A comparative study of acquisition schemes for diffusion tensor imaging using MRI

This study has investigated the effects of the selection of the diffusion-weighted (DW) gradient directions on the precision of a diffusion tensor imaging (DTI) experiment. The theoretical analysis provided a quantitative framework in which the noise performance of DTI schemes could be assessed objectively and for the development of novel DTI schemes, which employ multiple DW gradient directions. This generic framework was first applied to the examination of two commonly used DTI schemes, which employed 6 DW gradient directions and hitherto were used indiscriminately under the sole condition of noncollinearity. It was then used to design and assess a novel 12-DW-gradient-direction DTI protocol, which employed the same total number of DW acquisitions as the two conventional schemes (12). This theoretical investigation was then corroborated using rigorous simulation and DTI experiments on both an isotropic phantom and a healthy human brain. Both the theoretical and the experimental analysis demonstrated that the two conventional schemes showed a significantly different noise performance and that use of the new multiple-DW-gradient-direction scheme clearly improved the precision of the DTI measurements.     

Minimal gradient encoding for robust estimation of diffusion anisotropy

This study has investigated the relationship between the noise sensitivity of measurement by magnetic resonance imaging (MRI) of the diffusion tensor (D) of water and the number N of diffusion-weighting (DW) gradient directions, using computer simulations of strongly anisotropic fibers with variable orientation. The DW directions uniformly sampled the diffusion ellipsoid surface. It is shown that the variation of the signal-to-noise ratio (SNR) of three ideally rotationally invariant scalars of D due to variable fiber orientation provides an objective quantitative measure for the diffusion ellipsoid sampling efficiency, which is independent of the SNR value of the baseline signal obtained without DW; the SNR variation decreased asymptotically with increasing N. The minimum number N(0) of DW directions, which minimized the SNR variation of the three scalars of D was determined, thereby achieving the most efficient ellipsoid sampling. The resulting time efficient diffusion tensor imaging (DTI) protocols provide robust estimation of diffusion anisotropy in the presence of noise and can improve the repeatability/reliability of DTI experiments when there is high variability in the orientation of similar anisotropic structures, as for example, in studies which require repeated measurement of one individual, intersubject comparisons or multicenter studies.     

Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts

Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes. A theoretical framework is developed to model noise in DTI, and by using simulations based on this theory, the degree to which the noise, tensor estimation method and acquisition protocol affect tensor-derived quantities, such as fractional anisotropy and apparent diffusion coefficient, is clarified. These results are then validated against clinical data. It is shown that reliability of tensor contrasts depends on the noise level, estimation method, diffusion-weighting scheme and underlying anatomy. The propensity for bias and errors does not monotonically increase with noise. Comparative results are shown in both graphical and tabular forms, so that decisions about suitable acquisition protocols and processing methods can be made on a case-by-case basis without exhaustive experimentation.     

Diffusion tensor encoding schemes optimized for white matter fibers with selected orientations

A method to produce gradient encoding schemes that minimize the noise of diffusion tensor imaging (DTI) indices for selected fiber orientations has been developed. The accuracy of DTI measurements depends on the gradient encoding scheme used. Most current acquisition schemes contain diffusion directions uniformly distributed in 3D space in order to provide equal noise levels for fibers in any orientation. However, when considering specific fiber bundles such as the corticospinal tract (CST) or parts of fiber bundles, the range of fiber orientations of interest may be limited. We hypothesized that, when studying fiber tracts with a limited range of orientations, measuring diffusion in directions that are uniformly distributed in 3D space may be suboptimal for the noise levels of various DTI indices. Therefore, we first used simulations to determine six diffusion directions that minimize the noise of DTI measurements for selected fiber orientations. The resulting optimized set of directions was then tested on the right CST of a healthy human subject, and its performance was compared with that of conventional acquisition strategies. Both the simulations and the experiments on the human subject demonstrated that the new scheme significantly reduced the standard deviation of DTI indices for tensors with primary eigenvectors within a selected range of orientations.     

