., Peters, Terry, Khan, Ali R.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2021; Wiley Subscription Services, Inc
• Subjects: Adult; Brain; Brain - diagnostic imaging; Coefficient of variation; Datasets; Diffusion; diffusion kurtosis imaging; diffusion magnetic resonance imaging; Diffusion Tensor Imaging - methods; Echo-Planar Imaging; Evaluation; Female; Fiber orientation; Field strength; Humans; Image Processing, Computer-Assisted - methods; Imaging; In vivo methods and tests; Kurtosis; Magnetic resonance imaging; Male; Microstructure; Neuroimaging; Parameters; Phantoms, Imaging; quality of fitting; Reproducibility; Reproducibility of Results; Retrospective Studies; Spatial discrimination; Spatial resolution; Statistical analysis; Statistical tests; Substantia alba; Substantia grisea; Three dimensional printing; Variance analysis; Young Adult; diffusion kurtosis imaging; diffusion magnetic resonance imaging; quality of fitting; reproducibility
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27408

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/mm2 (dataset A), two additional datasets with b‐values = 1000, 3000 s/mm2 (dataset B) and b‐values = 1000, 2000 s/mm2 (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.