描述人体内水分子扩散各向异性特征的新方法

通过核磁共振扩散张量成像(DTI)得到的特定值域的扩散各向异性指数(DAI) 可用于揭示水分子扩散椭球的形态学特征, 定量反映被成像物体内部水分子扩散的优势方向和强度, 间接得到被成像物体内部的组织结构信息. DAI的可靠性直接影响对DTI数据的分析和理解. 本文基于扩散张量椭球的几何学信息, 提出利用扩散椭球几何比(EGR)定量描述水分子扩散的各向异性程度. 通过蒙特卡罗模拟实验和对人脑DTI数据进行分析, 并与当前广泛应用的水分子扩散各向异性分数(FA)和近期文献提出的扩散椭球面积比(EAR)进行对比. 实验发现EGR在不同级别噪声影响下的对比度效果和抗噪性都优于FA及EAR. 而且EGR 加入了体积修正, 增强了盘形扩散张量情况下的敏感性, 能够更好地鉴别神经纤维束交叉情况, 对于各向异性扩散程度较高的白质深层和相对均质的表层都有较好的量化区分结果. 关键词： 扩散系数 各向异性扩散 扩散张量成像 扩散椭球几何比     

9.4T下TX模型小鼠脑组织的扩散张量成像研究

本文首次应用9.4 T高场磁共振扩散张量成像（diffusion tensor imaging，DTI）技术，研究了Wilson疾病模型TX（toxic milk）小鼠的脑组织微观结构改变和结构连接情况.基于感兴趣区域（region of interest，ROI）的分析发现，与对照组相比，TX模型组的各向异性比值（fractional anisotropy，FA）在海马、尾状核和苍白球显著下降；平均扩散率（mean diffusivity，MD）则呈现上升趋势，但无统计学意义.纤维束追踪法结果表明TX模型组小鼠的脑结构连接并未受到严重破坏，证明了铜累积对脑组织的损伤具有区域性.     

低场MRI系统中线扫描扩散张量成像方法的研究

磁共振扩散张量成像(DTI)是在扩散加权成像(DWI)基础上发展起来的一种新型技术,可以无创伤显示脑白质纤维,诊断脑白质病变. 但是由于各种原因,DTI一般只在超导高场磁共振成像(MRI)仪器上进行,这就限制了这一重要诊断手段临床应用的广泛性. 本文在低场磁共振成像系统上应用线扫描实现了扩散张量成像,并测量了健康志愿者大脑内主要解剖结构的表观扩散系数(ADC)和各项异性分数(FA),得到的数据与高场仪器上的相关数据比较是吻合的. 因此临床上使用在低场强上得到的DTI图像评价脑白质是可行的,而且通常在临床上这也是足够的.     

b值范围对6种体部扩散模型在前列腺应用的影响评估

论文研究了不同b值采集范围对6种体部扩散模型定量参数计算的影响．研究涉及扩散模型包含单指数模型(Mono)、扩散峰度成像(DKI)、体素内非相干运动模型(IVIM)、扩散拉伸指数模型(SEM)、分数微积分模型(FROC)和随机游走模型(CTRW),b值范围0～2 500 s/mm²．通过扩散模型参数之间的相关性、t检验以及前列腺病灶良恶性鉴别能力三个维度,评估了不同b值采集范围对参数计算的影响．结果显示与参考采样方案相比,随着最大b值降低,所得同一扩散参数感兴趣区域(ROI)均值的差异逐渐增大,但相关性降低不明显,且前列腺病灶良恶性的鉴别能力也保持相似水平．基于实验结果,建议在临床实践中采用b值范围为0～1 500 s/mm²的采集方案．这一方案在具备较高采集效率的同时,一半以上参数与参考采样方案结果的相关性不低于0.98,且良恶性鉴别能力指标曲线下面积(AUC)值的差别小于0.01．此外,不同扩散模型对于b值方案的敏感性存在差异,其中SEM和CTRW模型的参数受b值范围的影响相对较小．     

