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.     

A rotation-invariant spherical harmonic decomposition method for mapping intravoxel multiple fiber structures

A new rotation-invariant spherical harmonic decomposition (SHD) method is proposed in this paper for analyzing high angular resolution diffusion (HARD) imaging. Regular SHD methods have been used to characterize the features of the apparent diffusion coefficient (ADC) profile measured by the HARD technique. However, these regular SHD methods are rotation-variant, i.e., the magnitude and/or the phase of the harmonic components changes with the rotation of the ADC profile. We propose a new rotation-invariant SHD (RI-SHD) method based on the rotation-invariant property of a diffusion tensor model. The basic idea of the proposed method is to reorient the measured ADC profile into a local coordinate system determined by the three eigenvectors of the diffusion tensor in each imaging voxel, and then apply a SHD to the ADC profile. Both simulations and in vivo experiments were carried out to validate the method. Comparisons were made between the component maps from a regular SHD method, diffusion circular spectrum mapping (DCSM) method and the proposed RI-SHD method. The results indicate that the regular SHD maps vary significantly with the rotation of the diffusion-encoding scheme, whereas the maps of the DCSM and the proposed method remain unchanged. In particular, the (0,0)-th, (2,2)-th and (4,4)-th component maps from the RI-SHD method exhibited good consistency with the 0th, 2nd and 4th order maps of the DCSM method, respectively. Compared with the regular SHD methods used in HARD imaging, the proposed RI-SHD method is superior in characterizing the diffusion patterns of multiple fiber structures between different brain regions or across subjects.     

A Bayesian framework to identify principal intravoxel diffusion profiles based on diffusion-weighted MR imaging

In this paper we introduce a new method to characterize the intravoxel anisotropy based on diffusion-weighted imaging (DWI). The proposed solution, under a fully Bayesian formalism, deals with the problem of joint Bayesian Model selection and parameter estimation to reconstruct the principal diffusion profiles or primary fiber orientations in a voxel. We develop an efficient stochastic algorithm based on the reversible jump Markov chain Monte Carlo (RJMCMC) method in order to perform the Bayesian computation. RJMCMC is a good choice for this problem because of its ability to jump between models of different dimensionality. This methodology provides posterior estimates of the parameters of interest (fiber orientation, diffusivities etc) unconditional of the model assumed. It also gives an empirical posterior distribution of the number of primary nerve fiber orientations given the DWI data. Different probability maps can be assessed using this methodology: 1) the intravoxel fiber orientation map (or orientational distribution function) that gives the probability of finding a fiber in a particular spatial orientation; 2) a three-dimensional map of the probability of finding a particular number of fibers in each voxel; 3) a three-dimensional MaxPro (maximum probability) map that provides the most probable number of fibers for each voxel. In order to study the performance and reliability of the presented approach, we tested it on synthetic data; an ex-vivo phantom of intersecting capillaries; and DWI data from a human subject.     

Probabilistic streamline q-ball tractography using the residual bootstrap

Q-ball imaging has the ability to discriminate multiple intravoxel fiber populations within regions of complex white matter architecture. This information can be used for fiber tracking; however, diffusion MR is susceptible to noise and multiple other sources of uncertainty affecting the measured orientation of fiber bundles. The proposed residual bootstrap method utilizes a spherical harmonic representation for high angular resolution diffusion imaging (HARDI) data in order to estimate the uncertainty in multimodal q-ball reconstructions. The accuracy of the q-ball residual bootstrap technique was examined through simulation. The residual bootstrap method was then used in combination with q-ball imaging to construct a probabilistic streamline fiber tracking algorithm. The residual bootstrap q-ball fiber tracking algorithm is capable of following the corticospinal tract and corpus callosum through regions of crossing white matter tracts in the centrum semiovale. This fiber tracking algorithm is an improvement upon prior diffusion tensor methods and the q-ball data can be acquired in a clinically feasible time frame.     

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.     

