应用DTI技术对人脑白质纤维束的初步研究

目的建立扩散张量纤维束成像对人脑白质纤维的显示方法,并应用中国数字化可视人体数据进行对照观察,验证扩散张量成像(DTI)方法的可靠性。方法选择5名健康志愿者进行DTI成像,采用DtiStudio软件进行分析处理,重建出部分各向异性(FA)图、容积比(VR)图、相对各向异性(RA)图、表面扩散系数(ADC)图以及二维彩色张量图。应用中国数字化可视人体数据集断面图像、FA图及彩色FA图进行对照观察,利用fibertracking纤维跟踪软件及3DMRI软件进行三维重建显示脑内主要白质纤维束,辨认脑内白质纤维束的位置、形态。结果应用DTI纤维束成像可以清晰准确地描绘脑白质内主要神经纤维束的解剖图谱,包括联络纤维如弓形纤维、钩束、扣带束、上纵束和下纵束,连合纤维如胼胝体、前连合和穹隆,投射纤维如锥体束、视放射、内侧丘系等。DTI纤维束成像结果与已知解剖知识、中国可视化人体断面图像具有很好的一致性。结论应用DTI纤维束成像可以清晰准确地描绘脑白质内主要神经纤维束的解剖图谱,其结果与中国可视化人体断面图像、已知解剖知识是一致的,应用DTI纤维束成像研究脑内纤维连通性是可靠的。     

应用弥散张量纤维束成像技术重建儿童胼胝体压部纤维束

目的 探讨儿童胼胝体压部纤维束发育规律.方法 120名健康儿童(年龄5d～14岁)分为5组,≤1岁为第1组(22名),＞1岁～3岁为第2组(20名),＞3岁～6岁为第3组(22名),＞6岁～10岁为第4组(30名),＞10岁～14岁为第5组(26名).分别行脑部常规MRI和弥散张量成像(DTI)扫描,对原始数据进行弥散...     

三维脑白质纤维束示踪成像

目的使用磁共振弥散张量成像数据三维示踪大脑白质纤维束。方法对18名健康自愿者使用弥散加权-回波平面成像(DW-EPI)序列进行头部DTI检查。输出DICOM格式的DTI图像数据并根据已知的脑白质纤维束的解剖学知识,选取起始区、目标区、回避区,使用Diffusion Tensor VisualizerⅡ软件进行脑白质纤维束示踪三维重建。结果大脑的连合系、联络系和投射系的各种脑白质纤维束的结构和其在三维空间的走行可在每例实验对象的三维示踪结果显示,与解剖学所描述相对比基本一致。结论使用三维脑白质纤维束示踪成像可以在活体立体直观地显示大脑的各种脑白质纤维束。不仅可以用于解剖学教学,还对临床神经系统疾病的辅助诊断和科学研究有潜在的应用价值。     

磁共振弥散张量成像的脑白质纤维优化重建

磁共振弥散张量成像(DTI)可对大脑白质纤维束的三维几何结构进行无创研究,其主要方法为DTI纤维追踪成像.研究打破现有DTI纤维追踪成像从初始种子区域逐步追踪增长的框架,提出脑白质的纤维优化重建方法,从全局角度描述追踪纤维,通过其对弥散倾向和几何结构的优化估计,重构最优纤维路径.结果表明,该方法可提供连接两个感兴趣区域(ROI)的有效和对称连接,同时通过全局优化手段消除累积噪声及局部随机噪声,提高长距离成像的可靠性;在人工合成数据集和真实人体数据集中均能较好成像,较之现有纤维追踪成像具有更高的可靠性和可重复性.     

基于弥散张量成像的原发性失眠患者大脑白质变化

使用基于纤维束示踪的空间统计学方法对原发性失眠患者的弥散张量成像影数据进行分析,探讨其大脑白质变化。结果显示相较于健康对照组,原发性失眠患者的白质连接发生改变,主要表现为前放射冠、胼胝体膝部的各向异性分数降低。进一步的相关分析结果表明胼胝体膝部区域各向异性分数值降低与患者睡眠状况自评量表分数显著负相关。     