Evaluation of Disease Stage of Radiation-Induced Brain Injury: a DTI Study with Pathological Correlation

The aim of this study is to evaluate if diffusion tensor imaging (DTI) can distinguish the disease process of radiation-induced brain injury when combined with apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. Twenty-one rabbits received irradiation of 100 Gy in the right brain hemisphere. Twelve rabbits were screened with magnetic resonance imaging (MRI) and DTI before radiation, and imaged at every week until week 9 following radiation. The rabbits that had MRI were euthanized at week 9 for histologic evaluation, while other nine rabbits without MRI were randomly killed for histologic evaluation at weeks 2, 4 and 6, respectively. From the DTI, the ADC and FA values were measured, and rADC and rFA were calculated. After radiation, the trend of the ADC value can be divided into three stages. In the first stage, the ADC value of the target tissues gradually decreased. In the second stage, the ADC value of white matter in the target tissues showed a recovery trend, back to the initial level similar to that in contralateral. In the third stage, the ADC value of white matter in the target tissue continues to increase over the ADC value of baseline and contralateral white matter. The FA value of radiation-targeted area showed continuous decreasing tendency. Pathological evaluation showed the different features in three stages. DTI can distinguish the different disease stages when combined with the ADC and FA values.     

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.     

Dependence of the fractional anisotropy in cervical spine from the number of diffusion gradients,repeated acquisition and voxel size

The aim of this study is to investigate the consequences of using different gradient schemes, number of repeated measurements and voxel size on the fractional anisotropy (FA) value in a diffusion tensor imaging (DTI) sequence on the cervical tract of the spinal cord. Twenty healthy volunteers underwent a total of 86 DTI axial acquisitions performed by using different voxel size and number of diffusion gradient directions (NDGDs). Three different diffusion gradient schemes were applied, named 6, 15 and 32 according to the NDGD. Furthermore, some acquisitions were repeated to investigate the effects of image averaging on FA value.     

数据采集方案对神经扩散模型影响的评估

基于单次数据采集的多种扩散模型联合应用已逐渐成为临床研究的热点,本研究比较了三种采集方案对于神经扩散模型定量计算的影响,包括Q空间笛卡尔网格（QGrid）、多壳层异向（Free）和多壳层同向（MDDW）采集方案,涉及的扩散模型包含扩散张量成像（DTI）;扩散峰度成像（DKI）;神经突方向分散度和密度成像（NODDI）;平均表观传播（MAP）模型.结果表明DTI和DKI模型对采集方案相对不敏感,而NODDI和MAP对采集方案和最大b值的设置相对较敏感,并且QGrid和Free方案一致性较高,因此在大样本和多中心研究中需要考虑采集方案的选择.此外,考虑到QGrid和Free方案分别在结合更多扩散模型和神经纤维束成像应用上更具优势,因此推荐使用.     

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.     

A study of rotationally invariant and symmetric indices of diffusion anisotropy.

This study investigated the properties of a class of rotationally invariant and symmetric (relative to the principal diffusivities) indices of the anisotropy of water self-diffusion, namely fractional anisotropy (FA), relative anisotropy (RA), and volume ratio (VR), with particular emphasis to their measurement in brain tissues. A simplified theoretical analysis predicted significant differences in the sensitivities of the anisotropy indices (AI) over the distribution of the principal diffusivities. Computer simulations were used to investigate the effects on AI image quality of three magnetic resonance (MR) diffusion tensor imaging (DTI) acquisition schemes, one being novel: the schemes were simulated on cerebral model fibres varying in shape and spatial orientation. The theoretical predictions and the results of the simulations were corroborated by experimentally determined spatial maps of the AI in a normal feline brain in vivo. We found that FA mapped diffusion anisotropy with the greatest detail and SNR whereas VR provided the strongest contrast between low- and high-anisotropy areas at the expense of increased noise contamination and decreased resolution in anisotropic regions. RA proved intermediate in quality. By sampling the space of the effective diffusion ellipsoid more densely and uniformly and requiring the same total imaging time as the published schemes, the novel DTI scheme achieved greater rotational invariance than the published schemes, with improved noise characteristics, resulting in improved image quality of the AI examined. Our findings suggest that significant improvements in diffusion anisotropy mapping are possible and provide criteria for the selection of the most appropriate AI for a particular application.