Estimation of fiber orientation and spin density distribution by diffusion deconvolution

A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes. A phantom study was conducted to compare the angular resolution of the fODF with the dODF, and the in vivo datasets were acquired using single-shell, two-shell, and grid sampling schemes, which were then reconstructed by QBI, GQI, and DSI, respectively. The phantom study showed that the fODF significantly improved the angular resolution over the dODF at 45- and 60-degree crossing angles. The in vivo study showed consistent fODF regardless of the applied sampling schemes and reconstruction methods, and the ability to resolve crossing fibers was improved in reduced sampling condition. The fiber spin density obtained from deconvolution showed a higher contrast-to-noise ratio than the fractional anisotropy (FA) mapping, and further application on tractography showed that the fiber spin density can be used to determine the termination of fiber tracts. In conclusion, the proposed deconvolution method is generally applicable to different q-space imaging methods. The calculated fODF improves the angular resolution and also provides a quantitative index of fiber spin density to refine fiber tracking.     

Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms

Diffusion spectrum imaging (DSI) has been demonstrated to resolve crossing axonal fibers by mapping the probability density function of water molecules diffusion at each voxel. However, the accuracy of DSI in defining individual fiber orientation and the validity of Fourier relation under finite gradient pulse widths are not assessed yet. We developed an ex vivo and an in vivo model to evaluate the error of DSI with gradient pulse widths being relatively short and long, respectively. The ex vivo model was a phantom comprising sheets of parallel capillaries filled with water. Sheets were stacked on each other with capillaries crossed at 45 degrees or 90 degrees. High-resolution T2-weighted images (T2WI) of the phantom served as a reference for the orientation of intersecting capillaries. In the in vivo model, manganese ions were infused into rats' optic tracts. The optic tracts were enhanced on T1-weighted images (T1WI) and served as a reference for the tract orientation. By comparing DSI with T2WI, the deviation angles between the primary orientation of diffusion spectrum and the 90 degrees and 45 degrees phantoms were 1.19 degrees +/- 4.82 degrees and -0.71 degrees +/- 4.91 degrees, respectively. By comparing DSI with the T1WI of rat optic tracts, the deviation angle between primary orientation of diffusion spectrum and optic tracts was -0.41 degrees +/- 6.18 degrees. In addition, two sequences of DSI using short and long gradient pulses were performed in a rat brain. The bias of the primary orientation between these two sequences was approximately 10 degrees. In conclusion, DSI can resolve crossing fiber orientation accurately. The effect of finite gradient pulse widths on the primary orientation is not critical.     

An objective method for regularization of fiber orientation distributions derived from diffusion-weighted MRI

Spherical deconvolution is an elegant method by which the orientation of crossing fibers in the brain can be estimated from a diffusion-weighted MRI measurement. However, higher resolution of fiber directions comes at the cost of higher susceptibility to noise. In this study, we describe the use of linear regularization of the fiber orientation distribution function by Damped Singular Value Decomposition. Furthermore, the degree of regularization is optimized on a voxel-by-voxel basis with no user interaction using Generalized Cross Validation. We find, by simulations, that regularization can improve the reliability of fiber orientation determination when the signal-to-noise ratio is low. Simulations and in vivo measurements indicate that spurious peaks of the fiber orientation distribution function in regions with low anisotropy largely disappear when regularization is introduced. The methods examined are fast enough to be used on a routine basis with diffusion MRI data sets and may improve estimation of water diffusion properties in heterogeneous white matter and boost reliability of fiber tracking through regions of brain with complex fiber geometry.     

弥散频谱成像的研究进展

研究表明人类大脑中的很多区域内存在着多方向的纤维束,这类区域的体素内水分子的平均弥散并不符合高斯分布。因此,基于单方向纤维束模型的弥散张量成像在揭示组织微观结构中存在先天缺陷。弥散频谱成像使用多b值多方向的扫描序列,采集水分子在整个q空间的弥散信息,通过一个具有高角分辨率的概率密度函数来描述水分子的弥散运动,能够可靠地观测单体素内多方向的纤维束,并据此进行纤维束追踪重建出真实而复杂的组织结构。作者全面介绍了近年来弥散频谱成像在基本原理、方法学和应用方面的研究进展,为推动国内弥散成像技术在科研和临床上的发展提供帮助。     

Informed constrained spherical deconvolution (iCSD)

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.     