用于幼儿白质纤维束分析的快速密度聚类算法

目的从弥散张量成像(diffusion tensor imaging,DTI)中聚类白质(white matter,WM)纤维束对于定量分析幼儿大脑发育是非常重要的中间步骤。经典的基于图谱的分析方法通常是针对经过配准算法转化到通用模板空间之后的空间平均弥散张量做分析,其中配准算法不可避免地平滑了纤维束的局部特性而带来误差。更为直观可行的方法是首先将交织的白质纤维分离成单一类型的纤维束,然后在本地空间对来自不同主体的同一类型的纤维束进行统计分析,以避免来自配准算法的平滑误差。最近发表的密度峰值聚类(density peaks clustering,DP)算法,在没有任何现有模板的情况下聚类结构复杂的白质纤维依然具有很强的鲁棒性。尽管如此,密度的计算是DP的核心步骤,特别是当纤维数量巨大时,计算非常耗时。方法首先本文提出一个快速密度峰值聚类算法(fast density peaks clustering,FDP),将过程中的密度和关键参数的全局计算转换成局部计算,并利用一种二叉树结构来有序地存储这些用于局部计算的近邻,从而约50倍地加速了密度计算步骤。然后通过在基准数据集上的实验和与经典聚类算法的纤维聚类效果对比验证了FDP的加速效果和精确度。最后,证明了本算法在幼儿纤维发育分析中应用的合理性与可行性。结果密度计算基于上述结构的直接访问,因此显著降低了计算量;基于该算法的纤维数据统计结果也与基于图谱分析方法的一般结论一致。结论本文提出的快速密度聚类算法具备快速、准确和鲁棒的优势,其在幼儿白质纤维发育分析中的应用合理地降低了传统方法中繁杂的人工和计算消耗。     

基于扩散张量的脑白质内神经纤维束的可视化技术

介绍了在功能磁共振成像的研究中发展非常快的扩散张量成像技术的基本原理，以及如何利用扩散张量数据来重建脑白质内的神经纤维柬图像。其中主要介绍了白质束成像技术及其优缺点，并且分析了神经纤维束可视化技术的应用前景及其局限性。     

磁共振弥散张量成像联合纤维束成像在脑梗死中的应用研究

目的 分析脑梗死患者磁共振弥散张量成像(DTI)和纤维束成像(DTT)的特点,探讨DTI、DTT在对不同时期脑梗死患者诊断的价值。方法分别对58例不同时期脑梗死患者和25名健康志愿者行MRI检查,包括T1WI、T2WI成像、FLAIR及DTI成像,重建部分各向异性(FA)图,对梗死区、健侧相应部位及正常对照组相应部位进...     

基于扩散形状的DTI纤维跟踪算法研究

到目前为止，研究者们已经提出了许多种神经纤维束的三维可视化技术，其中，基于扩散跟踪的白质束成像技术在白质纤维束的可视化和分析中使用得最多。本文针对现有纤维跟踪算法存在的问题，提出了基于扩散形状的纤维跟踪算法，该算法结合了流线跟踪（streamline tracking，STT）法与张量弯曲（tensor deflection，TEND）法的优点，对不同的扩散形状采用不同的跟踪方向，尤其在平面扩散的情况提出了更接近纤维走行的跟踪方法，减少了跟踪方向与实际纤维走行的误差，能更完整、更准确地显示大脑白质的纤维走行。     

老年脑白质病变与阿尔茨海默病痴呆的关联研究

目的:探讨老年脑白质病变(WML)与阿尔茨海默病(AD)痴呆的关联。方法:回顾性研究。纳入2018年8月—2020年7月陆军特色医学中心神经内科981例住院患者的临床和影像学资料。其中男472例、女509例,年龄75～89岁,患者均行头颅MR检查评估有无WML病变,并采用神经心理量表评估有无认知功能障碍。依据临床诊断结...     

