Diffusion Tensor MR Imaging of the Brain and White Matter Tractography
In the last 10-15 years, MR imaging techniques have been increasingly applied to the study of molecular displacement (diffusion) in biologic tissue [1, 2]. The ability to spatially map the diffusion of free water protons in vivo using 1H MR imaging and the observation that the diffusion of free water protons is reduced in acutely infarcted brain tissue are responsible for the widespread use of these techniques in clinical imaging [3,4,5,6,7,8]. More recently, the dependency of molecular diffusion on the orientation of white matter fiber tracts has elicited great interest in studying the factors that influence this dependency and in spatially mapping these fiber tracts using diffusion imaging [9,10,11,12,13,14,15,16,17,18,19,20,21].
In this paper, we briefly describe the tensor theory used to characterize molecular diffusion in white matter and how the tensor elements are measured experimentally using diffusion-sensitive MR imaging. We then review techniques for acquiring relatively high-resolution diffusion-sensitive MR images and computer-based algorithms that allow the generation of white matter fiber tract maps from the tensor data. We provide an overview of current experience and some clinical examples that are ongoing in our center. Finally, we discuss the possible future role of these white matter maps in the assessment of white matter diseases, congenital brain malformations, central nervous system neoplasms (presurgical evaluation), and brain function.
Background
Random motion of water molecules (diffusion) in the presence of a strong magnetic gradient results in MR signal loss as a result of the dephasing of spin coherence (Fig. 1). The application of a pair of strong gradients to elicit differences in the diffusivity of water molecules among various biologic tissues is known as diffusion sensitization or diffusion weighting [22, 23]. The degree of diffusion weighting is described by the b value, a parameter that is determined by the type of sensitizing gradient scheme implemented in the MR experiment. For the Stejskal-Tanner spin-echo scheme [24] (Fig. 2)—a pulsed pair of approximately rectangular gradients around a 180° radiofrequency pulse that is most commonly implemented on clinical MR scanners—the b value is determined by the duration (δ) and strength (G) of the sensitizing pulsed gradients, and the time interval between the two pulsed gradients (Δ) is determined according to the equation:where γ is the gyromagnetic ration. Thus, the b value (diffusion sensitization) can be increased by using stronger (G) and longer (δ) pulsed gradients or by lengthening the time between the pulsed gradients (Δ).
\[ \[\ \mathrm{b\ value}={\gamma}^{2}G^{2}{\delta}^{2}({\Delta}-{\delta}/3),\ \] \]
Adding diffusion-sensitizing gradients to an imaging sequence (spatial encoding) constitutes the basis for diffusion-weighted MR imaging. The signal intensity (S) in every voxel of a diffusion-weighted MR image is influenced by the choice of b value and pulse sequence TE and by two parameters intrinsic to biologic tissues: apparent diffusion coefficient (ADC), a coefficient that reflects molecular diffusivity in the presence of restrictions, such as viscosity and spatial barriers; and spin—spin relaxation time (T2). The following formula describes the relationship between signal intensity in a diffusion-weighted MR image and the different parameters:where S0 is the signal intensity at a b value of 0; or the natural logarithm
\[ \[\ \mathrm{S}=\mathrm{S}_{0}{\ }\mathrm{e}^{-\mathrm{b(ADC)}},\ \] \]
\[ \[\ \mathrm{Ln{\ }(S{\ }/{\ }S}_{0})=-\mathrm{b(ADC)}.\ \] \]
Acquiring diffusion-weighted images with at least two different b values (commonly 20 and 1000 sec/mm2) while keeping the TE fixed allows the determination of the ADC value for each image voxel (Fig. 3). The lower of the two b values is purposefully selected to be slightly greater than zero to eliminate the effects of large vessels and flow. Assigning a gray scale to the range of ADC values in the different voxels constitutes an ADC map (Fig. 4A,4B,4C). The ADC map provides contrast based purely on differences in diffusivity of water in biologic tissue that is not contaminated by differences in T2 relaxation times (T2 shine-through).
In white matter, the diffusion of free water molecules is not the same in all directions of a three-dimensional space (anisotropy) [9, 10]. Diffusion anisotropy is predominantly caused by the orientation of fiber tracts in white matter and is influenced by its micro- and macrostructural features [25]. Of the microstructural features, intraaxonal organization appears to be of greatest influence on diffusion anisotropy; other features include density of fiber and neuroglial cell packing, degree of myelination, and individual fiber diameter. On a macroscopic scale, the variability in the orientation of all white matter tracts in an imaging voxel influences the degree of anisotropy assigned to that voxel [25].
Diffusion anisotropy is characterized by a 3 × 3 second-rank tensor. A tensor is a mathematic construct that describes the properties of an ellipsoid in three-dimensional space (Fig. 5A,5B). In diffusion tensor imaging, the diffusion properties of water are measured in the laboratory frame of reference, using the spatial coordinates x, y, and z (z is the axis along the main magnetic field). In this laboratory frame, the tensor matrix has nine nonzero elements, of which three are the same (symmetric tensor). The remaining six elements (Dxx, Dyy, Dzz, Dxy, Dxz, and Dyz) for each voxel are calculated from six images obtained by applying diffusion-sensitizing gradients in at least six non-colinear directions (for example: xx, yy, zz, xy, xz, and yz) in addition to a non—diffusion-weighted image. A property of second-rank tensors is that they can always be diagonalized, leaving only three nonzero elements along the main diagonal of the tensor—namely, the eigenvalues (λ1, λ2, λ3). The eigenvalues reflect the shape or configuration of the ellipsoid; and their sum (trace = λ1 + λ2 + λ3), which is independent of the orientation of the ellipsoid (rotationally invariant), reflects the size of the ellipsoid [18]. The mathematic relationship between the principal coordinates of the ellipsoid and the laboratory frame is described by the eigenvectors (v1, v2, v3). The ellipsoid's surface represents the root mean square diffusive displacement of free water in anisotropic media.
