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Diffusion-Tensor MR Imaging and Tractography: Exploring Brain Microstructure and Connectivity¹

¹ From the Department of Radiology, Sections of Neuroradiology (P.G.P.N., E.R.M.) and Biomedical Image Analysis (R.V.), Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104; and Department of Diagnostic Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea (S.K.L.). Received March 14, 2006; revision requested May 18; revision received November 10; accepted December 18; final version accepted February 12, 2007; final review by E.R.M. May 17. Address correspondence to E.R.M. (e-mail: emelhem{at}rad.upenn.edu).

	ABSTRACT

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 
Diffusion magnetic resonance (MR) imaging is evolving into a potent tool in the examination of the central nervous system. Although it is often used for the detection of acute ischemia, evaluation of directionality in a diffusion measurement can be useful in white matter, which demonstrates strong diffusion anisotropy. Techniques such as diffusion-tensor imaging offer a glimpse into brain microstructure at a scale that is not easily accessible with other modalities, in some cases improving the detection and characterization of white matter abnormalities. Diffusion MR tractography offers an overall view of brain anatomy, including the degree of connectivity between different regions of the brain. However, optimal utilization of the wide range of data provided with directional diffusion MR measurements requires careful attention to acquisition and postprocessing. This article will review the principles of diffusion contrast and anisotropy, as well as clinical applications in psychiatric, developmental, neurodegenerative, neoplastic, demyelinating, and other types of disease.

© RSNA, 2007

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 
Described by Le Bihan et al (1) in 1986, diffusion-weighted (DW) imaging has rapidly been adopted into the radiologic armamentarium. The most common application is the early detection of ischemia, as manifested by restricted diffusion in a territorial distribution (2). Clinically, DW imaging is nearly ideal for this purpose: It is highly sensitive, highly specific, and noninvasive and provides a diagnosis within the therapeutic window (3). However, the scope of diffusion imaging extends beyond the detection of acute ischemia. By incorporating directionality into a DW measurement, diffusion-tensor (DT) images can be obtained. Rather than probe cellular pathophysiology, DT imaging provides a means of investigating tissue microstructure and brain anatomy. This article will review the principles of diffusion contrast and anisotropy, as well as clinical applications in psychiatric, developmental, neurodegenerative, neoplastic, demyelinating, and other types of disease.

	PRINCIPLES

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 
Diffusion contrast is based on the self-diffusion of water molecules in tissue (1,4). Although a variety of sequences are now used to acquire DW images, all DW sequences include two equal and opposing motion-probing gradients. As a result of being subjected to opposing gradients, the signal from a voxel will decrease exponentially as a function of the strength of the gradients (G), the duration of the gradients (), the amount of time passing between the gradients (), and the diffusion coefficient of water molecules in the voxel (D). The first three factors do not vary from voxel to voxel and can be quantified collectively with a b value, defined as ²G²²( – /3), where is the gyromagnetic constant. Only intravoxel diffusion (D) varies from voxel to voxel, and thus contrast in a DW image is a function of the apparent diffusion coefficient. The relative contributions of T2 contrast and diffusion contrast in the DW signal (S) are governed by the Stejskal-Tanner equation, S = S₀ x e^–bD, where S₀ is the signal measured without motion-probing gradients that is used to form the image with a b value of 0 sec/mm² (hereafter, b0 image).

DW imaging is only sensitive for motion of water molecules that is aligned with the motion-probing gradients. In gray matter, changing the direction of the gradient does not substantially affect the signal since diffusion is roughly isotropic (equally likely in all directions). On the other hand, diffusion in white matter is often strongly anisotropic and occurs maximally in the same orientation as white matter tracts (Fig 1) (5). Anisotropic diffusion is an effect of the microstructural properties of the voxel, and decreased anisotropy is a common feature of neuronal abnormalities. The relationship between tissue microstructure and anisotropy is probably multifactorial: Evidence is mounting for the hypothesis that the integrity of the myelin sheath and axonal membrane is reflected by restriction of diffusion orthogonal to the fiber, whereas the integrity of intra-axonal structures (such as microtubules) is positively correlated with diffusion parallel to the fiber (6–12). However, anisotropy may also be decreased from nonspecific abnormalities such as vasogenic edema.

