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本文引用的文献

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Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging.扩散峰度成像中张量和张量衍生指标的估计。
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Diffusion imaging, white matter, and psychopathology.弥散成像、白质与精神病理学。
Annu Rev Clin Psychol. 2011;7:63-85. doi: 10.1146/annurev-clinpsy-032210-104507.
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Preliminary evidence of altered gray and white matter microstructural development in the frontal lobe of adolescents with attention-deficit hyperactivity disorder: a diffusional kurtosis imaging study.注意缺陷多动障碍青少年额叶灰质和白质微观结构发育改变的初步证据:扩散峰度成像研究。
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Myelination and support of axonal integrity by glia.胶质细胞对轴突完整性的髓鞘形成和支持。
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Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction.近期脑梗死患者大脑弥散峰度增高的初步观察。
NMR Biomed. 2011 Jun;24(5):452-7. doi: 10.1002/nbm.1610. Epub 2010 Oct 19.
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Monte Carlo study of a two-compartment exchange model of diffusion.双室扩散交换模型的蒙特卡罗研究。
NMR Biomed. 2010 Aug;23(7):711-24. doi: 10.1002/nbm.1577.
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Diffusion imaging in multiple sclerosis: research and clinical implications.多发性硬化症的弥散成像:研究与临床意义。
NMR Biomed. 2010 Aug;23(7):865-72. doi: 10.1002/nbm.1515.
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MRI quantification of non-Gaussian water diffusion by kurtosis analysis.磁共振成像(MRI)通过峰度分析对非高斯水扩散的定量研究。
NMR Biomed. 2010 Aug;23(7):698-710. doi: 10.1002/nbm.1518.
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Orientationally invariant indices of axon diameter and density from diffusion MRI.基于弥散磁共振成像的各向同性轴突直径和密度指标
Neuroimage. 2010 Oct 1;52(4):1374-89. doi: 10.1016/j.neuroimage.2010.05.043. Epub 2010 May 23.
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Two-compartment models of the diffusion MR signal in brain white matter.脑白质中扩散磁共振信号的双室模型。
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):329-36. doi: 10.1007/978-3-642-04268-3_41.

基于弥散峰度成像的脑白质特征分析。

White matter characterization with diffusional kurtosis imaging.

机构信息

Department of Radiology, New York University School of Medicine, New York, NY, USA.

出版信息

Neuroimage. 2011 Sep 1;58(1):177-88. doi: 10.1016/j.neuroimage.2011.06.006. Epub 2011 Jun 13.

DOI:10.1016/j.neuroimage.2011.06.006
PMID:21699989
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3136876/
Abstract

Diffusional kurtosis imaging (DKI) is a clinically feasible extension of diffusion tensor imaging that probes restricted water diffusion in biological tissues using magnetic resonance imaging. Here we provide a physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-axonal space. For the b-values typically used in DKI, the diffusion in each compartment is assumed to be anisotropic Gaussian and characterized by a diffusion tensor. The principal parameters of interest for the model include the intra- and extra-axonal diffusion tensors, the axonal water fraction and the tortuosity of the extra-axonal space. A key feature is that these can be determined directly from the diffusion metrics conventionally obtained with DKI. For three healthy young adults, the model parameters are estimated from the DKI metrics and shown to be consistent with literature values. In addition, as a partial validation of this DKI-based approach, we demonstrate good agreement between the DKI-derived axonal water fraction and the slow diffusion water fraction obtained from standard biexponential fitting to high b-value diffusion data. Combining the proposed WM model with DKI provides a convenient method for the clinical assessment of white matter in health and disease and could potentially provide important information on neurodegenerative disorders.

摘要

扩散峰度成像(DKI)是扩散张量成像的一种临床可行的扩展,它利用磁共振成像来探测生物组织中受限的水分子扩散。在这里,我们通过将组织建模为两个不可交换的隔室,即轴内空间和轴外空间,为白质区域的 DKI 指标提供了一种具有物理意义的解释。对于 DKI 中常用的 b 值,假设每个隔室中的扩散是各向异性的高斯分布,并由扩散张量来描述。该模型的主要感兴趣参数包括轴内和轴外扩散张量、轴内水分数和轴外空间的迂曲度。一个关键的特点是,这些参数可以直接从 DKI 常规获得的扩散指标中确定。对于三名健康的年轻人,我们从 DKI 指标中估计了模型参数,并发现它们与文献值一致。此外,作为对这种基于 DKI 的方法的部分验证,我们证明了 DKI 衍生的轴内水分数与从高 b 值扩散数据的标准双指数拟合获得的缓慢扩散水分数之间存在良好的一致性。将所提出的 WM 模型与 DKI 相结合,为评估健康和疾病状态下的白质提供了一种便捷的方法,并且可能为神经退行性疾病提供重要信息。