From diffusion tensor imaging, the eigenvectors can be characterized in each voxel. Several measures of diffusion anisotropy, including fractional anisotropy, relative anisotropy, and volume ratio, can be calculated on the basis of formulas that incorporate the tensor elements (Appendix 1) to generate quantitative brain maps [26] (Fig. 6A,6B,6C,6D). A more powerful approach than just using anisotropy maps is to include the knowledge of the eigenvalues and eigenvectors to generate white matter color maps, in which the intensity represents anisotropy and the color represents direction. In addition to the two-dimensional color maps, three-dimensional white matter fiber tract maps can be created that are based on similarities between neighboring voxels in the shape (quantitative diffusion anisotropy measures) and orientation (principal eigenvector map) of the diffusion ellipsoid [19, 21] (Fig. 6A,6B,6C,6D). The algorithms used to generate these maps are detailed in the next section.
Methodology
Diffusion tensor MR imaging is the only noninvasive in vivo method for mapping white matter fiber tract trajectories in the human brain. However, this process is technically and mathematically demanding and requires several advances in the fields of diffusion-weighted MR imaging and data processing [21, 27,28,29,30]. First, high-quality MR images are mandatory. Recent MR scanner hardware improvements, including better stability and homogeneity of the main magnetic field, as well as stronger and faster magnetic gradients, have been imperative for the acquisition of high-quality (signal-to-noise ratio and spatial resolution) diffusion-weighted MR images in a reasonable time. Furthermore, several schemes to reduce commonly encountered artifacts related to motion, eddy currents, and field inhomogeneity have also been critical for the success of diffusion tensor MR imaging. Second, a robust mathematic framework capable of generating a smooth representation of the macroscopic white matter fiber tract direction from discrete, coarsely sampled, and voxel-averaged diffusion tensor data has to be developed. Third, a computer-based algorithm that allows the reliable following or tracking of the individual white matter fiber tracts must be created.
Data Acquisition
The process of data acquisition in diffusion tensor MR imaging consists of diffusion sensitization and spatial encoding. Diffusion tensor imaging is influenced by the strength, number, and orientation (diffusion tensor encoding scheme) of the sensitizing gradients. Regarding the strength of the diffusion-sensitizing gradients, Melhem et al. [31] have shown an effect of b value on the measures of diffusivity and anisotropy in different anatomic locations in the brain. On the other hand, the impact of the number of b values on these measures has been minimal in the range between 0 and 1000 sec/mm2 [32]. Regarding the number of directions and the orientation of the diffusion-sensitizing gradients, more uniform scanning of three-dimensional space has been shown to result in a reduction in noise in raw diffusion-encoded measurements and to improve the accuracy of diffusion tensor estimates and fiber tracking applications. Uniform scanning of three-dimensional space is achieved by increasing the number of sensitizing gradient directions (up to 642 directions) and optimizing their spatial orientation [33, 34].
Recently, on the basis of analytic comparisons and Monte Carlo simulations, Hasan et al. [35] have found, using a single-tensor model of diffusion, that there is no substantial advantage to implementing more than six encoding directions as long as the orientation of the gradients (diffusion tensor encoding schemes) is optimized. Those authors also addressed the influence of various diffusion tensor encoding schemes, including heuristic, numerically optimized, and geometric polyhedra, on the accuracy of diffusion tensor estimates; they found that the commonly implemented six-directional heuristic schemes are suboptimal [35].
A variety of spatial encoding schemes have been proposed for diffusion tensor MR imaging. The most commonly used in practice are based on echoplanar readout and include single-shot (so-called snapshot) and multishot (or interleaved, segmented) techniques. The advantages of multishot echoplanar imaging compared with single-shot are greater spatial resolution, greater signal-to-noise ratio, and less susceptibility-related distortion. The major disadvantage is longer data acquisition time, which makes multishot echoplanar techniques more susceptible to artifacts related to respiration, cerebrovascular and cerebrospinal fluid flow, eye motion, and involuntary head motion. In diffusion-sensitive multishot echoplanar imaging, even small bulk motions can cause ghosting artifacts because of discontinuities between the echoes sampled at different times in k-space. In single-shot echoplanar imaging sequences, these discontinuities in k-space are eliminated because the entire set of echoes is acquired with the same motion-induced error [23]. To reduce motion-related artifacts in diffusion-sensitive multishot echoplanar techniques, cardiac gating and motion correction schemes such as navigator echo have been implemented [36,37,38,39,40]. Navigator echo correction, which is a non—phase-encoded readout gradient echo, provides a measure of the motion-induced phase variations between the echoes in each shot and corrects for small amounts of motion [40]. Diffusion-sensitive multishot echoplanar techniques with cardiac gating and navigator echo correction provide images with reduced motion artifacts and great spatial resolution that are essential for evaluating the micro-structures of white matter in vivo [40, 41].