View larger version (174K): [in this window] [in a new window] [Download PPT slide]   Figure 1: Multiple transverse DW images (b value, 800 sec/mm2; repetition time msec/echo time msec, 6500/99; matrix, 128 x 128; field of view [FOV], 220 x 220 mm; section thickness, 3 mm) of same brain slice with diffusion gradients applied in different directions demonstrate anisotropic diffusion. Signal intensity of white matter varies with direction of diffusion gradients, most conspicuously in corpus callosum (arrows). Signal is strongest when diffusion gradient is oriented orthogonally to white matter fiber tracts, as in two panels on right.

In the tensor model, it is assumed that a water molecule undergoing diffusion for a limited time will generally be constrained to a volume known as the diffusion ellipsoid. This volume is spherical in voxels with isotropic diffusion, where water diffusion is completely symmetric. In contrast, voxels with anisotropic diffusion have oblate (flattened) or prolate (elongated) diffusion ellipsoids, depending on the relative magnitudes of the eigenvalues. Although it is possible to produce an image that represents the diffusion ellipsoid in every voxel, interpreting such an image is cumbersome. For this reason, properties of the tensor are often abstracted by various indices to produce gray-scale or color maps. For example, the trace is a simple index of diffusion determined by the sum of the principal diffusivities. Similarly, fractional anisotropy (FA) is an index ranging from 0 (isotropic) to 1 (maximally anisotropic) and is defined as by using the following equation:

for eigenvalues ₁, ₂, and ₃ and mean diffusivity . Anisotropy maps are often color encoded to represent directional information regarding the principal eigenvector (Fig 2). Taken together, a combination of these maps provides a useful compromise between ease of interpretation and clinical utility. Even at low resolution, they can be used to segment white matter tracts by visual inspection, thus allowing a radiologist to evaluate "white-white" contrast.

View larger version (31K): [in this window] [in a new window] [Download PPT slide]   Figure 2: Common types of coronal DT images (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm) in a healthy subject. A, Mean diffusivity maps, B, fractional anisotropy maps, and C, color-encoded maps of the principal eigenvector (red, left to right; blue, cranial to caudal; green, anterior to posterior).

	ACQUISITION

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

The signal from tissue that undergoes gross motion may be misinterpreted as increased diffusivity, therefore scan times are generally kept short by using large voxel sizes and fast acquisition techniques such as echo-planar imaging (20). However, DT imaging with spin-echo echo-planar imaging and (less commonly used) gradient-echo echo-planar imaging is associated with several forms of artifacts. Some, such as eddy current distortion, are amenable to correction during postprocessing and can further be reduced with a modified acquisition (21–27). Likewise, artifacts induced with cardiac pulsation can be decreased with cardiac gating (28). Cerebrospinal fluid, which has a very high apparent diffusion coefficient, can contaminate measurements in nearby voxels; this can be addressed by decreasing voxel size or using fluid-attenuated inversion-recovery DW imaging (29–33).

On the other hand, susceptibility artifact at air-bone interfaces in the skull is more difficult to correct. Single-shot stimulated-echo acquisition mode, or STEAM, MR imaging has shown promise in reducing susceptibility artifact (34). Several multi-shot diffusion protocols have also been used to reduce susceptibility artifact without introducing phase errors from subject motion (35). For example, line-scan imaging is relatively insensitive to motion, and although used mainly in the spine, it has also been applied to DT imaging of the brain (36,37). Alternately, navigator information can be acquired during a multi-shot sequence to account for motion during reconstruction. One such approach is periodically rotated overlapping parallel lines with enhanced reconstruction, or PROPELLER, DT imaging, which acquires data in a rotating blade of k-space (38–40). Similarly, self-navigated interleaved spiral, or SNAILS, DT imaging acquires data through a spiral in k-space (41). In both cases, the oversampled center of k-space provides the navigator data. Finally, parallel imaging has proved useful in reducing susceptibility artifact in both single-shot and multi-shot acquisitions (42–44).

