弥散张量成像在轻度认知功能障碍和老年性痴呆中的应用研究进展

弥散张量成像（diffusiontensorimaging,DTI）是在弥散加权成像基础上发展起来的一项新型磁共振成像技术,可显示脑白质纤维束的特点,揭示白质纤维束微观结构的改变,是一个新的非侵入性神经影像工具,能在活体绘制脑的微观结构。其通过在多个方向上施加弥散敏感梯度从而测量水分子在各个方向上弥散的程度。     

磁共振弥散张量成像在偏瘫型脑瘫儿童的应用

磁共振弥散张量成像(diffusion tensor imaging,DTI)是近年发展起来的一种弥散成像技术,是在弥散加权成像(diffusion weighted imaging,DWI)基础上发展起来新的功能成像技术,可在三维空间内定量分析组织内水分子的弥散运动,利用组织内水分子弥散呈各向异性的特征进行成像。DTI是目前惟一无创性活体研究脑白质纤维束形态结构的方法,可以清晰勾画出脑内     

MR弥散张量成像在中枢神经系统中的应用与前景

M R弥散张量成像(diffusion tensor im aging,DTI)是近年迅速发展的一种M R新技术,其通过在多个方向上施加弥散敏感梯度从而测量水分子弥散的程度和方向性。在其基础上发展起来的纤维束示踪成像技术(fibertractography,FT)可将走行方向各异的白质纤维束以三维形式重组,这一技术     

弥散张量成像技术在评估脑卒中患者白质纤维束损伤及预后方面的研究进展

弥散张量成像(DTI)技术利用组织中水分子的自由热运动的各向异性的原理,并通过特殊的软件处理成像,对大脑白质纤维束的三维几何结构进行研究,重建脑部白质神经连接,可直观地显示脑内病变对白质纤维形态结构直接或间接的影响。本文对DTI在脑卒中患者白质纤维束损伤及预后评估中的研究进展进行综述。     

人脑连合纤维的弥散张量纤维束成像

目的建立弥散张量纤维束成像对人脑连合纤维的显示方法,探讨显示结果与已知解剖知识的一致性.方法对1个正常志愿者进行单次激发回波平面弥散张量成像,利用纤维束成像软件包显示脑内连合纤维,观察重建的连合纤维与已知解剖知识的一致性.结果通过选择恰当的感兴趣区,设置不同的分数各向异性阈值、角度阈值、步长和体素内采样数目等参数,弥散张量纤维束成像可以清楚地显示胼胝体、穹隆和前连合等连合纤维的三维结构.显示结果与已知解剖知识具有良好的一致性.结论纤维束成像的结果与解剖学描述具有高度一致性,该方法是一种可靠的研究人脑纤维连接的方法.     

基于水分子弥散MRI研究进展

目的 水分子弥散广泛存在于体内各组织器官中，MRI通过测量和表征水分子在组织内的弥散和灌注等信息，可筛查、诊断多种疾病及评估其预后。采用不同b值及弥散梯度方向衍生出多种MR水分子弥散成像技术。本文对基于水分子弥散的MRI研究及其临床应用进展进行综述。     

磁共振弥散张量成像在儿童脑发育的应用及展望

磁共振弥散张量成像(DTI)是利用水分子的扩散现象进行成像的,它能多参数量化检测局部水分子的扩散特征,可获得白质纤维束分子水平的显微结构,是目前唯一能够无创性显示活体脑白质纤维束走行、排列、密度等细微解剖结构的检查方法,也是揭示脑网络和脑连接的最强手段,并可以反映不同个体间脑连接的差异,从而有助于大脑疾病的诊断。本文总结DTI在儿童脑发育方面的临床应用现状及新进展,以期对儿童脑发育进行动态观察提出新的影像认识和视角。     

IVIM-DWI腹部应用及其进展

体素内不相干运动(intravoxel incoherent motion imaging,IVIM)是指MR弥散加权成像(diffusion-weighted imaging,DWI)上体素内信号衰减同时包括真性水分子弥散和毛细血管网中随机血流微循环灌注,更全面地分析组织扩散成像数据,揭示疾病的病理生理学改变。近年来,IVIM逐渐被应用于临床研究中,其在腹部的研究也越来越多,主要集中在肝脏、胰腺及肾脏等部位。作者对IVIM在腹部中的应用及其进展进行综述。     