Other spatial encoding schemes used in diffusion tensor imaging include gradient-and spin-echo (GRASE), fast spin-echo, and line scanning [42,43]. Both GRASE and fast spin-echo techniques are resistant to distortions from static field inhomogeneities and diffusion sensitization gradient-induced eddy currents compared with single-shot echoplanar techniques. However, GRASE and fast spin-echo suffer from a low signal-to-noise ratio and relatively long acquisition times [44]. Diffusion tensor line scanning or column-selective excitation techniques are occasionally used in imaging children because of the inherent insensitivity of those techniques to variations in the phase of the MR signal induced by physiologic motion [42,45]. Again, the main disadvantages of this technique are an inherently low signal-to-noise ratio (because only a single column contributes to the MR signal) and relatively long acquisition times.
In this review, brain diffusion tensor MR images are generated on a 1.5-T scanner equipped with a 2.3 G/cm triple-axis gradient system and a head coil operating in receive mode. The imaging protocol consists of a diffusion-sensitive, cardiac-gated, navigator echo—corrected, two-dimensional multishot spin-echo echoplanar readout. The TE is fixed at 92 msec, whereas the TR varies with the heart rate (5000-6000 msec). The number of echoes acquired in each TR interval is 18 (1 navigator echo and 17 phase-encoded echoes). The field of view ranges from 12 × 12 to 25 × 25 cm, depending on the head size. The number of sampling points along frequency- and phase-encoding directions ranges from 64 to 128 interpolated to 128 to 256 (zero filling) to maintain a pixel size of 0.98 mm2. Forty interleaved, 3-mm-thick slices are acquired in the axial plane using the multislice mode. Diffusion sensitization is performed along six noncolinear directions (heuristic encoding schemes: [1, 0, 0]: [0, 1, 0]: [0, 0, 1]: [0, 2-1/2, 2-1/2]: [2-1/2, 0, 2-1/2]: [2-1/2, 2-1/2, 0]) using diffusion weighting of b = 600 sec/mm2 at the maximum gradient strength of 2.1 G/cm. A reference image with low diffusion weighting (b = 33 sec/mm2) is also recorded. A single set of these seven measurements takes 4-5 min depending on heart rate. These measurements are repeated at least six times to increase the signal-to-noise ratio. The images are checked and the acquisition is repeated when motion-related degrading artifacts are found. In addition, double-echo (TEs of 22 and 92 msec) two-dimensional multishot spin-echo echoplanar MR imaging is performed for anatomic guidance. The diffusion tensor and double-echo MR imaging protocols are matched for spatial orientation and resolution to facilitate the coregistration process. The entire examination requires approximately 50 min.
Data Correction and Processing
Misregistration of diffusion tensor MR images has deleterious effects on spatial resolution and on the accuracy of diffusivity and anisotropy estimates [46]. Misregistration is caused by variations in geometric distortions and by misalignment resulting from minimal motion between the various measurements. The geometric distortions are caused by static field inhomogeneity resulting from imperfect shimming and differences in magnetic properties of adjacent tissues and from diffusion sensitization gradient-induced eddy currents [41,44,46]. Unwarping algorithms, using inhomogeneity field maps, are used for the correction of distortion originating from the static magnetic field inhomogeneities [41]. For the correction of diffusion sensitization gradient-induced eddy current distortions, two methods are often used. The first method entails the application of least squares straight line fits and cross-correlation functions to the diffusion-weighted image [46,47]. The second method uses a one-dimensional inhomogeneity field map in the read and phase-encoding directions in each slice and each diffusion-sensitizing direction step to correct the distortion [41]. Motion-related misalignment can be corrected by computer-based algorithms, such as Automated Image Registration software [48], that take into account differences in contrast and spatial resolution between the coregistered images.
From the diffusion tensor MR imaging data, quantitative, directionally invariant (relatively independent of the diffusion gradient directions, head position, and rotation) indexes can be derived that measure distinct intrinsic features of water diffusion in biologic tissue. The most fundamental quantitative measures are the three principal diffusivities (eigenvalues) of the diffusion tensor, which are the principal diffusion coefficients measured along the three principal coordinates of the ellipsoid in each voxel. From the eigenvalues, several indexes that measure the degree of diffusion anisotropy in tissues have been proposed. The most intuitive and simplest indexes are ratios of the principal diffusivities [49], such as the dimensionless anisotropy ratio λh / λl or 2λh / (λm + λl), which measure the relative magnitudes of the diffusivities along the longest axis of the diffusion ellipsoid and other orthogonal axes. Here the eigenvalues are sorted in order of decreasing magnitude (λh: highest diffusivity; λm: intermediate diffusivity; and λl: lowest diffusivity). Because simulations have revealed that these sorted indexes are statistically biased by noise contamination [49], other nonsorted indexes, such as volume ratio, relative anisotropy, and fractional anisotropy, have been proposed [49] (Appendix 1). Volume ratio—the ratio of the volume of an ellipsoid to the volume of a sphere whose radius is the averaged diffusivity—ranges between 0 and 1, where 0 means highest anisotropy and 1 indicates complete isotropic diffusion. Relative anisotropy—the ratio of the variance of the eigenvalues to their mean—and fractional anisotropy—the ratio of the anisotropic component of the diffusion tensor to the whole diffusion tensor—both vary between 0 (isotropic diffusion) and 1 (infinite anisotropic diffusion).
Simulations have shown a rapid divergence of the eigenvalues and invariant anisotropy indexes from true values as the signal-to-noise ratio decreases [49, 50]. In tissues with low-level anisotropy, this divergence is further exaggerated in anisotropy indexes, requiring sorting of the eigenvalues. Indexes such as the “lattice” anisotropy index that include all the information of the eigenvectors (both shape and orientation of the ellipsoid) and do not require sorting have been shown to reduce the effect of noise on the measurement [49].