Although the tensor model can represent most white matter regions, it does not adequately describe voxels with crossing, diverging, or converging white matter tracts (45–47). These fiber tracts theoretically could be resolved with improved voxel resolution, perhaps by using high-field-strength imaging (48). Presently, however, multiple directions of diffusion within a single voxel are modeled with higher order vectors (49–51). Higher order methods generally involve examining q-space, which contains the Fourier transform of diffusion properties just as k-space in conventional MR imaging contains the Fourier transform of magnetic properties (52–54). DT imaging is based on a very limited sampling of q-space, and the resulting ellipsoids rely on several assumptions regarding the properties of diffusion in a voxel (49,55). High angular resolution diffusion imaging, or HARDI, methods, while more time consuming than DT imaging, use increased sampling in q-space to produce an improved diffusion profile (49,56–58). Diffusion spectrum imaging, for example, uses data throughout all of q-space to reconstruct a complete diffusion profile (50,59). In contrast, q-ball imaging samples points that are arranged on the surface of a sphere in q-space, reducing acquisition times by focusing on the diffusion parameters that are most relevant to tractography. Q-space techniques have proved useful in resolving multiple white matter orientations in regions such as the centrum semiovale, where DT imaging generally performs poorly (60–62). However, their steeper technical requirements have limited their clinical use.

Once fiber orientations have been determined for a sufficient number of voxels, tractography can be used to draw inferences regarding the overall geometry of white matter in the brain. A wide variety of algorithms are used for this purpose (Fig 3). Generally, streamline (deterministic) tractography connects neighboring voxels by propagating the ends of fiber tracts from user-defined seed voxels until termination criteria are met, such as excessive angular deviation of the fiber tracts or subthreshold voxel anisotropy (46,63–68). Although the seed voxels define the origin of all fiber tracts under examination, additional regions may be designated to restrict the output to a tract of interest. For instance, tractography of the corticospinal tract may include fiber tracts if and only if they pass through both the internal capsule and the cerebral peduncles (Fig 3a). Tract selection and seed placement are typically highly interactive, which can result in strong operator dependence.

View larger version (12K): [in this window] [in a new window] [Download PPT slide]   Figure 3a: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (14K): [in this window] [in a new window] [Download PPT slide]   Figure 3b: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (21K): [in this window] [in a new window] [Download PPT slide]   Figure 3c: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (141K): [in this window] [in a new window] [Download PPT slide]   Figure 3d: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (82K): [in this window] [in a new window] [Download PPT slide]   Figure 3e: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (117K): [in this window] [in a new window] [Download PPT slide]   Figure 3f: (a) Abstract representation of tensors in a 5 x 5 grid, with two regions of interest (ROIs) (red and green). Strongly anisotropic voxels are dark blue, whereas weakly anisotropic voxels are light blue. (b) Streamline tractography propagates a fiber tract in the direction of principal eigenvector, preserving voxel-to-voxel directional information. (c) Probabilistic tractography produces a likelihood map of the diffusion path between two ROIs. Rather then delineate a single best path, the likelihood map shows the probability that a particle diffusing between ROIs traverses each voxel. Coronal images of internal capsule in healthy adults demonstrate (d) diffusion ellipsoid maps (principal eigenvector is denoted by color: red, left to right; blue, cranial to caudal; green, anterior to posterior), (e) streamline tractography of corticospinal tract (red), and (f) probabilistic tractography of corticospinal tract (color denotes index of probability that voxel is included in tract) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (82K): [in this window] [in a new window] [Download PPT slide]   Figure 4a: (a) Tractography of corticospinal tract performed by iteratively extending streamlines (green) from a seed ROI in the direction of principal eigenvector by using the fiber assignment by continuous tracking, or FACT, algorithm (63,64). Images are superimposed on sagittal fractional anisotropy map. (b) Volume-rendered probability map of corticospinal tract produced by generating several thousand tracts from each seed voxel (69). Rather than follow the same path, since each track is extended it may overlap or depart from the rest at random. The probability of deviating from principal eigenvector in a traversed voxel reflects its diffusion properties, with increased divergence in voxels of low anisotropy. Probability map is formed from superposition of all tracts from all voxels in the seed ROI tract (color denotes index of probability that voxel is included in tract) (DT imaging: 12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (73K): [in this window] [in a new window] [Download PPT slide]   Figure 4b: (a) Tractography of corticospinal tract performed by iteratively extending streamlines (green) from a seed ROI in the direction of principal eigenvector by using the fiber assignment by continuous tracking, or FACT, algorithm (63,64). Images are superimposed on sagittal fractional anisotropy map. (b) Volume-rendered probability map of corticospinal tract produced by generating several thousand tracts from each seed voxel (69). Rather than follow the same path, since each track is extended it may overlap or depart from the rest at random. The probability of deviating from principal eigenvector in a traversed voxel reflects its diffusion properties, with increased divergence in voxels of low anisotropy. Probability map is formed from superposition of all tracts from all voxels in the seed ROI tract (color denotes index of probability that voxel is included in tract) (DT imaging: 12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

	ANALYSIS

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 
DT imaging produces numerous measures ranging in dimension from scalars to tensor fields, which calls for a wide variety of statistical techniques to perform group analyses. Specific methods for the statistical analysis of full tensors remain under development (78,79). Currently, scalar DT imaging measures (including but not limited to anisotropy, diffusivity, and probability maps) are most commonly compared by using histogram, ROI, or voxel-based analysis. To establish confidence intervals in these measures, bootstrap methods repeatedly sample data from multiple acquisitions (80–83).