氧原子在Zr(0001)表面附近的扩散

在密度泛函理论计算的基础上,利用微动弹性带（nudged elastic band）方法研究了氧原子在Zr（0001）表面附近的扩散.首先计算了氧原子从稳定的表面面心立方（SFCC）位置向表面六角密排位置的扩散激活能（0.77 eV）;然后计算了氧原子从稳定的SFCC位置扩散到表面下第1层与第2层之间的八面体间隙位置,再继续向表面下第2层与第3层之间的八面体间隙位置扩散的激活能,在此过程中氧原子需克服两个能垒,其激活能分别为2.14和2.57 eV.结果表明,氧原子在Zr（0001）表面上方的扩散比较容易,而氧原子向Zr（0001）表面下的扩散相对较难. 关键词： Zr（0001）表面 微动弹性带 氧的扩散     

磁共振扩散张量成像中扩散敏感梯度磁场方向分布方案的研究进展

磁共振扩散张量成像可以定量无创研究人体内水分子在三维空间中的各向异性扩散规律,进而获取重要的病理及生理信息.为了得到水分子各向异性扩散信息,需要按照一定的方案依次施加不同方向的扩散敏感梯度磁场,测量水分子在这些方向上的扩散系数用以估算扩散张量.扩散张量成像测量结果的准确程度受梯度磁场方向分布方案的影响,本文对扩散敏感梯度磁场方向分布方案进行综述,包括完全随机方案、启发式方案、规则多面体式方案和数值优化方案等,分析这些方案的优势与局限性,并提出需进一步研究的问题.     

基于高斯平均的DTI脑模板构建方法

在获取被试的张量数据后通常对其进行多通道线性平均以得到张量模板.但线性平均不仅会忽略张量中的向量信息,还会使灰质和白质的交界处过于平滑,降低模板的分辨率.为了解决以上问题,本文引入了四元数及高斯加权平均来构建高斯扩散张量成像（Diffusion Tensor Imaging,DTI）脑模板.本文首先对55个健康被试的DTI数据进行预处理,使得数据伪影最小化;再通过扩散张量成像工具包（Diffusion Tensor Imaging ToolKit,DTI-TK）将预处理后的数据进行初步空间标准化;然后将张量通过特征分解得到特征向量和特征值;最后,将由特征向量转化的四元数标量和特征值分别进行高斯加权平均得到平均后的特征向量和特征值,并对其进行重建得到张量模板.实验结果表明相比于线性DTI模板,高斯DTI模板在DTED、COH、DVED、OVL、corr_FA评估指标上表现更优,而IA指标较差,说明本文提出的高斯DTI模板在整体信息保留方面有所优化,但方向信息有所丢失.     

核磁共振水分子扩散张量成像中基于广义Fibonacci数列的扩散敏感梯度磁场方向分布方案

为了准确得到人体内水分子各向异性扩散信息,在核磁共振扩散张量成像及高角分辨率扩散成像实验中,需要在众多空间均匀分布的方向上依次施加扩散敏感梯度磁场,测量水分子在不同方向上的扩散系数.目前方向分布方案的缺点有方向数目不连续、均匀性有待提高及部分方向数据的损坏会影响整个数据集等.本文以广义Fibonacci数列为基础,提出新的可以产生连续方向数目的扩散敏感梯度磁场方向分布方案,整个方案的方向均匀性较好,数据集内的部分数据仍然具有很好的空间均匀性,而且本方案中相邻两个扩散敏感梯度磁场方向接近相反,可以减小快速变化的高强度梯度磁场产生的涡流对结果的影响.     