In this review, motion-related misalignment of the diffusion tensor MR images is corrected off-line using Automated Image Registration. Subsequently, the voxel intensities of the multiple diffusion-weighted images are fitted using multivariant linear least square fitting to obtain the six elements of the symmetric diffusion tensor. The diffusion tensors at each voxel are diagonalized to obtain eigenvalues and eigenvectors for each voxel. The eigenvector (v1) associated with the largest eigenvalue (λ1) is used to represent the local fiber direction. Fractional anisotropy maps are calculated from the eigenvalues on the basis of standard formulas (Appendix 1). White matter color maps are created on the basis of the three vector elements of the eigenvector v1 for each voxel. The absolute values of the vector elements are assigned to red (x element), green (y element), and blue (z element) [12]. If the principal eigenvector is aligned along the x-axis, pure red is assigned to the corresponding voxel, whereas if the eigenvector is 45° between the x- and y-axes, yellow (red plus green) is assigned to the voxel. The intensity of color in each voxel is gauged by the degree of fractional anisotropy (Fig. 6A,6B,6C,6D).
White Matter Fiber Tracking
Methods to reconstruct white matter tracts can be placed into two broad categories [19, 21, 51,52,53,54]. Methods in the first category are based on line propagation algorithms that use local tensor information for each step of the propagation. Simple line propagation techniques that connect voxels on the basis of discrete number fields (local principal eigenvector orientation) are incapable of providing accurate representation of white matter tracts [19]. Improvements in line propagation techniques using continuous, rather than discrete, number fields can provide connections that follow the actual white matter tract [19, 52]. Furthermore, line propagation techniques can be modified to create a smooth (curved) path by interpolating the vector of the principal axis or the whole diffusion tensor at each coordinate as a line is propagated [21, 51].
Methods in the second category are based on global energy minimization to find a path between two predetermined voxels with minimum energy violation. This category includes the methods of fast marching (Parker GJ et al., presented at the International Society of Magnetic Resonance meeting, April 2000) and simulated annealing (Tuch DS et al., presented at the ISMRM meeting, April 2001).
In this review, fiber tracking is performed using a line propagation technique based on continuous number fields developed at our center called “fiber assignment using continuous tracking” [19]. Tracking is launched from a seed voxel from which a line is propagated in both retrograde and antegrade directions according to the principal eigenvector at each voxel. Tracking propagates on the basis of the orientation of the eigenvector that is associated with the largest eigenvalue. Tracking is terminated when it reaches a voxel with fractional anisotropy lower than a threshold of 0.25-0.35 and when the angle between the two principal eigenvectors is greater than 35-40° (Fig. 7).
Fractional anisotropy thresholds help to exclude gray matter and to segment white matter tracts that are separated by gray matter (Fig. 7). In the situation in which two white matter tracts are close (Fig. 7), the angle between the principal eigenvectors of the adjacent white matter voxels becomes an important criterion for adequate segmentation. Our choice of fractional anisotropy and angle thresholds is based on experiments that evaluated the reliability of tracking the corticospinal tract using different thresholds.
Knowing the fiber projections in relation to anatomic landmarks, we define multiple regions of interest. The use of multiple regions of interest allows separation of white matter tracts that are adjacent to each other in one anatomic location and distant in another. For example, at the level of the cerebral peduncle, it is difficult to separate the different corticopontospinal tracts; placing another region of interest in the lower pons facilitates their separation (Fig. 8A,8B,8C).
Tracking can be achieved in one of two ways. The first method consists of initiating tracking from each voxel included in the region of interest. This method can delineate a limited number of branching patterns of the tract of interest (if the region of interest contains 10 voxels, only 10 tracking results delineate the tract). In the second method, fiber tracking is initiated from the center of every voxel in the brain, but only fibers passing through the specified regions of interest are retained [51]. In this approach, multiple tracts penetrate the region of interest, thus revealing a more comprehensive structure of the tract.
One of the difficulties in tracking a fiber path from the three-dimensional vector matrix is to translate the discrete vector information to continuous fiber tracts. The fiber-assignment-using-continuous-tracking algorithm maintains the information of intercept when the tracking exits a voxel by performing the tracking in a continuous number field rather than in a discrete number field (Fig. 9A,9B).
The final step of this process is coregistering the three-dimensional white matter fiber tracts on the anatomic T2-weighted images using Automated Image Registration software.
Applications
Diffusion tensor imaging data promise a range of applications in clinical medicine. Initially, diffusion anisotropy was observed in skeletal muscle [55]. Since then, diffusion tensor measures have been used to study fibrous organized structures like human and animal spinal cords [56], myocardium [57, 58], intervertebral discs [59], and cerebral white matter [60].
Clinically, properties derived from the diffusion tensor like the trace, which reflects overall water content, have been used successfully to evaluate brain ischemia [61, 62]. Measures of the diffusion tensor have also been used to investigate brain development [17, 63] and to aid in neurosurgical planning. In addition, parameters derived from the diffusion tensor, such as anisotropy indexes, have been used to evaluate white matter disease in Krabbe's disease [64], cerebral adrenoleukodystrophy [65], AIDS [66], multiple sclerosis [20, 67, 68], hypertensive encephalopathy [69], age-related changes [70], schizophrenia [71], Alzheimer's disease [72], ischemic leukoaraiosis [73], and epilepsy [74]. Other studies have also probed the potential of diffusion tensor MR imaging in brain tumors [75], migraines [76], and eclampsia [77].