Histogram analysis does not require any presuppositions regarding anatomy or pathologic features, making it suitable for widespread diseases such as multiple sclerosis or small vessel ischemic disease (74,84–86). The brain is considered globally, and the frequencies of particular DT imaging values in different individuals are evaluated. Consequently, only global conclusions can be drawn regarding the composition of white and/or gray matter, which may be a disadvantage when considering lesions in the brain, as their effects are often dependent on location.

ROI analysis is used to test hypotheses regarding specific regions where disease is suspected. If motor symptoms are present, for example, DT imaging measures may be calculated only in voxels believed to contain the internal capsule. Any significant differences that are detected can be ascribed to the ROI, thus offering a possible correlation between structure and function. Potential pitfalls include bias in ROI selection, which can partially be addressed by automation (87–90). In addition, ROIs drawn on DT images may suffer from artifact and decreased resolution, whereas those drawn on higher resolution images must be accurately registered to the DT images. Intersubject registration can be used to reduce error by standardizing ROIs on every subject. However, registration of full tensor datasets must be performed carefully; in registered tensor maps, unlike scalar maps, corresponding voxels do not have the same values since the operations used to map voxels during registration should change the orientation of the tensors. For instance, a shear deformation should cause tensors to realign in the direction of the shear. Thus, conventional registration should be followed by proper reorientation of tensors (91–93).

Finally, DT imaging measures can be compared on a voxel-by-voxel basis to localize differences between groups without a priori assumptions regarding the location of pathologic features (although voxels near each other are often assumed to be correlated to mitigate the problem of multiple comparisons) (94). Voxel-based analysis is usually less operator dependent and more easily automated than ROI analysis, but it can only be performed after intersubject registration (91–93). Statistical packages originally developed for blood oxygen level–dependent (BOLD) functional MR imaging have been adopted for DT imaging measures, including scalar maps of anisotropy and diffusivity (95). Nevertheless, it is important to be aware of the limitations of these methods when interpreting results, particularly when statistically significant voxels or clusters are detected that do not have a reasonable anatomic correlate (96,97).

Tractography is still in its infancy, and no consensus has emerged regarding the best means of analyzing its output. It is often used to segment white matter into specific tracts; the corpus callosum, for example, has been segmented according to the cortical destination of its fiber tracts, with correlation of callosal lesions to clinical presentation (98,99). One of the more promising methods of analysis is to examine a DT imaging measure such as anisotropy along the course of a selected fiber tract, which can either be performed during tractography or by defining a specific reference frame afterward (100–102). Finally, probabilistic tractography has been used to generate a connectivity matrix describing the relationships between every pair of voxels; by examining patterns within the matrix, clusters of white matter with homogeneous connectivity can be determined (103). It is important to bear in mind that tractography findings can be affected by any process that alters diffusion anisotropy, including those external to the axon such as vasogenic edema. Thus, interpretation of tractography requires an appreciation of the distinction between anatomy and physiology.

	APPLICATIONS

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 
Normal Brain
All of these types of analyses have been used to demonstrate the relationship between white matter structure and function. For example, IQ has been positively correlated with anisotropy in white matter association tracts (104). Reading ability has been correlated with anisotropy of the left temporoparietal white matter, where tractography has localized the white matter circuitry connecting Broca and Wernicke language areas (Fig 5) (105–110). In visual pathways, increased anisotropy has been correlated with improved reaction time, and tractography can be used to demonstrate topographically ordered fiber tracts in normal subjects (intriguingly, these fiber tracts are disorganized in the blind) (111–114).

View larger version (64K): [in this window] [in a new window] [Download PPT slide]   Figure 5: Tractography (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm) of a healthy right-handed subject demonstrates frontotemporal fiber tracts corresponding to arcuate fasciculus (red), superimposed on a sagittal b 0 image. This white matter tract connects motor and receptive speech centers and has greater relative fiber density in the left hemisphere.

