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MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal

Institution:
1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: fzhang@bwh.harvard.edu.
2Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Elsevier Science
Publication Date:
Aug-2019
Journal:
Neuroimage
Volume Number:
196
Pages:
68-80
Citation:
Neuroimage. 2019 Aug 1;196:68-80.
PubMed ID:
30978492
PMCID:
PMC6592693
Appears in Collections:
NAC, LMI, NA-MIC, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
Generated Citation:
Zhang F., Ning L., O'Donnell L.J., Pasternak O. MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal. Neuroimage. 2019 Aug 1;196:68-80. PMID: 30978492. PMCID: PMC6592693.
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Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.