Requisites for sound application of diffusion tensor MR imaging and white matter tractography in central nervous system disease are the establishment of normative anisotropy values and white matter tract maps, and the validation of the normative white matter tract maps on the basis of detailed anatomic knowledge of these tracts. As illustrated in Figures 6A,6B,6C,6D and 10A,10B, it is feasible to map anisotropy values and white matter tracts for both the infra- and supratentorial compartments of the brain in healthy volunteers. However, the process of anatomic validation has been more difficult because of a lack of an appropriate gold standard. In fact, diffusion tensor MR imaging is the only method available that has the potential for tracking white matter tracts in vivo. Attempts at in vitro validation based on white matter histology are limited because of geometric distortions resulting from dissection, freezing, dehydration, fixation, cutting into thin slices, and thawing of the histologic samples [21]. The radiologist interpreting these maps needs to have a detailed knowledge of the location, origin, and termination of the different white matter tracts in the central nervous system. Furthermore, a thorough understanding of the effects of artifacts originating from image acquisition (diffusion tensor MR imaging), data processing, and the tracking algorithm on the accuracy of the geometry (shape) and topology (branching) of the white matter tracts is imperative for proper interpretation of the maps. Specifically, misregistration of diffusion tensor MR images caused by eddy currents, ghosting due to motion, and signal loss due to susceptibility variations can all affect the computed trajectory of the fiber tract. Furthermore, the tracking algorithm may fail to provide an accurate representation of the fiber trajectory when a voxel contains nonuniformly distributed fibers, curved fibers, or two or more interdigitating fiber populations. This failure is largely because the direction of the measured principal eigenvector is based on a voxel average and does not necessarily represent the trajectories of individual microscopic tracts [21].
At our center, diffusion tensor MR imaging and white matter tractography are being applied in three major clinical research projects. The first project studies children with a cerebral form of adrenoleukodystrophy, a genetic disorder that involves the white matter, adrenal cortex, and testes [78]; the second studies patients with spastic cerebral palsy resulting from periventricular leukomalacia, a dominant pattern of anoxic brain injury in premature infants [79]; and the third studies infants with holoprosencephaly, a congenital brain malformation attributed to errors in ventral induction [80].
In adrenoleukodystrophy, diffusion tensor MR imaging may help identify affected white matter that is not evident on conventional MR imaging, differentiate between potentially reversible and irreversibly damaged white matter, and categorize affected white matter on the basis of well-defined histopathologic zones (Fig. 11A,11B). Information derived from ADC and fractional anisotropy measures in these patients may also shed light on axonal and myelin ultrastructure contributions to the anisotropy phenomena. Preliminary results show that affected white matter can be divided into three distinct zones on the basis of differences in diffusion values that may reflect varying degrees of axonal and myelin loss (Table 1). The effect of white matter lesions on specific commissural (corpus callosum) (Fig. 12A,12B,12C,12D), projectional (corticospinal), and association tracts is currently being correlated with neurologic and neuropsychologic function. Similar hypotheses are being tested in patients with periventricular leukomalacia. The severity of injury to the periventricular white matter determines the degree of damage to white matter tracts traversing the injured zone (Fig. 13A,13B,13C,13D). In particular, correlations between damage to different transcallosal cortical connections and types of neuropsychologic deficits are being evaluated. The severity of spastic diplegia is also being correlated with the degree of injury to traversing corticospinal tracts.
| White Matter Zone | ADCi | FA |
|---|---|---|
| NAWM | 0.66 (0.12) | 0.52 (0.16) |
| Area A | 0.84 (0.16) | 0.31 (0.12) |
| Area B | 1.36 (0.19) | 0.22 (0.07) |
| Area C | 1.73 (0.15) | 0.13 (0.05) |
| Note.—Data are averages and SDs, in units of 10-3 mm2/sec. NAWM = normal-appearing white matter. | ||
In children with holoprosencephaly, anomalous white matter tracts are identified in addition to the well-described noncleavage of cortical and deep gray matter structures. Rigorous comparison with age-matched normative white matter tract maps is still required for better understanding of these complex anomalies. Defining white and gray matter anomalies may allow more precise prediction of developmental outcome in holoprosencephaly (Fig. 14A,14B,14C,14D).
Other applications of diffusion tensor MR imaging and white matter tractography with more immediate clinical impact are the evaluation of brain tumors and acute stroke. Defining the relationship of brain tumors to eloquent white matter tracts will undoubtedly help guide the surgical approach and the extent of the resection (Fig. 15A,15B,15C,15D,15E). The true extent of infiltration of neoplasms along white matter tracts may be better delineated with these techniques. With respect to stroke, preliminary results have shown an immediate increase in fractional anisotropy in regions of reversible acute ischemia (decreased diffusion), which may be related to reduction in flow. Further investigation of this finding and its use in guiding acute stroke management is warranted.
The Future
White matter tractography based on diffusion tensor imaging is a rapidly evolving technology in central nervous system imaging, with many challenges and exciting new applications. Improvements in signal-to-noise ratios continue to be required for more precise calculation of anisotropy measures and more accurate white matter fiber tracking. Imaging with isotropic, high-resolution voxels reduces the effect of volume averaging on the direction of the principal eigenvector, which provides a better representation of the actual orientation of the fiber tract within a voxel and the accuracy of the tracking algorithm [21]. Pulse sequence innovations are needed, such as an optimized diffusion-sensitive three-dimensional echoplanar technique that allows isotropic high-resolution imaging with an improved signal-to-noise ratio compared with two-dimensional acquisitions. Another critical issue that limits the routine clinical use of this technique is the relatively long acquisition time resulting from the many averages or excitations required to enhance image signal-to-noise ratio and built-in acquisition redundancies needed to replace motion-corrupted images. To improve the efficiency of the acquisition, a scheme is needed that is based on “real-time” navigator echo correction. The scheme consists of continuously identifying and reacquiring only the corrupted portions of each acquisition. This scheme improves the efficiency of scanning by eliminating the need for a manual check of each acquisition before deciding whether more scans are needed and by decreasing the degree of redundancy in the acquisition.