Developmental Abnormalities
In premature newborns, increased anisotropy is found in developing cortical gray matter rather than in unmyelinated white matter, and cortical anisotropy steadily decreases during the first few months of life (99,117–125). This likely reflects the radial anisotropy of the glial scaffolding that guides the migration of neurons to the cortex (126,127). Unsurprisingly, a spectrum of migrational abnormalities and other developmental brain disorders has been demonstrated with DT imaging. For example, DT imaging has identified cortical dysplasia with greater sensitivity than did conventional MR imaging (Fig 6) (128). In patients with band heterotopias, tractography has been used to suggest potential connectivity between regions of heterotopic gray matter and normal cortex (Fig 7) (129). In lissencephaly, tractography of the grossly abnormal subcortical and deep white matter has demonstrated incomplete development of the fornix and cingulate tracts (130). Tractography in patients with periventricular leukomalacia has supported the hypothesis that spastic paralysis may involve extrapyramidal and sensory pathways (Fig 8) (131,132). DT imaging in patients with alobar holoprosencephaly has demonstrated absent corticospinal tracts (Fig 9) (133). Many white matter tract structures, such as the middle cerebellar peduncles, were found to be smaller in alobar holoprosencephaly than in semilobar holoprosencephaly or lobar holoprosencephaly (Fig 10). Furthermore, the size of the corticospinal tracts and middle cerebellar peduncles in all three variants was correlated with neurodevelopmental status.

View larger version (108K): [in this window] [in a new window] [Download PPT slide]   Figure 6: Images in 17-year-old boy with left-side motor seizure. A, Equivocal finding (arrow) on transverse T2-weighted image(4000/100; matrix, 352 x 352; FOV, 230 x 230 mm; section thickness, 5 mm). B, Transverse fast inversion recovery with myelin suppression image (repetition time msec/echo time msec/inversion time msec, 4500/22/300; section thickness, 3 mm; matrix, 256 x 256; FOV, 230 x 230 mm) shows focal cortical dysplasia in right precentral gyrus (arrow). C, Transverse fluorine 18 fluorodeoxyglucose positron emission tomography reveals decreased metabolism in right precentral area. D, Whole-brain white matter tractography (blue-green) depicts decreased subcortical fiber connectivity in right precentral area and adjacent cortex (DT imaging: 32 directions; six-channel sensitivity encoding; sensitivity encoding factor of two; b value, 600 sec/mm2; 6599–8280/70; matrix, 96 x 96; FOV, 220 x 220 mm; section thickness, 2.3 mm).

View larger version (73K): [in this window] [in a new window] [Download PPT slide]   Figure 7: Images in 18-year-old woman with band heterotopia. Transverse T2-weighted (left) (4000/100; matrix, 352 x 352; FOV, 230 x 230 mm; section thickness, 5 mm) and T1-weighted (middle) (450/12; matrix, 256 x 256; FOV, 230 x 230 mm; section thickness, 5 mm) images demonstrate thick band heterotopia (arrow) with homogeneous signal intensity to gray matter, a so-called double cortex. This band shows high anisotropy (arrow) on fractional anisotropy map (right), suggesting its radial orientation and arrested neuronal migration (32 directions; six-channel sensitivity encoding; sensitivity encoding factor of two; b value, 600 sec/mm2; 6599–8280/70; matrix, 96 x 96; FOV, 220 x 220 mm; section thickness, 2.3 mm). It is also consistent with passage of intact white matter tracts through the abnormality.

View larger version (63K): [in this window] [in a new window] [Download PPT slide]   Figure 8: Tractography in patient with periventricular leukomalacia demonstrates degeneration of posterior thalamic radiation (PTR), with intact fiber tracts (arrows) in the posterior limb of internal capsule (ICPL) (six directions; b value, 600 sec/mm2; 5000–6000/92; matrix, 64 x 51; FOV, 120 x 96 mm; section thickness, 3 mm). lv = lateral ventricle, tv = third ventricle. (Reprinted, with permission, from reference 131.)

View larger version (79K): [in this window] [in a new window] [Download PPT slide]   Figure 9: A, Transverse T2-weighted image (3000/20, 80) demonstrates alobar holoprosencephaly. B, Transverse color-encoded DT image depicts absence of corticospinal tracts (red, left to right; blue, cranial to caudal; green, anterior to posterior). C, Transverse color-encoded DT image depicts normal corticospinal tracts in healthy subject (DT imaging: six directions; b value, 600 sec/mm2; 5000–6000/92; matrix, 64 x 64; FOV, 120 x 120 mm; section thickness, 3 mm). (Reprinted, with permission, from reference 133.)