Evaluation of the accuracy and angular resolution of q-ball imaging

Q-ball imaging (QBI) has been proposed for the mapping of multiple intravoxel fiber structures using the Funk-Radon transform on high angular resolution diffusion images (HARDI). However, the accuracy and the angular resolution of QBI to define fiber orientations and its dependence on diffusion imaging parameters remain unclear. The phantom models, made up of sheets of parallel capillaries filled with water, were designed to evaluate the accuracy and the angular resolution of QBI at different |q| values. With an inner diameter of 20 mum and an outer diameter of 90 mum, the capillaries afforded a restrictive environment compared with the diffusion measurement scale. Further, the angular resolutions of QBI at various |q| value were also quantified on the corpus callosum in the human brain. The full width at half maximum (FWHM) of the main lobe of normalized orientation distribution function (nODF) was calculated and adopted to quantify the angular resolution of QBI. With the phantom model, a higher |q| value resulted in worse accuracy but better angular resolution for QBI. The same trend where a higher |q| value yielded a better angular resolution was also observed in the human study. Upon comparison of QBI with T2WI, QBI with |q|=277 cm(-1) (b=3000 s/mm(2)) was found to be insufficient to differentiate capillaries crossing at 45 degrees . However, when encoding with |q|=320, 358, and 392 cm(-1) (b=4000, 5000, and 6000 s/mm(2)), the deviation angles between the primary ODF and the 45 degrees phantoms were -4.91 degrees +/-2.72 degrees , -1.37 degrees +/-2.32 degrees , and -0.69 degrees +/-1.54 degrees with adequate signal-to-noise ratio (SNR). These results were consistent with the FWHM-nODF, which showed that a |q| value of 320 cm(-1) was the threshold to resolve capillaries intersecting at 45 degrees . Additionally, it was demonstrated in both the phantom model and the human brain that QBI encoding with lower |q| values may result in underestimation of the orientations of the crossing fibers. In conclusion, QBI was found to accurately resolve crossing fiber orientations and was highly dependent on the selected |q| value.     

White matter atlas generation using HARDI based automated parcellation

Most diffusion imaging studies have used subject registration to an atlas space for enhanced quantification of anatomy. However, standard diffusion tensor atlases lack information in regions of fiber crossing and are based on adult anatomy. The degree of error associated with applying these atlases to studies of children for example has not yet been estimated but may lead to suboptimal results. This paper describes a novel technique for generating population-specific high angular resolution diffusion imaging (HARDI)-based atlases consisting of labeled regions of homogenous white matter. Our approach uses a fiber orientation distribution (FOD) diffusion model and a data driven clustering algorithm. White matter regional labeling is achieved by our automated data driven clustering algorithm that has the potential to delineate white matter regions based on fiber complexity and orientation. The advantage of such an atlas is that it is study specific and more comprehensive in describing regions of white matter homogeneity as compared to standard anatomical atlases. We have applied this state of the art technique to a dataset consisting of adolescent and preadolescent children, creating one of the first examples of a HARDI-based atlas, thereby establishing the feasibility of the atlas creation framework. The white matter regions generated by our automated clustering algorithm have lower FOD variance than when compared to the regions created from a standard anatomical atlas.     

Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images

Diffusion MR imaging provides a unique tool to probe the microgeometry of nervous tissue and to explore the wiring diagram of the neural connections noninvasively. Generally, a forward model is established to map the intra-voxel fiber architecture onto the observable diffusion signals, which is reformulated in this article by adopting a measure-theoretic approach. However, the inverse problem, i.e., the spherical deconvolution of the fiber orientation density from noisy MR measurements, is ill-posed. We propose a nonparametric representation of the tangential distribution of the nerve fibers in terms of a Dirichlet process mixture. Given a second-order approximation of the impulse response of a fiber segment, the specified problem is solved by Bayesian statistics under a Rician noise model, using an adaptive reversible jump Markov chain Monte Carlo sampler. The density estimation framework is demonstrated by various experiments with a diffusion MR dataset featuring high angular resolution, uncovering the fiber orientation field in the cerebral white matter of the living human brain.     