Fiber tracking algorithms, such as diffusion spectrum techniques that may resolve fiber orientation heterogeneity in a voxel, are needed to accurately define crossing and dispersing white matter tracts [81]. Algorithms that take into consideration the medium and minor eigenvectors, in addition to the principal eigenvector, may better resolve intravoxel fiber orientation. Objective methods for comparing white matter tracts in patients with normative templates, for quantifying the dimensions of various white matter tracts, and for correlating these dimensions with cognitive function, are also required for validation of this technique.
Finally, an exciting application of this technique is in the clarification of the temporal relationships between loci of signal change in functional MR imaging experiments. Understanding the temporal sequence of regional activations is an important part of understanding how, rather than where, a cognitive process is executed. Because of the substantial disparity between the relatively slow evolution of functional MR imaging signal changes and the speed of the underlying neural processes, functional MR imaging may not be able to differentiate between secondary and higher order sites in the activation cascade. Mapping of white matter tracts may help divide apparently secondary activation sites into true secondary and higher order sites on the basis of the nature of their connections to the primary site of activation.
Fractional anisotropy: |  |
| Relative anisotropy: |  |
| Volume ratio: |  |
| λ1, λ2, and λ3 are the eigenvalues of the principal, medium, and minor eigenvectors, respectively. | |
| D̄ is the trace (λ1 + λ2 + λ3) of the diffusion tensor. | |
Footnote
Address correspondence to E. R. Melhem.
References
1.
Carr HY, Purcell EM. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Phys Rev 1954; 94:630-638
2.
Paulson OB, Hertz MM, Bolwig TG, Lassen NA. Filtration and diffusion of water across the blood-brain barrier in man. Microvasc Res 1977; 13:113-124
3.
Taylor DG, Bushell MC. The spatial mapping of translational diffusion coefficients by the NMR imaging techniques. Phys Med Biol 1985; 30:345-349
4.
Moseley ME, Kucharczyk J, Mintorovitch J, et al. Diffusion-weighted MR imaging of acute stroke: correlation with T2-weighted and magnetic susceptibility-enhanced MR imaging in cats. AJNR 1990; 11:423-429
5.
Mintorovitch J, Moseley ME, Chileuitt L, Shimizu H, Cohen Y, Weinstein PR. Comparison of diffusion- and T2-weighted MRI for the early detection of cerebral ischemia and reperfusion in rats. Magn Reson Med 1991; 18:39-50
6.
Minematsu K, Li L, Fisher M, Sotak CH, Davis MA, Fiandaca MS. Diffusion-weighted magnetic resonance imaging: rapid and quantitative detection of focal brain ischemia. Neurology 1992; 42:235-240
7.
van Gelderen P, de Vleeschouwer MHM, des Pres D, Pekar J, van Zijl PCM, Moonen CTW. Water diffusion and acute stroke. Magn Reson Med 1994; 31:154-163
8.
Sorensen AG, Buonanno FS, Gonzalez RG, et al. Hyperacute stroke: evaluation with combined multisection diffusion-weighted and hemodynamically weighted echo-planar MR imaging. Radiology 1996; 199:391-401
9.
Chenevert TL, Brunberg JA, Pipe JG. Anisotropic diffusion in human white matter: demonstration with MR techniques in vivo. Radiology 1990; 177:401-405
10.
Moseley ME, Cohen Y, Kucharczyk J, et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 1990; 176:439-445
11.
Doran M, Hajnal JV, Van Bruggen N, King MD, Youn IR, Bydder GM. Normal and abnormal white matter tracts shown by MR imaging using directional diffusion weighted sequences. J Comput Assist Tomogr 1990; 14:865-873
12.
Douek P, Turner R, Pekar J, Patronas N, Le Bihan D. MR color mapping of myelin fiber orientation. J Comput Assist Tomogr 1991; 15:923-929
13.
Sakuma H, Nomura Y, Takeda K, et al. Adult and neonatal human brain: diffusional anisotropy and myelination with diffusion-weighted MR imaging. Radiology 1991; 180:229-233
14.
Coremans J, Luypaert R, Verhelle F, Stadnik T, Osteaux M. A method for myelin fiber orientation mapping using diffusion-weighted MR images. Magn Reson Imaging 1994; 12:443-454
15.
Nomura Y, Sakuma H, Takeda K, Tagami T, Okuda Y, Nakagawa T. Diffusional anisotropy of the human brain assessed with diffusion-weighted MR: relation with normal brain development and aging. AJNR 1994; 15:231-238
16.
Ono J, Harada K, Mano T, Sakurai K, Okada S. Differentiation of dys- and demyelination using diffusional anisotropy. Pediatr Neurol 1997; 16:63-66
17.
Neil JJ, Shiran SI, McKinstry RC, et al. Normal brain in human newborns: apparent diffusion and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology 1998; 209:57-66
18.
Shimony JS, McKinstry RC, Akbudak E, et al. Quantitative diffusion-tensor anisotropy brain MR imaging: normative human data and anatomic analysis. Radiology 1999; 212:770-784
19.