FSL在DTI大脑图像分析中的应用现状

随着弥散张量成像(diffusion tensor imaging,DTI)技术在大脑结构方面应用研究的增多,与之相关的数据分析软件也在增多,其中FMRIB Software Library(FSL)在示踪时使用的是基于概率性示踪的算法,在进行交叉纤维示踪和灰质连接上有很大优势。本文主要根据数据分析方式的不同对FSL包含的两个子工具(tract-based spatial statistics,TBSS和FMRIB’s diffusion toolbox,FDT)在DTI大脑图像分析方面的应用现状进行分类介绍,主要涉及老年抑郁症、精神分裂症以及认知功能等领域。本文首先简要介绍DTI原理,然后根据数据分析方式的不同,在TBSS部分分为全脑分析和感兴趣区域(region of interest,ROI)分析两方面,在FDT部分分为全脑示踪、ROI互连分析、基于连通度的分类三方面进行介绍,最后对FSL的局限性以及发展状况进行总结。     

基于机器学习的睡眠剥夺注意力易损性分类研究

目的:旨在寻找可以对睡眠剥夺后注意力易损与耐受个体进行准确区分的白质纤维束。方法:借助弥散张量成像技术获取各向异性分数、轴向扩散系数、径向扩散系数及平均扩散系数等反映白质弥散特性的特征参数,使用支持向量机分类算法构建睡眠剥夺易损性分类模型;采用准确性、敏感性、特异性、阳性预测值和阴性预测值等指标评价分类模型的性能表现;采用置换检验评估分类模型的显著性。结果:与只采用单一类型特征相比,使用组合特征构建的分类器表现性能最佳,其准确性、敏感性、特异性、阳性预测值、阴性预测值及曲线下面积分别为83.67%、80.00%、87.50%、86.96%、80.77%、88.67%。在组合特征构建的分类模型中对分类贡献较大的白质纤维束主要包括放射冠、内囊前肢、丘脑后辐射及皮质脊髓束等投射纤维、上纵束和扣带等联络纤维以及胼胝体和穹窿联合等联合纤维。结论:特定脑区间白质纤维束的微观结构特性可以作为区分睡眠剥夺后注意力易损与耐受个体的影像学标志物。     

新生儿胼胝体大小及形状与胎龄的相关性研究

目的　通过高场MRI检测不同胎龄新生儿出生时胼胝体的面积、偏心率及斜率,为早期评价和改善脑发育提供参考。 方法　从沧州市人民医院提取符合研究条件的新生儿头颅高场MRI图像286例,勾画胼胝体后使用MATLAB R2010a中的图像处理函数进行测量,得到胼胝体面积、偏心率及斜率并作统计学分析。 结果　胼胝体面积与胎龄的相关系数为0.61（P＜0.001）,最适曲线为二次曲线Y=0.132X2-6.179X+141.759,决定系数为0.37（P＜0.001）;偏心率与胎龄的相关系数为0.25（P＜0.001）,最适曲线为直线方程Y=0.009X+0.319,决定系数为0.07（P＜0.001）;后部斜率与胎龄的相关系数为0.27（P＜0.001）,最适曲线为直线方程Y=0.005X+0.349,决定系数为0.07（P＜0.001）。 结论　新生儿胎龄愈小,胼胝体愈小且愈圆。     

DTI of human skeletal muscle: the effects of diffusion encoding parameters,signal‐to‐noise ratio and T2 on tensor indices and fiber tracts

In this study, we have performed simulations to address the effects of diffusion encoding parameters, signal‐to‐noise ratio (SNR) and T₂ on skeletal muscle diffusion tensor indices and fiber tracts. Where appropriate, simulations were corroborated and validated by in vivo diffusion tensor imaging (DTI) of human skeletal muscle. Specifically, we have addressed: (i) the accuracy and precision of the diffusion parameters and eigenvectors at different SNR levels; (ii) the effects of the diffusion gradient direction encoding scheme; (iii) the optimal b value for diffusion tensor estimation; (iv) the effects of changes in skeletal muscle T₂; and, finally, the influence of SNR on fiber tractography and derived (v) fiber lengths, (vi) pennation angles and (vii) fiber curvatures. We conclude that accurate DTI of skeletal muscle requires an SNR of at least 25, a b value of between 400 and 500 s/mm², and data acquired with at least 12 diffusion gradient directions homogeneously distributed on half a sphere. Furthermore, for DTI studies focusing on skeletal muscle injury or pathology, apparent changes in the diffusion parameters need to be interpreted with great care in view of the confounding effects of T₂, particularly for moderate to low SNR values. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluating kurtosis‐based diffusion MRI tissue models for white matter with fiber ball imaging

In order to quantify well‐defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to a value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging. Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FAs) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability.     

