scholarly journals Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains

2020 ◽  
Author(s):  
Tianjia Zhu ◽  
Qinmu Peng ◽  
Austin Ouyang ◽  
Hao Huang
Keyword(s):  
Cerebral Cortex ◽  
Pearson Correlation ◽  
Temporal Cortex ◽  
Diffusion Kurtosis Imaging ◽  
Brain Sample ◽  
Noninvasive Biomarker ◽  
Histological Images ◽  
Cortical Regions ◽  
Non Gaussian ◽  
Diffusion Kurtosis

AbstractPurposeTo investigate the neuroanatomical underpinning of healthy macaque brain cortical microstructure measured by diffusion kurtosis imaging (DKI) which characterizes non-Gaussian water diffusion.MethodsHigh-resolution DKI was acquired from 6 postmortem macaque brains. Neurofilament density (ND) was quantified based on structure tensor from neurofilament histological images of a different macaque brain sample. After alignment of DKI-derived mean kurtosis (MK) maps to the histological images, MK and histology-based ND were measured at corresponding regions of interests characterized by distinguished cortical MK values in the prefrontal/precentral-postcentral and temporal cortices. Pearson correlation was performed to test significant correlation between these cortical MK and ND measurements.ResultsHeterogeneity of cortical MK across different cortical regions was revealed, with significantly and consistently higher MK measurements in the prefrontal/precentral-postcentral cortex compared to those in the temporal cortex across all 6 scanned macaque brains. Corresponding higher ND measurements in the prefrontal/precentral-postcentral cortex than in the temporal cortex were also found. The heterogeneity of cortical MK is associated with heterogeneity of histology-based ND measurements, with significant correlation between cortical MK and corresponding ND measurements (P <0.005).ConclusionThese findings suggested that DKI-derived MK can potentially be an effective noninvasive biomarker quantifying underlying neuroanatomical complexity inside the cerebral cortical mantle for clinical and neuroscientific research.

scholarly journals Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging

2019 ◽  
Vol 116 (10) ◽  
pp. 4681-4688 ◽  
Author(s):  
Minhui Ouyang ◽  
Tina Jeon ◽  
Aristeidis Sotiras ◽  
Qinmu Peng ◽  
Virendra Mishra ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Tensor ◽  
Cortical Plate ◽  
Diffusion Kurtosis Imaging ◽  
Third Trimester ◽  
Microstructural Changes ◽  
The Third ◽  
Cortical Regions ◽  
Non Gaussian ◽  
Diffusion Kurtosis

During the third trimester, the human brain undergoes rapid cellular and molecular processes that reshape the structural architecture of the cerebral cortex. Knowledge of cortical differentiation obtained predominantly from histological studies is limited in localized and small cortical regions. How cortical microstructure is differentiated across cortical regions in this critical period is unknown. In this study, the cortical microstructural architecture across the entire cortex was delineated with non-Gaussian diffusion kurtosis imaging as well as conventional diffusion tensor imaging of 89 preterm neonates aged 31–42 postmenstrual weeks. The temporal changes of cortical mean kurtosis (MK) or fractional anisotropy (FA) were heterogeneous across the cortical regions. Cortical MK decreases were observed throughout the studied age period, while cortical FA decrease reached its plateau around 37 weeks. More rapid decreases in MK were found in the primary visual region, while faster FA declines were observed in the prefrontal cortex. We found that distinctive cortical microstructural changes were coupled with microstructural maturation of associated white matter tracts. Both cortical MK and FA measurements predicted the postmenstrual age of preterm infants accurately. This study revealed a differential 4D spatiotemporal cytoarchitectural signature inferred by non-Gaussian diffusion barriers inside the cortical plate during the third trimester. The cytoarchitectural processes, including dendritic arborization and neuronal density decreases, were inferred by regional cortical FA and MK measurements. The presented findings suggest that cortical MK and FA measurements could be used as effective imaging markers for cortical microstructural changes in typical and potentially atypical brain development.

scholarly journals Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhijun Geng ◽  
Yunfei Zhang ◽  
Shaohan Yin ◽  
Shanshan Lian ◽  
Haoqiang He ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Diagnostic Performance ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Characteristic Analysis ◽  
Microstructural Heterogeneity ◽  
Diffusion Kurtosis ◽  
And Diffusion

Purpose. To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods. A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results. For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05 ). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and Kapp (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001 ). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion. The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging

2006 ◽  
Vol 19 (2) ◽  
pp. 236-247 ◽  
Author(s):  
Hanzhang Lu ◽  
Jens H. Jensen ◽  
Anita Ramani ◽  
Joseph A. Helpern
Keyword(s):  
Three Dimensional ◽  
Water Diffusion ◽  
Diffusion Kurtosis Imaging ◽  
Non Gaussian ◽  
Diffusion Kurtosis

scholarly journals White matter microstructural integrity across the adult lifespan: Combined perspective of diffusion tensor and kurtosis imaging

2021 ◽  
Author(s):  
Hiba Taha ◽  
Jordan A Chad ◽  
J. Jean Chen
Keyword(s):  
White Matter ◽  
Brain Aging ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
General Pattern ◽  
B Value ◽  
Diffusion Kurtosis Imaging ◽  
Adult Lifespan ◽  
Non Gaussian ◽  
Diffusion Kurtosis