View larger version (48K): [in this window] [in a new window] [Download PPT slide]   Figure 10: Transverse color-encoded DT images (red, left to right; blue, cranial to caudal; green, anterior to posterior) of middle cerebellar peduncle (arrows) in patients with alobar (right), semilobar (middle), and lobar (left) holoprosencephaly demonstrate manifestation of increasing severity of holoprosencephaly (six directions; b value, 600 sec/mm2; 5000–6000/92; matrix, 64 x 64; FOV, 120 x 120 mm; section thickness, 3 mm). (Reprinted, with permission, from reference 133.)

View larger version (62K): [in this window] [in a new window] [Download PPT slide]   Figure 11a: Tractography (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm) in (a) a patient with amyotrophic lateral sclerosis and (b) a healthy subject. Tractography is superimposed on sagittal b0 image. In the right(R) hemisphere, descending fiber tracts connecting the cortex and brainstem are shown in purple and the corticospinal tract is shown in green. The ratio of the number of fiber tracts in corticospinal tract to the total number fiber tracts is decreased in amyotrophic lateral sclerosis. (Reprinted, with permission, from reference 145.)

View larger version (67K): [in this window] [in a new window] [Download PPT slide]   Figure 11b: Tractography (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm) in (a) a patient with amyotrophic lateral sclerosis and (b) a healthy subject. Tractography is superimposed on sagittal b0 image. In the right(R) hemisphere, descending fiber tracts connecting the cortex and brainstem are shown in purple and the corticospinal tract is shown in green. The ratio of the number of fiber tracts in corticospinal tract to the total number fiber tracts is decreased in amyotrophic lateral sclerosis. (Reprinted, with permission, from reference 145.)

Demyelinating Disease
The specificity of DT imaging measures for white matter abnormalities has spurred its use in demyelinating diseases, particularly multiple sclerosis. Several groups have demonstrated increased diffusivity and decreased anisotropy in demyelinating lesions (Fig 12) (175–179). In some studies, diffusivity and anisotropy varied with the degree and type of contrast enhancement (177,179,180). However, DT imaging has also demonstrated abnormalities in normal-appearing white matter and normal-appearing gray matter; it is unclear whether the latter represents an inflammatory lesion in gray matter or the effects of retrograde axonal degeneration (181–183). Patients with primary or secondary progressive multiple sclerosis demonstrated increased gray matter diffusivity, compared with patients with relapsing-remitting multiple sclerosis or healthy controls (184). In patients with clinically isolated syndromes (considered a precursor to multiple sclerosis), tractography has defined the corticospinal tracts and demonstrated higher lesion volume within them than in other white matter, although the correlation between disease progression and DT findings is less concrete (185).

View larger version (113K): [in this window] [in a new window] [Download PPT slide]   Figure 12: A, Transverse b0 image demonstrates focus of signal abnormality (arrow) in the right corona radiata in 38-year-old woman with multiple sclerosis. B, Transverse fractional anisotropy map of lesion (arrow) demonstrates decreased anisotropy (DT imaging: 12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (62K): [in this window] [in a new window] [Download PPT slide]   Figure 13: Images in 55-year-old man who complained of sudden onset of left-sided weakness. Transverse, A, fluid-attenuated inversion-recovery (9000/90/2000; matrix, 288 x 288; FOV, 230 x 230 mm; section thickness, 5 mm) and, B, DW (six directions; six-channel sensitivity encoding; sensitivity encoding factor of two; b value, 1000 sec/mm2; 3300/60; matrix, 128 x 128; FOV, 230 x 230 mm; section thickness, 5 mm) images show acute infarction in right basal ganglia. C, Fiber tractography with superimposed transverse b 0 image (32 directions, six-channel sensitivity encoding; sensitivity encoding factor of two; b value, 600 sec/mm2; 6599–8280/70; matrix, 96 x 96; FOV, 220 x 220 mm; section thickness, 2.3 mm) clearly shows the relationship between infarction and corticospinal tract. The infarct is anterior to the course of corticospinal tract (arrows). The patient experienced complete recovery of motor function.

