Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit. Issue 4 (24th October 2020)

• Record Type: Journal Article
• Authors: Kasa, Loxlan W.; Haast, Roy A.M.; Kuehn, Tristan K.; Mushtaha, Farah N.; Baron, Corey A.; Peters, Terry; Khan, Ali R.
• Is Part Of: Journal of magnetic resonance imaging. Volume 53:Issue 4(2021)
• Page Start: 1175
• Page End: 1187
• Publication Date: 2020-10-24
• Subjects: diffusion magnetic resonance imaging -- diffusion kurtosis imaging -- reproducibility -- quality of fitting; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27408
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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.