Mori S, Crain BJ, Chacko VP, van Zijl PCM. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999; 45:265-269
20.
Bammer R, Augustin M, Strasser-Fuchs S, et al. Magnetic resonance diffusion tensor imaging for characterizing diffuse and focal white matter abnormalities in multiple sclerosis. Magn Reson Med 2000; 44:583-591
21.
Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med 2000; 44:625-632
22.
Le Bihan D, Breton E, Lallemand D, Aubin MI, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988; 168:497-505
23.
Turner R, Le Bihan D, Maier J, Vavrek R, Hedges LK, Pekar J. Echo-planar imaging of intravoxel incoherent motion. Radiology 1990; 177:407-414
24.
Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965; 42:288-292
25.
Pierpaoli C, Jezzard P, Basser PJ, Barnett A, DiChiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996; 201:637-648
26.
Sorensen AG, Wu O, Copen WA, et al. Human acute cerebral ischemia: detection of changes in water diffusion anisotropy by using MR imaging. Radiology 1999; 212:785-792
27.
Papadakis NG, Martin KM, Pickard JD, Hall LD, Carpenter Ta, Huang CL. Gradient pre-emphasis calibration in diffusion-weighted echo-planar imaging. Magn Reson Med 2000; 44:616-624
28.
Basser PJ, Pajevic S. Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise. Magn Reson Med 2000; 44:41-50
29.
Skare S, Li TQ, Nordell B, Ingvar M. Noise considerations in the determination of diffusion tensor anisotropy. Magn Reson Imaging 2000; 18:659-669
30.
Parker GJM, Schnabel JA, Symms MR, Werring DJ, Barker GJ. Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. J Magn Reson Imaging 2000; 11:702-710
31.
Melhem ER, Itoh R, Jones L, Barker PB. Diffusion tensor MR imaging of the brain: effect of diffusion weighting on trace and anisotropy measurements. AJNR 2000; 21:1813-1820
32.
Burdette JH, Elster AD, Ricci PE. Calculation of apparent diffusion coefficients (ADCs) in brain using two-point and six-point methods. J Comput Assist Tomogr 1998; 22:792-794
33.
Jones DK, Horsfield MA, Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 1999; 42:515-525
34.
Papadakis NG, Xing D, Huang CLH, Hall LD, Carpenter TA. A comparative study of acquisition schemes for diffusion tensor imaging using MRI. J Magn Reson 1999; 137:67-82
35.
Hasan KM, Parker DL, Alexander AL. Comparison of gradient encoding schemes for diffusion-tensor MRI. J Magn Reson Imaging 2001; 13:769-780
36.
Brockstedt S, Thomsen C, Wirestam R, Holtas S, Stahlberg F. Quantitative diffusion coefficient maps using fast spin-echo MRI. Magn Reson Imaging 1998; 16:877-886
37.
Mori S, van Zijl PC. A motion correction scheme by twin-echo navigation for diffusion-weighted magnetic resonance imaging with multiple RF echo acquisition. Magn Reson Med 1998; 40:511-516
38.
Anderson AW, Gore JC. Analysis and correction of motion artifacts in diffusion weighted imaging. Magn Reson Med 1994; 32:379-387
39.
Ordidge RJ, Helpern JA, Qing ZX, Knight RA, Nagesh V. Correction of motional artifacts in diffusion-weighted MR images using navigator echoes. Magn Reson Imaging 1994; 12:455-460
40.
Butts K, de Crespigny A, Pauly JM, Moseley M. Diffusion-weighted interleaved echo-planar imaging with a pair of orthogonal navigator echoes. Magn Reson Med 1996; 35:763-770
41.
Jezzard P, Barnett AS, Pierpaoli C. Characterization of and correction for eddy current artifacts in echo planar diffusion imaging. Magn Reson Med 1998; 39:801-812
42.
Finsterbusch J, Frahm J. Diffusion-weighted single-shot line scan imaging of the human brain. Magn Reson Med 1999; 42:772-778
43.
Alsop DC. Phase insensitive preparation of single-shot RARE: application to diffusion imaging in humans. Magn Reson Med 1997; 38:527-533
44.
Itoh R, Melhem ER, Folkers PJ. Diffusion-tensor MR imaging of the human brain with gradient- and spin-echo readout: technical note. AJNR 2000; 21:1591-1595
45.
Finsterbusch J, Frahm J. Diffusion tensor mapping of the human brain using single-shot line scan imaging. J Magn Reson Imaging 2000; 12:388-394
46.
Haselgrove JC, Moore JR. Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient. Magn Reson Med 1996; 36:960-964
47.
Bastin ME. Correction of eddy current-induced artifacts in diffusion tensor imaging using iterative cross-correlation. Magn Reson Imaging 1999; 17:1011-1024
48.
Woods RP, Cherry SR, Mazziotta JC. Rapid automated algorithm for aligning and reslicing PET images. J Comput Assist Tomogr 1992; 16:620-633
49.
Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996; 36:893-906
50.
Bastin ME, Armitage PA, Marshall I. A theoretical study of the effect of experimental noise on the measurement of anisotropy in diffusion imaging. Magn Reson Imaging 1998; 16:773-785
51.
Conturo TE, Lori NF, Cull TS, et al. Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A 1999; 96:10422-10427
52.
Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med 1999; 42:1123-1127
53.
Poupon C, Clark CA, Frouin V, et al. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. Neuroimage 2000; 12:184-195
54.