基于DTI复杂网络的两性大脑差异性研究

为解释男女大脑差异是如何影响人们的思维和行为的,我们利用DTI复杂网络技术对男女两性大脑的差异性进行了定量分析。将44例健康志愿者分成男女两组,利用磁共振弥散张量成像对志愿者进行扫描和数据采集,通过构建脑白质纤维结构网络,并结合图论知识和复杂网络特征度量参数,对男女大脑结构差异及其在功能上的体现进行分析。对两组志愿者的脑结构网络分析发现,男女两组志愿者均具有小世界属性。结构网络局部参数分析显示,男女在涉及语言、情感、运动、方向感等的脑区具有显著差异。男女两性大脑生理结构的差别确实会导致大脑在功能上出现差别,两性大脑在功能上的差别可认为是两性在负责相关功能的脑区所具有的差异所导致。     

Cortex‐sparing fiber dissection: an improved method for the study of white matter anatomy in the human brain

Classical fiber dissection of post mortem human brains enables us to isolate a fiber tract by removing the cortex and overlying white matter. In the current work, a modification of the dissection methodology is presented that preserves the cortex and the relationships within the brain during all stages of dissection, i.e. ‘cortex‐sparing fiber dissection’. Thirty post mortem human hemispheres (15 right side and 15 left side) were dissected using cortex‐sparing fiber dissection. Magnetic resonance imaging study of a healthy brain was analyzed using diffusion tensor imaging (DTI)‐based tractography software. DTI fiber tract reconstructions were compared with cortex‐sparing fiber dissection results. The fibers of the superior longitudinal fasciculus (SLF), inferior fronto‐occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF) and uncinate fasciculus (UF) were isolated so as to enable identification of their cortical terminations. Two segments of the SLF were identified: first, an indirect and superficial component composed of a horizontal and vertical segment; and second, a direct and deep component or arcuate fasciculus. The IFOF runs within the insula, temporal stem and sagittal stratum, and connects the frontal operculum with the occipital, parietal and temporo‐basal cortex. The UF crosses the limen insulae and connects the orbito‐frontal gyri with the anterior temporal lobe. Finally, a portion of the ILF was isolated connecting the fusiform gyrus with the occipital gyri. These results indicate that cortex‐sparing fiber dissection facilitates study of the 3D anatomy of human brain tracts, enabling the tracing of fibers to their terminations in the cortex. Consequently, it is an important tool for neurosurgical training and neuroanatomical research.     

The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi‐tensor model vs. diffusion orientation distribution function

Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q‐sampling imaging (GQI), q‐ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI‐computed diffusivities and fractional anisotropy were significantly confounded by gel‐mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel‐mimicked edema when compared with the gold standard (r² = 0.92, P = 1.9 × 10^?9, bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel‐mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.     

The neurobiology of depression in later-life: clinical, neuropsychological, neuroimaging and pathophysiological features

As the population ages, the economic and societal impacts of neurodegenerative and neuropsychiatric disorders are expected to rise sharply. Like dementia, late-life depressive disorders are common and are linked to increased disability, high healthcare utilisation, cognitive decline and premature mortality. Considerable heterogeneity in the clinical presentation of major depression across the life cycle may reflect unique pathophysiological pathways to illness; differentiating those with earlier onset who have grown older (early-onset depression), from those with illness onset after the age of 50 or 60 years (late-onset depression). The last two decades have witnessed significant advances in our understanding of the neurobiology of early- and late-onset depression, and has shown that disturbances of fronto-subcortical functioning are implicated. New biomedical models extend well beyond perturbations of traditional monoamine systems to include altered neurotrophins, endocrinologic and immunologic system dysfunction, inflammatory processes and gene expression alterations. This more recent research has highlighted that a range of illness-specific, neurodegenerative and vascular factors appear to contribute to the various phenotypic presentations. This review highlights the major features of late-life depression, with specific reference to its associated aetiological, clinical, cognitive, neuroimaging, neuropathological, inflammatory and genetic correlates. Data examining the efficacy of pharmacological, non-pharmacological and novel treatments for depression are discussed. Ultimately, future research must aim to evaluate whether basic biomedical knowledge can be successfully translated into enhanced health outcomes via the implementation of early intervention paradigms.