As neurosurgeons increasingly consider the degree to which tumors may displace and disrupt white matter tracts (Fig 14), the anatomic guidance provided by tractography is emerging as an important part of preoperative planning (203–205). This is particularly true of eloquent white matter such as the corticospinal tract, where tractography has been a useful adjunct to intraoperative fiber stimulation (206–208). Preoperative tractography showing tumor involvement of the corticospinal tract has been correlated to motor deficits, even when motor cortex was uninvolved (209). Conversely, normalization at postoperative tractography was predictive of improvement in function, suggesting a role for intraoperative tractography (210,211). For inoperable tumors, tractography may be helpful in gamma knife planning, and it has already been used in the radiosurgical treatment of arteriovascular malformations (212).

View larger version (86K): [in this window] [in a new window] [Download PPT slide]   Figure 14: Transverse (A–C, E) and coronal (D, F) color-encoded DT images (red, left to right; blue, cranial to caudal; green, anterior to posterior) demonstrate displacement (A–C), infiltration (D–E), and destruction (F) of white matter tracts (arrow) by tumor (*). G, Tractography with superimposed sagittal b 0 image demonstrates displacement of white matter tracts (red) by tumor (green) (12 directions; b value, 800 sec/mm2; 6500/99; matrix, 128 x 128; FOV, 220 x 220 mm; section thickness, 3 mm).

View larger version (32K): [in this window] [in a new window] [Download PPT slide]   Figure 15: Images in 7-year-old boy involved in a vehicular accident 3 years before and suffering from intractable seizure. Electroencephalography demonstrated persistent discharge of epileptic wave in the right hemisphere. Functional right hemispherectomy was planned for treatment of the intractable seizure. However, patient demonstrated right-side hemiplegia instead of left-side dysfunction. A, Transverse T2-weighted (4000/100; matrix, 352 x 352; FOV, 230 x 230 mm; section thickness, 5 mm) images show cerebromalacia of right hemisphere and atrophy. Precentral gyrus is relatively spared. B, Fractional anisotropy maps show lack of high signal intensity of longitudinal pontine fiber tracts in the left-side cerebral peduncle, suggesting axonal injury (arrows). C, Fiber tractography with superimposed transverse b0 image shows cutting off of left corticospinal tract at the level of left cerebral peduncle due to axonal injury (arrows). Right corticospinal tract is intact although atrophy and cerebromalacia are demonstrated in the remainder of the brain. Callosotomy was performed instead of functional hemispherectomy on the basis of DT and fiber tractography findings, because right hemispherectomy would have resulted in quadriplegia (DT imaging: 32 directions; six-channel sensitivity encoding; sensitivity encoding factor of two; b value, 600 sec/mm2; 6599–8280/70; matrix, 96 x 96; FOV, 220 x 220 mm; section thickness, 2.3 mm).

	CONCLUSION

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

The full potential of DT imaging will probably not be realized until it is integrated with other modalities to obtain a richer characterization of white matter, in a manner analogous to the combination of perfusion and diffusion imaging data used to demonstrate an ischemic penumbra. Perhaps the most intriguing application is the integration of tractography with functional imaging. Activation maps are the natural complement of tractography; a temporal relationship between activated foci implies the existence of subservient fiber tracts, whereas anatomic connectivity between two regions of the brain suggests a functional relationship. The excellent correlation of BOLD functional MR data with tractography findings in motor and visual cortex may illustrate the future of structure-function investigations in the brain, ultimately to culminate in a comprehensive description of the "human connectome" (219).

	ESSENTIALS

TOP ABSTRACT INTRODUCTION PRINCIPLES ACQUISITION ANALYSIS APPLICATIONS CONCLUSION ESSENTIALS References

 

• Diffusion-tensor (DT) imaging is capable of demonstrating abnormalities in a variety of disorders, ranging from multiple sclerosis to neurodegenerative disease, potentially with higher sensitivity and specificity than conventional imaging.
  
• The emerging field of clinical tractography is poised to make substantial contributions to preoperative planning in neuro-oncology and epilepsy.
  
• Those who are armed with a sound understanding of the principles of DT imaging and tractography will likely find DT imaging to be a useful tool in investigating the relationships between structure and function in the brain.
  

	FOOTNOTES

 

Abbreviations: BOLD = blood oxygen level–dependent • DT = diffusion tensor • DW = diffusion weighted • FOV = field of view • ROI = region of interest

Authors stated no financial relationship to disclose.

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