扩散张量成像的人脑模板构建

扩散张量脑模板包含丰富的大脑白质组织信息,在空间标准化或者脑图谱创建中具有重要价值,然而基于扩散张量模型构建的脑模板精度不高,特别是在脑部复杂的神经元微观结构区域中应用受到限制.针对这一问题,研究者们提出了基于高分辨率扩散成像构建大脑模板的方法.本文对使用扩散张量成像方法进行脑模板构建的研究进展进行了综述,首先介绍了扩散张量脑模板构建的发展进程,阐述了脑模板构建中解决的技术问题及同时存在的局限性;接着详细论述了基于扩散频谱成像及高角度分辨率扩散成像构建脑模板的不同方法间的差异,并总结了这些研究方法取得的重要进展;最后通过分析目前研究进展提出该研究问题中存在的不足以及未来的发展趋势.     

混浊介质多光子激发显微成像的快速模型

将二维点扩散函数（PSF）和蒙特卡罗方法相结合,引入了一种研究混浊介质显微成像的快速仿真模型,将该模型用于混浊介质的多光子激发（MPE）显微成像研究,极大地提高了模拟效率,与直接蒙特卡罗方法计算结果的比较证实了该方法的正确性和有效性,此外,一个计算实例说明了混混介质的多光子激发显微成像具有比共焦荧光显微成像更优的横向分辨率。     

Diffusion tensor imaging to study anisotropy in a particular porous system: the trabecular bone network

During the last decade, considerable effort has been invested into the development of diffusion tensor imaging (DTI) mainly used to investigate cerebral morphology. The aim of this paper is to review and to discuss our recent results about high magnetic field DTI application to study spongy bone tissue. Due to its peculiar properties, spongy bone represents a particular porous system sample. Strategies to perform DTI on porous systems and issues linked to DTI outcome interpretation are presented on the basis of our results concerning trabecular bone network characterization.     

Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI

Diffusion tensor mapping with MRI can noninvasively track neural connectivity and has great potential for neural scientific research and clinical applications. For each diffusion tensor imaging (DTI) data acquisition scheme, the diffusion tensor is related to the measured apparent diffusion coefficients (ADC) by a transformation matrix. With theoretical analysis we demonstrate that the noise performance of a DTI scheme is dependent on the condition number of the transformation matrix. To test the theoretical framework, we compared the noise performances of different DTI schemes using Monte-Carlo computer simulations and experimental DTI measurements. Both the simulation and the experimental results confirmed that the noise performances of different DTI schemes are significantly correlated with the condition number of the associated transformation matrices. We therefore applied numerical algorithms to optimize a DTI scheme by minimizing the condition number, hence improving the robustness to experimental noise. In the determination of anisotropic diffusion tensors with different orientations, MRI data acquisitions using a single optimum b value based on the mean diffusivity can produce ADC maps with regional differences in noise level. This will give rise to rotational variances of eigenvalues and anisotropy when diffusion tensor mapping is performed using a DTI scheme with a limited number of diffusion-weighting gradient directions. To reduce this type of artifact, a DTI scheme with not only a small condition number but also a large number of evenly distributed diffusion-weighting gradients in 3D is preferable.     

Development and initial evaluation of 7-T q-ball imaging of the human brain

Diffusion tensor imaging (DTI) noninvasively depicts white matter connectivity in regions where the Gaussian model of diffusion is valid but yields inaccurate results in those where diffusion has a more complex distribution, such as fiber crossings. q-ball imaging (QBI) overcomes this limitation of DTI by more fully characterizing the angular dependence of intravoxel diffusion with larger numbers of diffusion-encoding directional measurements at higher diffusion-weighting factors (b values). However, the former technique results in longer acquisition times and the latter technique results in a lower signal-to-noise ratio (SNR). In this project, we developed specialized 7-T acquisition methods utilizing novel radiofrequency pulses, eight-channel parallel imaging EPI and high-order shimming with a phase-sensitive multichannel B0 field map reconstruction. These methods were applied in initial healthy adult volunteer studies, which demonstrated the feasibility of performing 7-T QBI. Preliminary comparisons of 3 T with 7 T within supratentorial crossing white matter tracts documented a 79.5% SNR increase for b=3000 s/mm2 (P=.0001) and a 38.6% SNR increase for b=6000 s/mm2 (P=.015). With spherical harmonic reconstruction of the q-ball orientation distribution function at b=3000 s/mm2, 7-T QBI allowed for accurate visualization of crossing fiber tracts with fewer diffusion-encoding acquisitions as compared with 3-T QBI. The improvement of 7-T QBI at b factors as high as 6000 s/mm2 resulted in better angular resolution as compared with 3-T QBI for depicting fibers crossing at shallow angles. Although the increased susceptibility effects at 7 T caused problematic distortions near brain-air interfaces at the skull base and posterior fossa, these initial 7-T QBI studies demonstrated excellent quality in much of the supratentorial brain, with significant improvements as compared with 3-T acquisitions in the same individuals.     