Studies of healthy brain aging have reported diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at the typical b-value (approximately 1000 s/mm2) is Gaussian. Diffusion kurtosis imaging (DKI) is an extension of DTI that measures non-Gaussian diffusion (kurtosis) to better capture microenvironmental changes by incorporating additional data at a higher b-value. In this study, using UK Biobank data (b values of 1000 and 2000 s/mm2), we investigate (1) the extent of novel information gained from adding diffusional kurtosis to diffusivity observations in aging, and (2) how conventional DTI metrics in aging compare with diffusivity metrics derived from DKI, which are corrected for kurtosis. We find a general pattern of lower kurtosis alongside higher diffusivity among older adults. We also find differences between diffusivity metrics derived from DTI and DKI, emphasizing the importance of accounting for non-Gaussian diffusion. This work highlights the utility of measuring diffusional kurtosis as a simple addition to conventional diffusion imaging of aging.

Investigation of the Non-Gaussian Water Diffusion Properties in Bladder Cancer Using Diffusion Kurtosis Imaging

2015 ◽  
Vol 39 (2) ◽  
pp. 281-285 ◽  
Author(s):  
Shiteng Suo ◽  
Xiaoxi Chen ◽  
Xiang Ji ◽  
Zhiguo Zhuang ◽  
Lianming Wu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Water Diffusion ◽  
Diffusion Kurtosis Imaging ◽  
Non Gaussian ◽  
Diffusion Kurtosis ◽  
Diffusion Properties

scholarly journals The Role of Non-Gaussian Models of Diffusion Weighted MRI in Hepatocellular Carcinoma: A Systematic Review

2021 ◽  
Vol 10 (12) ◽  
pp. 2641
Author(s):  
Liberatore Tramontano ◽  
Carlo Cavaliere ◽  
Marco Salvatore ◽  
Valentina Brancato
Keyword(s):  
Hepatocellular Carcinoma ◽  
Systematic Review ◽  
Current Knowledge ◽  
Exponential Model ◽  
Response To Treatment ◽  
Diffusion Kurtosis Imaging ◽  
Diffusion Weighted ◽  
Non Gaussian ◽  
Diffusion Kurtosis

The importance of Diffusion Weighted Imaging (DWI) in hepatocellular carcinoma (HCC) has been widely handled in the literature. Due to the mono-exponential model limitations, several studies recently investigated the role of non-Gaussian DWI models in HCC. However, their results are variable and inconsistent. Therefore, the aim of this systematic review is to summarize current knowledge on non-Gaussian DWI techniques in HCC. A systematic search of the literature, including PubMed, Google Scholar, MEDLINE, and ScienceDirect databases, was performed to identify original articles since 2010 that evaluated the role of non-Gaussian DWI models for HCC diagnosis, grading, response to treatment, and prognosis. Studies were grouped and summarized according to the non-Gaussian DWI models investigated. We focused on the most used non-Gaussian DWI models (Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Stretched Exponential—SE). The quality of included studies was evaluated by using QUADAS-2 and QUIPS tools. Forty-three articles were included, with IVIM and DKI being the most investigated models. Although the role of non-Gaussian DWI models in clinical settings has not fully been established, our findings showed that their parameters may potentially play a role in HCC. Further studies are required to identify a standardized DWI acquisition protocol for HCC diagnosis, grading, response to treatment, and prognosis.

scholarly journals Parametric Mapping of Brain Tissues from Diffusion Kurtosis Tensor

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Yuanyuan Chen ◽  
Xin Zhao ◽  
Hongyan Ni ◽  
Jie Feng ◽  
Hao Ding ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Regional Analysis ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Diffusion Kurtosis Imaging ◽  
Mean Values ◽  
Detailed Classification ◽  
Magnetic Resonance Imaging Mri ◽  
Non Gaussian ◽  
Diffusion Kurtosis

Diffusion kurtosis imaging (DKI) is a new diffusion magnetic resonance imaging (MRI) technique to go beyond the shortages of conventional diffusion tensor imaging (DTI) from the assumption that water diffuse in biological tissue is Gaussian. Kurtosis is used to measure the deviation of water diffusion from Gaussian model, which is called non-Gaussian, in DKI. However, the high-order kurtosis tensor in the model brings great difficulties in feature extraction. In this study, parameters like fractional anisotropy of kurtosis eigenvalues (FAek) and mean values of kurtosis eigenvalues (Mek) were proposed, and regional analysis was performed for 4 different tissues: corpus callosum, crossing fibers, thalamus, and cerebral cortex, compared with other parameters. Scatterplot analysis and Gaussian mixture decomposition of different parametric maps are used for tissues identification. Diffusion kurtosis information extracted from kurtosis tensor presented a more detailed classification of tissues actually as well as clinical significance, and the FAek ofD-eigenvalues showed good sensitivity of tissues complexity which is important for further study of DKI.

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

2020 ◽  
Vol 61 (9) ◽  
pp. 1228-1239
Author(s):  
Xiaodan Chen ◽  
Lin Lin ◽  
Jie Wu ◽  
Guang Yang ◽  
Tianjin Zhong ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Mean Diffusivity ◽  
Labeling Index ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
High Grade ◽  
Ki 67 ◽  
Diffusion Kurtosis ◽  
Median Maximum

Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

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