A filtered approach to neural tractography using the Watson directional function

We propose a technique to simultaneously estimate the local fiber orientations and perform multi-fiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a filter as a post-processing step to obtain a smooth trajectory. We formulate fiber tracking as causal estimation: at each step of tracing the fiber, the current estimate of the signal is guided by the previous. To do this, we model the signal as a discrete mixture of Watson directional functions and perform tractography within a filtering framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides an accurate estimate of the local structure at each point along the fiber. We choose the Watson function since it provides a compact representation of the signal parameterized by the principal diffusion direction and a scaling parameter describing anisotropy, and also allows analytic reconstruction of the oriented diffusion function from those parameters. Using a mixture of two and three components (corresponding to two-fiber and three-fiber models) we demonstrate in synthetic experiments that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments examine the corpus callosum and internal capsule and confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.     

NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction

We present a high angular resolution brain atlas constructed by averaging 90 diffusion spectrum imaging (DSI) datasets in the ICBM-152 space. The spatial normalization of the diffusion information was conducted by a novel q-space diffeomorphic reconstruction method, which reconstructed the spin distribution function (SDF) in the ICBM-152 space from the diffusion MR signals. The performance of this method was examined by a simulation study modeling nonlinear transformation. The result showed that the reconstructed SDFs can resolve crossing fibers and that the accumulated quantitative anisotropy can reveal the relative ratio of the fiber populations. In the in vivo study, the SDF of the constructed atlas was shown to resolve crossing fiber orientations. Further, fiber tracking showed that the atlas can be used to present the pathways of fiber bundles, and the termination locations of the fibers can provide anatomical localization of the connected cortical regions. This high angular resolution brain atlas may facilitate future connectome research on the complex structure of the human brain.     

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.     

Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI

Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions of crossing fibers, however, the tensor model fails because it cannot represent multiple, independent intra-voxel orientations. Most of the methods that have been proposed to resolve this problem require diffusion magnetic resonance imaging (MRI) data that comprise large numbers of angles and high b-values, making them problematic for routine clinical imaging and many scientific studies. We present a technique based on compressed sensing that can resolve crossing fibers using diffusion MRI data that can be rapidly and routinely acquired in the clinic (30 directions, b-value equal to 700 s/mm²). The method assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation. A fast algorithm for computing the best orientations based on a hierarchical compressed sensing algorithm and a novel metric for comparing estimated orientations are also proposed. The performance of this approach is demonstrated using both simulations and in vivo images. The method is observed to resolve crossing fibers using conventional data as well as a standard q-ball approach using much richer data that requires considerably more image acquisition time.     

Mossy fiber sprouting in pilocarpine-induced status epilepticus rat hippocampus: a correlative study of diffusion spectrum imaging and histology

Mossy fiber sprouting (MFS) is the main characteristic of temporal lobe epilepsy (TLE), which is highly correlated with the frequencies of recurrent seizures as well as degrees of severity of TLE. A recent MRI technique, referred to as diffusion spectrum imaging (DSI), can resolve crossing fibers and investigate the intravoxel heterogeneity of water molecular diffusion. Being able to achieve higher accuracy in depicting the complex fiber architecture, DSI may help improve localization of the seizure-induced epileptic foci. In this study, two indices of DSI, which represented the mean diffusivity (MSL) and diffusion anisotropy (DA), were proposed. A correlative study between diffusion characteristics and the severity of MFS was investigated in the pilocarpine-induced status epilepticus (SE) rat model. Nine SE rats and five control rats were studied with MRI and histological Timm's staining. For MSL, no significant correlation was found in the dentate gyrus (DG), r=-0.36; p=0.2017, and positive correlation was found in cornu ammonis (CA3), r=0.62; p=0.0174. The correlation between DA and Timm's score showed positive correlation in DG, r=0.71; p=0.0047, and negative correlation in CA3, r=-0.63; p=0.0151. Our results were compatible with the previous reports on fiber architecture alterations in DG and CA3 subregions. In conclusion, the histological correspondence of DSI indices was demonstrated. With DSI indices, longitudinal follow-up of hippocampal fiber architecture can be achieved to elucidate the pathophysiology of TLE, which might be helpful in disease localization.     