磁共振弥散峰度成像在脑外伤的研究进展

弥散峰度成像(DKI)是一种能量化组织内水分子弥散非高斯运动的磁共振新技术,是弥散成像技术的延伸,对于描绘脑组织微观结构具有独特优势。DKI在神经系统疾病的应用已表明是一种比磁共振弥散张量成像更敏感的磁共振定量检测方法,现就DKI的原理及其在脑外伤方面的研究进展予以综述。     

磁共振弥散张量成像技术及其在中枢神经系统临床应用进展

1994年,Basser等首次提出磁共振弥散张量成像（diffusiontensorimaging,DTI）的概念,它可研究脑白质纤维的微观结构及形态结构,探测白质纤维束的生理或病理状态下的变化,并利用多参数进行定量分析。弥散张量纤维束成像（diffusiontensor tracyography,DTT）是基于DTI上的一种新的可视化成像技术,是目前唯一可在活体上显示脑白质纤维束的无创性成像方法。DTI及DTT可清晰显示神经纤维束生理和病理的各向异性和构象特征,对疾病的诊断与鉴别诊断、肿瘤定性与分级、预后评估、制订治疗方案以及疗效评价等方面具有重要意义,近几年也成为国内外研究的热点,并取得很大成就。     

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.     

MR弥散张量成像在中枢神经系统的临床应用

DWI是一种较新的MR成像技术,图像对比与组织内水分子运动的不同有关,水分子的运动用表观扩散系数表示,DWI对急性脑缺血的早期诊断有重要的临床价值;DTI可用于评估各向同性及各向异性扩散.DTI主要用于评估影响脑白质尤其是白质纤维束完整性的疾病.本文主要对DWI、尤其是DTI在中枢神经系统中的临床应用现状作一综述.     

Hybrid diffusion imaging

Diffusion measurements in the human central nervous system are complex to characterize and a broad spectrum of methods have been proposed. In this study, a comprehensive diffusion encoding and analysis approach, hybrid diffusion imaging (HYDI), is described. The HYDI encoding scheme is composed of multiple concentric "shells" of constant diffusion weighting, which may be used to characterize the signal behavior with low, moderate and high diffusion weighting. HYDI facilitates the application of multiple data analyses strategies including diffusion tensor imaging (DTI), multi-exponential diffusion measurements, diffusion spectrum imaging (DSI) and q-ball imaging (QBI). These different analysis strategies may provide complementary information. DTI measures (mean diffusivity and fractional anisotropy) may be estimated from either data in the inner shells or the entire HYDI data. Fast and slow diffusivities were estimated using a nonlinear least squares bi-exponential fit on geometric means of the HYDI shells. DSI measurements from the entire HYDI data yield empirical model-independent diffusion information and are well-suited for characterizing tissue regions with complex diffusion behavior. DSI measurements were characterized using the zero displacement probability and the mean-squared displacement. The outermost HYDI shell was analyzed using QBI analysis to estimate the orientation distribution function (ODF), which is useful for characterizing the directions of multiple fiber groups within a voxel. In this study, an HYDI encoding scheme with 102 diffusion-weighted measurements was obtained over most of the human cerebrum in under 30 min.     

Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques

In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.     

Development of cerebral fiber pathways in cats revealed by diffusion spectrum imaging

Examination of the three-dimensional axonal pathways in the developing brain is key to understanding the formation of cerebral connectivity. By tracing fiber pathways throughout the entire brain, diffusion tractography provides information that cannot be achieved by conventional anatomical MR imaging or histology. However, standard diffusion tractography (based on diffusion tensor imaging, or DTI) tends to terminate in brain areas with low water diffusivity, indexed by low diffusion fractional anisotropy (FA), which can be caused by crossing fibers as well as fibers with less myelin. For this reason, DTI tractography is not effective for delineating the structural changes that occur in the developing brain, where the process of myelination is incomplete, and where crossing fibers exist in greater numbers than in the adult brain. Unlike DTI, diffusion spectrum imaging (DSI) can define multiple directions of water diffusivity; as such, diffusion tractography based on DSI provides marked flexibility for delineation of fiber tracts in areas where the fiber architecture is complex and multidirectional, even in areas of low FA. In this study, we showed that FA values were lower in the white matter of newborn (postnatal day 0; P0) cat brains than in the white matter of infant (P35) and juvenile (P100) cat brains. These results correlated well with histological myelin stains of the white matter: the newborn kitten brain has much less myelin than that found in cat brains at later stages of development. Using DSI tractography, we successfully identified structural changes in thalamo-cortical and cortico-cortical association tracts in cat brains from one stage of development to another. In newborns, the main body of the thalamo-cortical tract was smooth, and fibers branching from it were almost straight, while the main body became more complex and branching fibers became curved reflecting gyrification in the older cats. Cortico-cortical tracts in the temporal lobe were smooth in newborns, and they formed a sharper angle in the later stages of development. The cingulum bundle and superior longitudinal fasciculus became more visible with time. Within the first month after birth, structural changes occurred in these tracts that coincided with the formation of the gyri. These results show that DSI tractography has the potential for mapping morphological changes in low FA areas associated with growth and development. The technique may also be applicable to the study of other forms of brain plasticity, including future studies in vivo.     