Geometric analysis of the b-dependent effects of Rician signal noise on diffusion tensor imaging estimates and determining an optimal b value

The optimal diffusion weighting (DW) factor, b, for use in diffusion tensor imaging (DTI) studies remains uncertain. In this study, the geometric relations of DW quantities are examined, in particular, the effects of Rician noise in the measured magnetic resonance signal. This geometric analysis is used to make theoretical predictions for selecting a b value to reduce the influence of noise. It is shown that the optimal b value for DTI studies in healthy human parenchyma is approximately b=1200 s mm⁻², with a simple relation given as well for a given expected apparent diffusion coefficient. Monte-Carlo simulations on sets of realistic DTI measures are then performed, verifying the optimal DW for minimizing estimate errors. The effects of noise on various DTI parameters such as anisotropy indices (fractional anisotropy and scaled relative anisotropy), mean diffusivity, radial diffusivity, eigenvalues and the direction of the first eigenvector are investigated as well.     

The ellipsoidal area ratio: an alternative anisotropy index for diffusion tensor imaging

In the processing and analysis of diffusion tensor imaging (DTI) data, certain predefined morphological features of diffusion tensors are often represented as simplified scalar indices, termed diffusion anisotropy indices (DAIs). When comparing tensor morphologies across differing voxels of an image, or across corresponding voxels in different images, DAIs are mathematically and statistically more tractable than are the full tensors, which are probabilistic ellipsoids consisting of three orthogonal vectors that each has a direction and an associated scalar magnitude. We have developed a new DAI, the "ellipsoidal area ratio" (EAR), to represent the degree of anisotropy in the morphological features of a diffusion tensor. The EAR is a normalized geometrical measure of surface curvature in the 3D diffusion ellipsoid. Monte Carlo simulations and applications to the study of in vivo human data demonstrate that, at low noise levels, EAR provides a similar contrast-to-noise ratio (CNR) but a higher signal-to-noise ratio (SNR) than does fractional anisotropy (FA), which is currently the most popular anisotropy index in active use. Moreover, at the high noise levels encountered most commonly in real-world DTI datasets, EAR compared with FA is consistently much more robust to perturbations from noise and it provides a higher CNR, features useful for the analysis of DTI data that are inherently noise sensitive.     

Convergent brain microstructure across multiple genetic models of schizophrenia and autism spectrum disorder: A feasibility study

Neuroimaging studies of psychiatric illness have revealed a broad spectrum of structural and functional perturbations that have been attributed in part to the complex genetic heterogeneity underpinning these disorders. These perturbations have been identified in both preclinical genetic models and in patients when compared to control populations, but recent work has also demonstrated strong evidence for genetic, molecular, and structural convergence of several psychiatric diseases. We explored potential similarities in neural microstructure in preclinical genetic models of ASD (Fmr1, Nrxn1, Pten) and schizophrenia (Disc1 svΔ2) and in age- and sex-matched control animals with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Our findings demonstrate a convergence in brain microstructure across these four genetic models with both tract-based and region-of-interest based analyses, which continues to buttress an emerging understanding of converging neural microstructure in psychiatric disease.