Collateral nerve fibers in human spinal cord: visualization with magnetic resonance diffusion tensor imaging

Diffusion tensor magnetic resonance imaging provides structural information about nerve fiber tissue. The first eigenvector of the diffusion tensor is aligned with the nerve fibers, i.e., longitudinally in the spinal cord. The underlying hypothesis of this study is that the presence of collateral nerve fibers running orthogonal to the longitudinal fibers results in an orderly arrangement of the second eigenvectors. Magnetic resonance diffusion tensor scans were performed with line scan diffusion imaging on a clinical MR scanner. Axial sections were scanned in a human cervical spinal cord specimen at 625 microm resolution and the cervical spinal cord of four normal volunteers at 1250 microm resolution. The spinal cord specimen was fixed and stained for later light microscopy of the collateral fiber architecture at 0.53 microm resolution. Diffusion measured by MR was found to be anisotropic for both white and gray matter areas of the spinal cord specimen; the average fractional anisotropy (FA) was 0.63 +/- 0.09 (diffusion eigenvalues lambda1 0.38 +/- 0.05 micros/mm2, lambda2 0.14 +/- 0.03 micros/mm2, lambda3 0.10 +/- 0.03 micros/mm2) in white matter and 0.27 +/- 0.04 (lambda1 0.36 +/- 0.04 micros/mm2, lambda2 0.28 +/- 0.03 micros/mm2, lambda3 0.21 +/- 0.04 micros/mm2 in gray matter. The normal-volunteer FA values were similar, i.e., 0.66 +/- 0.04 (lambda1 1.66 +/- 0.14 micros/mm2, lambda2 0.55 +/- 0.02 micros/mm2, lambda3 0.40 +/- 0.01 micros/mm2) in white matter and 0.35 +/- 0.03 (lambda1 1.14 +/- 0.07 micros/mm2, lambda2 0.70 +/- 0.03 micros/mm2, lambda3 0.58 +/- 0.02 micros/mm2) in gray matter. The first eigenvector pointed, as expected, in the longitudinal direction. The second eigenvector directions exhibited a striking arrangement, consistent with the distribution of interconnecting collateral nerve fibers discerned on the histology section. This finding was confirmed for the specimen by quantitative pixel-wise comparison of second eigenvector directions and collateral fiber directions assessed on light microscopy image data. Diffusion tensor MRI can reveal non-invasively and in great detail the intricate fiber architecture of the human spinal cord.     

Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts.

Noninvasive mapping of white matter tracts using diffusion tensor magnetic resonance imaging (DTMRI) is potentially useful in revealing anatomical connectivity in the human brain. However, a gold standard for validating DTMRI in defining axonal fiber orientation is still lacking. This study presents the first validation of the principal eigenvector of the diffusion tensor in defining axonal fiber orientation by superimposing DTMRI with manganese-enhanced MRI of optic tracts. A rat model was developed in which optic tracts were enhanced by manganese ions. Manganese ion (Mn(2+)) is a potent T1-shortening agent and can be uptaken and transported actively along the axon. Based on this property, we obtained enhanced optic tracts with a T1-weighted spin-echo sequence 10 h after intravitreal injection of Mn(2+). The images were compared with DTMRI acquired with exact spatial registration. Deviation angles between tangential vectors of the enhanced tracts and the principal eigenvectors of the diffusion tensor were then computed pixel by pixel. We found that under signal-to-noise (SNR) of 30, the variance of deviation angles was (13.27 degrees). In addition, the dependence of this variance on SNR obeys stochastic behavior if SNR is greater than 10. Based on this relation, we estimated that an rms deviation of less than 10 degrees could be achieved with DTMRI when SNR is 40 or greater. In conclusion, our method bypasses technical difficulties in conventional histological approach and provides an in vivo gold standard for validating DTMRI in mapping white matter tracts.