Poupon C, Mangin J, Clark CA, et al. Towards inference of human brain connectivity from MR diffusion tensor data. Med Image Anal 2001; 5:1-15
55.
Cleveland GG, Chang DC, Hazelwood CF. Nuclear magnetic resonance measurements of skeletal muscle: anisotropy of the diffusion coefficient of the intracellular water. Biophys J 1976; 16:104-105
56.
Lutsep HL, Albers GW, De Crespigny A, Kamat GN, Marks MP, Moseley ME. Clinical utility of diffusion-weighted magnetic resonance imaging in the assessment of ischemic stroke. Ann Neurol 1997; 41:574-580
57.
Hsu EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS. Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am J Physiol 1998; 274:H1627-H1634
58.
Reese TG, Weisskoff RM, Smith RN, Rosen BR, Dinsmore RE, Wedeen VJ. Imaging myocardial fiber architecture in vivo with magnetic resonance. Magn Reson Med 1995; 4:786-791
59.
Hsu EW, Setton LA. Diffusion tensor microscopy of the intervertebral disc anulus fibrosus. Magn Reson Med 1999; 41:992-999
60.
Peled S, Gudbjartsson H, Westin CF, Kikinis R, Jolesz FA. Magnetic resonance imaging showing orientation and asymmetry of white matter fiber tracts. Brain Res 1998; 780:27-33
61.
Moseley ME, Butts K, Yenari MA, Marks M, de Crespigny A. Clinical aspects of DWI. NMR Biomed 1995; 8:87-96
62.
Warach S, Gaa J, Siewert B, Weilpolski P, Edelman RR. Acute human stroke studied by whole brain echo planar diffusion weighted magnetic resonance imaging. Ann Neurol 1995; 7:231-241
63.
Huppi PS, Maier SE, Peled S, et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr Res 1998; 44:584-590
64.
Guo AC, Petrella JR, Kurtzberg J, Provenzale JM. Evaluation of white matter anisotropy in Krabbe disease with diffusion tensor MR imaging: initial experience. Radiology 2001; 218:809-815
65.
Ito R, Melhem ER, Mori S, Eicher FS, Raymond GV, Moser HW. Diffusion tensor brain MR imaging in X-linked cerebral adrenoleukodystrophy. Neurology 2001; 56:544-547
66.
Pomara N, Crandall DT, Choi SJ, Johnson G, Lim KO. White matter abnormalities in HIV-1 infection: diffusion tensor imaging study. Psychiatry Res 2001; 106:15-24
67.
Ciccarelli O, Werring DJ, Wheeler-Kingshott CA, et al. Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations. Neurology 2001; 56:926-933
68.
Filippi M, Cercignani M, Inglese M, Horsfield MA, Comi G. Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 2001; 56:304-311
69.
Schwartz RB, Mulkern RV, Gudbjartsson H, Jolesz F. Diffusion-weighted MR imaging in hypertensive encephalopathy: clues to pathogenesis. AJNR 1998; 19:859-862
70.
Sullivan EV, Adalsteinsson E, Hedehus M, et al. Equivalent disruption of regional white matter microstructure in aging healthy men and women. Neuroreport 2001; 12:99-104
71.
Foong J, Maier M, Clark CA, Barker GJ, Miller DH, Ron MA. Neuropathological abnormalities of the corpus callosum in schizophrenia: a diffusion tensor imaging study. J Neurol Neurosurg Psychiatry 2000; 68:242-244
72.
Rose SE, Chen F, Chalk JB, et al. Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with color-coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry 2000; 69:528-530
73.
Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SC, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke 1999; 30:393-397
74.
Eriksson SH, Rugg-Gunn FJ, Symms MR, Barker GJ, Duncan JS. Diffusion tensor imaging in patients with epilepsy and malformations of cortical development. Brain 2001; 124:627-636
75.
Krabbe K, Gideon P, Wagn P, et al. MR diffusion imaging of human intracranial tumors. Neuroradiology 1997; 39:483-489
76.
Chabriat H, Vahedi K, Clark CA, et al. Decreased hemispheric water mobility in hemiplegic migraine related to mutation of CACNA1A gene. Neurology 2000; 54:510-512
77.
Schaefer PW, Buonanno FS, Gonzalez RG, Schwamm LH. Diffusion-weighted imaging discriminates between cytotoxic and vasogenic edema in a patient with eclampsia. Stroke 1997; 28:1082-1085
78.
Melhem ER, Barker PB, Raymond GV, Moser HW. X-linked adrenoleukodystrophy: review of genetic, clinical, and MR imaging characteristics. AJR 1999; 173:1575-1581
79.
Melhem ER, Hoon AH, Ferrucci JT III, et al. Brain MR imaging in periventricular leukomalacia: relationship between lateral ventricular volume and severity of cognitive and motor impairment. Radiology 2000; 214:199-204
80.
Fitz CR. Holoprosencephaly and related entities. Neuroradiology 1983; 25:225-238
81.
Wiegell MR, Larsson HBW, Wedeen VJ. Fiber crossing in human brain depicted with diffusion tensor MR imaging. Radiology 2000; 217:897-903
82.
Stieltjes B, Kaufmann WE, van Zijl PC, et al. Diffusion tensor imaging and axonal tracking in the human brainstem. Neuroimage 2001; 14:723-735
Information & Authors
Information
Published In
Copyright
© American Roentgen Ray Society.
History
Submitted: December 28, 2000
Accepted: July 5, 2001
Authors
Metrics & Citations
Metrics
Citations
Export Citations
To download the citation to this article, select your reference manager software.