Dataset Information

Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

ABSTRACT: As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals.

SUBMITTER: Wang S

PROVIDER: S-EPMC5372828 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

Wang Sijia S Peterson Daniel J DJ Wang Yong Y Wang Qing Q Grabowski Thomas J TJ Li Wenbin W Madhyastha Tara M TM

Frontiers in neurology 20170329

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies ...[more]

PMID: 28424656

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Project description:Background and purposeWhite matter fiber tractography relies on fiber bundle orientation estimates from diffusion MR imaging. However, clinically feasible techniques such as DTI and diffusional kurtosis imaging use assumptions, which may introduce error into in vivo orientation estimates. In this study, fiber bundle orientations from DTI and diffusional kurtosis imaging are compared with diffusion spectrum imaging as a criterion standard to assess the performance of each technique.Materials and methodsFor each subject, full DTI, diffusional kurtosis imaging, and diffusion spectrum imaging datasets were acquired during 2 independent sessions, and fiber bundle orientations were estimated by using the specific theoretic assumptions of each technique. Angular variability and angular error measures were assessed by comparing the orientation estimates. Tractography generated with each of the 3 reconstructions was also examined and contrasted.ResultsOrientation estimates from all 3 techniques had comparable angular reproducibility, but diffusional kurtosis imaging decreased angular error throughout the white matter compared with DTI. Diffusion spectrum imaging and diffusional kurtosis imaging enabled the detection of crossing-fiber bundles, which had pronounced effects on tractography relative to DTI. Diffusion spectrum imaging had the highest sensitivity for detecting crossing fibers; however, the diffusion spectrum imaging and diffusional kurtosis imaging tracts were qualitatively similar.ConclusionsFiber bundle orientation estimates from diffusional kurtosis imaging have less systematic error than those from DTI, which can noticeably affect tractography. Moreover, tractography obtained with diffusional kurtosis imaging is qualitatively comparable with that of diffusion spectrum imaging. Because diffusional kurtosis imaging has a shorter typical scan time than diffusion spectrum imaging, diffusional kurtosis imaging is potentially more suitable for a variety of clinical and research applications.

Project description:Prior postmortem studies have shown gray matter (GM) microstructural abnormalities in schizophrenia. However, few studies to date have examined GM microstructural integrity in schizophrenia in vivo. Here, we employed diffusion kurtosis imaging (DKI) to test for differences in GM microstructure in eighteen schizophrenia (SZ) patients versus nineteen healthy controls (HC). GM microstructure was characterized in each participant using DKI-derived metrics of mean kurtosis (MK) and mean diffusivity (MD). Individual T1-weighted images were used to create subject-specific cortically-labelled regions of interest (ROIs) of the four cortical lobes and sixty-eight cortical GM regions delineated by the Desikan-Killiany atlas, and to derive the associated cortical thickness and area measures. The derived ROIs were also registered to the diffusion space of each subject and used to generate region-specific mean MK and MD values. We additionally administered the Wisconsin Card Sorting Test (WCST), Stroop test, and Trail Making Test part B (Trails-B) to test the relationship between GM metrics and executive function in SZ. We found significantly increased MK and MD in SZ compared to HC participants in the temporal lobe, sub-lobar temporal cortical regions (fusiform, inferior temporal, middle temporal and temporal pole), and posterior cingulate cortex after correcting for multiple comparisons. Correlational analyses revealed significant associations of MK and MD with executive function scores derived from the WCST, Stroop, and Trails-B tests, along with an inverse relationship between MK and MD and cortical thickness and area. A hierarchical multiple linear regression analysis showed that up to 85% of the inter-subject variability in cognitive function in schizophrenia measured by the WCST could be explained by MK in combination with either GM thickness or area. MK and MD appear to be sensitive to GM microstructural pathology in schizophrenia and may provide useful biomarkers of abnormal cortical microstructure in this disorder.

Project description:Simple summaryGlioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.PurposeNon-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.Methods and materialsThirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.ResultsThe large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).ConclusionThe DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.

Dataset Information

Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

Publications

Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

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