扩散张量成像在急性脊髓损伤中的研究进展

脊髓是中枢神经系统的重要组成部分,不同时期、不同程度的脊髓损伤造成的后果及预后也不同,急性脊髓损伤病情发展迅速且较为严重。常规磁共振成像(magnetic resonance imaging,MRI)上的信号变化对于临床评估具有一定的局限性,磁共振扩散加权成像(diffusion weighted imaging,DWI)和磁共振扩散张量成像(diffusion tensor imaging,DTI)通过测量水分子的扩散运动,从微观上反映脊髓的损伤情况,不仅能早期及时地判断出急性脊髓损伤,而且能定量分析白质纤维束损伤的严重程度,为临床对这类患者的干预提供一定的价值信息。本文简要介绍了磁共振扩散加权成像和磁共振扩散张量成像技术在急性脊髓损伤中的应用情况及研究进展。     

磁共振弥散张量成像在精神障碍中的应用

弥散张量成像可测量组织中的水扩散,而有助于测量和量化组织的方向和结构,可作为检测脑白质完整性和神经纤维束连通性的理想工具.通过观察白质内水分子扩散的特征,弥散张量成像可早期检出精神障碍性疾病所致脑改变,提示其改变的病理基础,并有助于评价治疗效果.尽管其在精神障碍中的研究刚刚开始,但显示了一定的临床应用前景.     

Correction for direction-dependent distortions in diffusion tensor imaging using matched magnetic field maps

Diffusion tensor imaging (DTI) has seen increased usage in clinical and basic science research in the past decade. By assessing the water diffusion anisotropy within biological tissues, e.g. brain, researchers can infer different fiber structures important for neural pathways. A typical DTI data set contains at least one base image and six diffusion-weighted images along non-collinear encoding directions. The resultant images can then be combined to derive the three principal axes of the diffusion tensor and their respective cross terms, which can in turn be used to compute fractional anisotropy (FA) maps, apparent diffusion coefficient (ADC) maps, and to construct axonal fibers. The above operations all assume that DTI images along different diffusion-weighting directions for the same brain register to each other without spatial distortions. This assumption is generally false, as the large diffusion-weighting gradients would usually induce eddy currents to generate diffusion-weighting direction-dependent field gradients, leading to mis-registration within the DTI data set. Traditional methods for correcting magnetic field-induced distortions do not usually take into account these direction-dependent eddy currents unique for DTI, and they are usually time-consuming because multiple phase images need to be acquired. In this report, we describe our theory and implementation of an efficient and effective method to correct for the main field and eddy current-induced direction-dependent distortions for DTI images under a unified framework to facilitate the daily practice of DTI acquisitions.     

人脑不同弥散梯度方向弥散加权像的研究

目的　研究人脑不同弥散梯度方向弥散加权像 (DWI)上豆状核及放射冠的表现特征。方法　利用不同弥散梯度方向对 5 0例正常人脑施加平面回波弥散加权成像检查 ,测量豆状核及放射冠的信号强度及其表观弥散系数 (ADC值 )。结果　豆状核的ADC值及信号强度在不同弥散梯度方向上差异无显著性 ,而放射冠的ADC值及信号强度在不同弥散梯度方向上有明显差异。结论　豆状核信号强度及其ADC值不受弥散梯度方向影响 ;而放射冠的信号强度及ADC值则受其影响 ,定量地研究放射冠的弥散应该描述弥散梯度方向。     

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Diffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-of-the-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI.