3D fiber tractography with susceptibility tensor imaging

Gradient-echo MRI has revealed anisotropic magnetic susceptibility in the brain white matter. This magnetic susceptibility anisotropy can be measured and characterized with susceptibility tensor imaging (STI). In this study, a method of fiber tractography based on STI is proposed and demonstrated in the mouse brain. STI experiments of perfusion-fixed mouse brains were conducted at 7.0 T. The magnetic susceptibility tensor was calculated for each voxel with regularization and decomposed into its eigensystem. The major eigenvector is found to be aligned with the underlying fiber orientation. Following the orientation of the major eigenvector, we are able to map distinctive fiber pathways in 3D. As a comparison, diffusion tensor imaging (DTI) and DTI fiber tractography were also conducted on the same specimens. The relationship between STI and DTI fiber tracts was explored with similarities and differences identified. It is anticipated that the proposed method of STI tractography may provide a new way to study white matter fiber architecture. As STI tractography is based on physical principles that are fundamentally different from DTI, it may also be valuable for the ongoing validation of DTI tractography.     

Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results

In this work we investigate the effects of echo planar imaging (EPI) distortions on diffusion tensor imaging (DTI) based fiber tractography results. We propose a simple experimental framework that would enable assessing the effects of EPI distortions on the accuracy and reproducibility of fiber tractography from a pilot study on a few subjects. We compare trajectories computed from two diffusion datasets collected on each subject that are identical except for the orientation of phase encode direction, either right-left (RL) or anterior-posterior (AP). We define metrics to assess potential discrepancies between RL and AP trajectories in association, commissural, and projection pathways. Results from measurements on a 3 Tesla clinical scanner indicated that the effects of EPI distortions on computed fiber trajectories are statistically significant and large in magnitude, potentially leading to erroneous inferences about brain connectivity. The correction of EPI distortion using an image-based registration approach showed a significant improvement in tract consistency and accuracy. Although obtained in the context of a DTI experiment, our findings are generally applicable to all EPI-based diffusion MRI tractography investigations, including high angular resolution (HARDI) methods. On the basis of our findings, we recommend adding an EPI distortion correction step to the diffusion MRI processing pipeline if the output is to be used for fiber tractography.     

Asymmetric fiber trajectory distribution estimation using streamline differential equation

Fiber orientation distribution estimation with diffusion magnetic resonance imaging (dMRI) is critical in white matter fiber tractography which is most commonly based on symmetric crossing structures. Ambiguous spatial correspondence between estimated diffusion directions and fiber geometry, such as asymmetric crossing, bending, fanning, or kissing, makes tractography challenging. Consequently, numerous tracts suggest intertwined connections in unexpected regions of the white matter or actually stop prematurely in the white matter. In this work, we propose a novel asymmetric fiber trajectory distribution (FTD) function defined on neighboring voxels based on a streamline differential equation from fluid mechanics. The spatial consistency with intra- and inter-voxel constraints is derived for FTD estimation by introducing the concept of divergence. At a local level, the FTD is a series of curve flows that minimize the energy function, characterizes the relations between fibers and joint fiber fragments within the same fiber bundle. Experiments are performed on FiberCup phantom, ISMRM 2015 Tractography challenge data, and in vivo brain dMRI data for qualitative and quantitative evaluations. Results show that our approach can reveal continuous asymmetric FTD details that are potentially useful for robust tractography.     

Characterization of displaced white matter by brain tumors using combined DTI and fMRI

In vivo white matter tractography by diffusion tensor imaging (DTI) has become a popular tool for investigation of white matter architecture in the normal brain. Despite some unresolved issues regarding the accuracy of DTI, recent studies applied DTI for delineating white matter organization in the vicinity of brain lesions and especially brain tumors. Apart from the intrinsic limitations of DTI, the tracking of fibers in the vicinity or within lesions is further complicated due to changes in diseased tissue such as elevated water content (edema), tissue compression and degeneration. These changes deform the architecture of the white matter and in some cases prevent definite selection of the seed region of interest (ROI) from which fiber tracking begins. We show here that for displaced fiber systems, the use of anatomical approach for seed ROI selection yields insufficient results. Alternatively, we propose to select the seed points based on functional MRI activations which constrain the subjective seed ROI selection. The results are demonstrated on two major fiber systems: the pyramidal tract and the superior longitudinal fasciculus that connect critical motor and language areas, respectively. The fMRI based seed ROI selection approach enabled a more comprehensive mapping of these fiber systems. Furthermore, this procedure enabled the characterization of displaced white matter using the eigenvalue decomposition of DTI. We show that along the compressed fiber system, the diffusivity parallel to the fiber increases, while that perpendicular to the fibers decreases, leading to an overall increase in the fractional anisotropy index reflecting the compression of the fiber bundle. We conclude that definition of the functional network of a subject with deformed white matter should be done carefully. With fMRI, one can more accurately define the seed ROI for DTI based tractography and to provide a more comprehensive, functionally related, white matter mapping, a very important tool used in pre-surgical mapping.     

利用新型扩散成像技术研究年老对大脑微观结构的影响

了解年老过程中大脑在细胞水平上发生的变化对于揭示老年人认知功能下降的原因有重要意义。扩散MRI(diffusion MRI,d MRI)技术是目前惟一可以无创探查活体组织微观结构的方法。扩散张量成像(DTI,diffusion tensor imaging)是临床上最常用的一种d MRI技术,但是由于某些固有缺陷,它不能充分刻画大脑组织的微观结构。作者介绍三种可以有效弥补DTI不足的新型扩散成像方法:扩散峰度成像(diffusion kurtosis imaging,DKI),扩散的受阻受限合成模型(composite hindered and restricted model of diffusion,CHARMED)和神经突方向离散度与密度成像(neurite orientation dispersion and density imaging,NODDI)。联合使用DTI和这些新技术,研究者可以更深入地了解年老如何影响大脑的微观结构。     

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.     

Diffusion MRI at 25: exploring brain tissue structure and function

Diffusion MRI (or dMRI) came into existence in the mid-1980s. During the last 25 years, diffusion MRI has been extraordinarily successful (with more than 300,000 entries on Google Scholar for diffusion MRI). Its main clinical domain of application has been neurological disorders, especially for the management of patients with acute stroke. It is also rapidly becoming a standard for white matter disorders, as diffusion tensor imaging (DTI) can reveal abnormalities in white matter fiber structure and provide outstanding maps of brain connectivity. The ability to visualize anatomical connections between different parts of the brain, non-invasively and on an individual basis, has emerged as a major breakthrough for neurosciences. The driving force of dMRI is to monitor microscopic, natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are thus used as a probe that can reveal microscopic details about tissue architecture, either normal or in a diseased state.     

Probabilistic tractography using Lasso bootstrap

Diffusion magnetic resonance imaging (dMRI) can be used for noninvasive imaging of white matter tracts. Using fiber tracking, which propagates fiber streamlines according to fiber orientations (FOs) computed from dMRI, white matter tracts can be reconstructed for investigation of brain diseases and the brain connectome. Because of image noise, probabilistic tractography has been proposed to characterize uncertainties in FO estimation. Bootstrap provides a nonparametric approach to the estimation of FO uncertainties and residual bootstrap has been used for developing probabilistic tractography. However, recently developed models have incorporated sparsity regularization to reduce the required number of gradient directions to resolve crossing FOs, and the residual bootstrap used in previous methods is not applicable to these models. In this work, we propose a probabilistic tractography algorithm named Lasso bootstrap tractography (LBT) for the models that incorporate sparsity. Using a fixed tensor basis and a sparsity assumption, diffusion signals are modeled using a Lasso formulation. With the residuals from the Lasso model, a distribution of diffusion signals is obtained according to a modified Lasso bootstrap strategy. FOs are then estimated from the synthesized diffusion signals by an algorithm that improves FO estimation by enforcing spatial consistency of FOs. Finally, streamlining fiber tracking is performed with the computed FOs. The LBT algorithm was evaluated on simulated and real dMRI data both qualitatively and quantitatively. Results demonstrate that LBT outperforms state-of-the-art algorithms.     

Bootstrap white matter tractography (BOOT-TRAC)

White matter tractography is a noninvasive method for estimating and visualizing the white matter connectivity patterns in the human brain using diffusion tensor imaging (DTI) data. Sources of experimental noise may induce errors in the measured fiber directions, which will reduce the accuracy of the estimated white matter trajectories. In this study, a statistical nonparametric bootstrap method is described for estimating the dispersion and other errors in white matter tractography results. Prior studies have derived models of tractography error using the signal-to-noise ratio (SNR) and diffusion anisotropy of the DTI data. Tractography errors measured using bootstrap methods were generally consistent with an analytic model of tractography error except in areas where branching was evident. White matter tractography with bootstrap resampling is also applied to estimate the probabilities of connection between brain regions. The approach was used to generate probabilistic connectivity maps between the cerebral peduncles and specific cortical regions.     

弥散张量成像观察脑小血管病与认知障碍的关系研究进展

脑小血管病（CSVD）可导致脑卒中和痴呆,并与认知障碍密切相关。弥散张量成像（DTI）能发现脑白质纤维结构细微改变,有助于研究CSVD认知障碍。本文对DTI研究CSVD与认知障碍关系的进展进行综述。     

高阶弥散张量纤维束成像评估胶质瘤研究进展北大核心CSCD

胶质瘤为中枢神经系统常见恶性肿瘤,手术切除为治疗最有效治疗手段,而术前评估极为重要。基于弥散张量成像(DTI)的弥散张量纤维束成像(DTT)可无创显示活体内脑白质纤维束走行,定量评估胶质瘤病理分级、与白质纤维的解剖关系及瘤周白质纤维损伤程度。本文就高阶DTT评估胶质瘤研究进展进行综述。     

Diffusion tensor MRI visualizes decreased subcortical fiber connectivity in focal cortical dysplasia

Diffusion tensor imaging (DTI) was applied to 12 patients with focal cortical dysplasia (FCD) in frontal or occipital cortex. Fiber tractography was obtained from seeding points in superior longitudinal fasciculus or posterior corona radiata. Mean fractional anisotropy of fiber bundles around the affected cortex was decreased in comparison to the contralateral hemisphere with statistical significance (paired t test, P = 0.0274). On visual analysis, tractography depicted decreased volume of fiber bundles connected to the dysplastic cortex invariably even in those with a normal T2 signal intensity of underlying white matter adjacent to FCD. DTI has high potential to be applied to localize the FCD and to provide a better understanding of the pathological changes in the white matter.     

k-space and q-space: combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7 T

There is ongoing debate whether using a higher spatial resolution (sampling k-space) or a higher angular resolution (sampling q-space angles) is the better way to improve diffusion MRI (dMRI) based tractography results in living humans. In both cases, the limiting factor is the signal-to-noise ratio (SNR), due to the restricted acquisition time. One possible way to increase the spatial resolution without sacrificing either SNR or angular resolution is to move to a higher magnetic field strength. Nevertheless, dMRI has not been the preferred application for ultra-high field strength (7 T). This is because single-shot echo-planar imaging (EPI) has been the method of choice for human in vivo dMRI. EPI faces several challenges related to the use of a high resolution at high field strength, for example, distortions and image blurring. These problems can easily compromise the expected SNR gain with field strength. In the current study, we introduce an adapted EPI sequence in conjunction with a combination of ZOOmed imaging and Partially Parallel Acquisition (ZOOPPA). We demonstrate that the method can produce high quality diffusion-weighted images with high spatial and angular resolution at 7 T. We provide examples of in vivo human dMRI with isotropic resolutions of 1 mm and 800 μm. These data sets are particularly suitable for resolving complex and subtle fiber architectures, including fiber crossings in the white matter, anisotropy in the cortex and fibers entering the cortex.     

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.     

Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results

A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somatotopic map of the striatum was reconstructed, based on fMRI activations that were provoked by unilateral motor tasks. Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers.     

Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI

We present new quantitative diffusion-tensor imaging (DTI) tractography-based metrics for assessing cerebral white matter integrity. These metrics extend prior work in this area. Tractography models of cerebral white matter were produced from each subject's DTI data. The models are a set of curves (e.g., "streamtubes") derived from DTI data that represent the underlying topography of the cerebral white matter. Nine metrics were calculated in whole brain tractography models and in three "tracts-of-interest": transcallosal fibers and the left and right cingulum bundles. The metrics included the number of streamtubes and several other based on the summed length of streamtubes, including some that were weighted by scalar anisotropy metrics and normalized for estimated intracranial volume. We then tested whether patients with subcortical ischemic vascular disease (i.e., vascular cognitive impairment or VCI) vs. healthy controls (HC) differed on the metrics. The metrics were significantly lower in the VCI group in whole brain and in transcallosal fibers but not in the left or right cingulum bundles. The metrics correlated significantly with cognitive functions known to be impacted by white matter abnormalities (e.g., processing speed) but not with those more strongly impacted by cortical disease (e.g., naming). These new metrics help bridge the gap between DTI tractography and scalar analytical methods and provide a potential means for examining group differences in white matter integrity in specific tracts-of-interest.     

Diffusion-regularized susceptibility tensor imaging (DRSTI) of tissue microstructures in the human brain

Susceptibility tensor imaging (STI) has been proposed as an alternative to diffusion tensor imaging (DTI) for non-invasive in vivo characterization of brain tissue microstructure and white matter fiber architecture, potentially benefitting from its high spatial resolution. In spite of different biophysical mechanisms, animal studies have demonstrated white matter fiber directions measured using STI to be reasonably consistent with those from diffusion tensor imaging (DTI). However, human brain STI is hampered by its requirement of acquiring data at more than 10 head rotations and a complicated processing pipeline. In this paper, we propose a diffusion-regularized STI method (DRSTI) that employs a tensor spectral decomposition constraint to regularize the STI solution using the fiber directions estimated by DTI as a priori. We then explore the high-resolution DRSTI with MR phase images acquired at only 6 head orientations. Compared to other STI approaches, the DRSTI generated susceptibility tensor components, mean magnetic susceptibility (MMS), magnetic susceptibility anisotropy (MSA) and fiber direction maps with fewer artifacts, especially in regions with large susceptibility variations, and with less erroneous quantifications. In addition, the DRSTI method allows us to distinguish more structural features that could not be identified in DTI, especially in deep gray matters. DRSTI enables a more accurate susceptibility tensor estimation with a reduced number of sampling orientations, and achieves better tracking of fiber pathways than previous STI attempts on in vivo human brain.     

磁共振扩散峰度与张量成像对脑部小血管病皮质脊髓束早期华勒氏变性微观变化的诊断价值

目的探讨磁共振扩散峰度成像(DKI)与扩散张量成像(DTI)对脑部小血管病(CSVD)皮质脊髓束早期华勒氏变性微观变化的诊断价值。 方法选择2016年9月至2018年4月于汕头大学医学院第二附属医院神经内科诊断为CSVD,且常规磁共振成像(MRI)提示为腔隙性脑梗死的患者56例,为CSVD组,另选择24例常规MRI无阳性发现者为对照组,均行DKI扫描。CSVD组患者依据T₂液体衰减反转恢复(T₂-Flair)序列图像显示病灶部位不同,分脑桥组(A1组)、内囊后肢前部组(A2组)、放射冠组(A3组)和皮层下组(A4组),于脑桥、内囊后肢前部、放射冠及皮层下避开梗死灶,在下行纤维束层面双侧看似正常脑白质区测量平均峰度(MK)及DTI参数平均扩散系数(MD)和各向异性分数(FA)值。 结果A1、A2、A3、A4组患者脑桥MK值(1.19±0.11,1.18±0.11,1.20±0.10,1.20±0.09),高于对照组脑桥MK值(1.05±0.12),均差异有统计学意义(t＝-3.47,-4.10,-5.29,-5.40;均P<0.01)。A1、A3、A4组患者皮层下MK值(1.00±0.08,1.00±0.13,0.99±0.13),高于对照组皮层下MK值(0.92±0.12),均差异有统计学意义(t＝-2.12,-2.22,-2.11;均P<0.05)。4个亚组中,MK、MD及FA在4个脑区差异有统计学意义(P<0.05)的脑区数目分别为7个、1个及2个。 结论在常规MRI无阳性发现时,DKI检测CSVD患者早期华勒氏变性微观结构变化较DTI敏感。     

Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies

We investigated the relation between cognitive processing speed and structural properties of white matter pathways via convergent imaging studies in healthy and brain-injured groups. Voxel-based morphometry (VBM) was applied to diffusion tensor imaging data from thirty-nine young healthy subjects in order to investigate the relation between processing speed, as assessed with the Digit-Symbol subtest from WAIS-III, and fractional anisotropy, an index of microstructural organization of white matter. Digit-Symbol performance was positively correlated with fractional anisotropy of white matter in the parietal and temporal lobes bilaterally and in the left middle frontal gyrus. Fiber tractography indicated that these regions are consistent with the trajectories of the superior and inferior longitudinal fasciculi. In a second investigation, we assessed the effect of white matter damage on processing speed using voxel-based lesion-symptom mapping (VLSM) analysis of data from seventy-two patients with left-hemisphere strokes. Lesions in left parietal white matter, together with cortical lesions in supramarginal and angular gyri were associated with impaired performance. These findings suggest that cognitive processing speed, as assessed by the Digit-Symbol test, is closely related to the structural integrity of white matter tracts associated with parietal and temporal cortices and left middle frontal gyrus. Further, fiber tractography applied to VBM results and the patient findings suggest that the superior longitudinal fasciculus, a major tract subserving fronto-parietal integration, makes a prominent contribution to processing speed.     

DTI及tractography对失写症书写中枢及相关结构的初步分析

目的扩散张量成像技术探讨失写症脑内神经纤维改变的特征.方法失写症5例,SIEMENS Trio 2003T行常规MRI和扩散张量成像.结果 5例左侧额中回后部不同程度信号异常.DTI fiber tractography显示书写中枢纤维数量明显降低,尤其U形纤维明显,额叶脑回间、中央前回、Broca区的纤维联系明显减少,内囊后肢前部的纤维中断,书写中枢与内囊后肢前部间的关系松散.结论失写症脑内纤维改变主要以额叶脑回间的U形纤维的减少,书写中枢与内囊后肢前部间的纤维联系明显减少.