(Radiology. 2000;217:897-903.)
© RSNA, 2000
Fiber Crossing in Human Brain Depicted with Diffusion Tensor MR Imaging1
Mette R. Wiegell, MSc,
Henrik B. W. Larsson, MD, PhD, Dr med and
Van J. Wedeen, MD
1 From the Department of Radiology, Nuclear Magnetic Resonance Center, Massachusetts General Hospital-East, Bldg 149, 13th St, Charlestown, MA 02129 (M.R.W., V.J.W.); and the Danish Research Center for Magnetic Resonance, Hvidovre Hospital, Denmark (M.R.W., H.B.W.L.). Received June 21, 1999; revision requested August 10; final revision received March 13, 2000; accepted March 20. Supported in part by the Sol Goldman Charitable Trust and National Institutes of Health grant 5RO1H256727. M.R.W supported by Apoteker fonden af 1991 (Copenhagen, Denmark). Address correspondence to V.J.W. (e-mail: van@nmr.mgh.harvard.edu).
 |
ABSTRACT
|
|---|
Human white matter fiber crossings were investigated with use of the full eigenstructure of the magnetic resonance diffusion tensor. Intravoxel fiber dispersions were characterized by the plane spanned by the major and medium eigenvectors and depicted with three-dimensional graphics. This method improves the analysis of fiber orientations, beyond the principal fiber directions, to a broader range of complex fiber architectures.
Index terms: Brain, MR, 10.121411, 10.121416, 10.12146 • Brain, white matter, 10.87 • Magnetic resonance (MR), diffusion study, 10.121411, 10.121416, 10.12146, 10.87 • Magnetic resonance (MR), image display, 10.121411, 10.121416, 10.12146, 10.87 • Magnetic resonance (MR), tissue characterization, 10.121411, 10.121416, 10.12146, 10.87
|
INTRODUCTION
|
|---|
White matter of the brain consists of projections to subcortical nuclei and association pathways between cortical gray matter structures. The integrity of these white matter connections is compromised in various degenerative and demyelinating pathologic conditions, including Wallerian degeneration, multiple sclerosis, and amyotrophic lateral sclerosis. In addition, tumors, head trauma, and edema often cause white matter displacements by mass effect. Hence, imaging of white matter structures would help identification of fiber tracts and assessment of their viability.
Conventional imaging modalities currently available in the clinic cannot reveal the structure of white matter anatomy. Recently, however, depiction of complex white matter architecture in vivo has been made possible with diffusion tensor magnetic resonance (MR) imaging (1–4). This technique is based on the observed anisotropy of water self-diffusion in white matter, which is thought to arise from the organization of diffusion or relaxation boundaries (eg, macromolecules and cell membranes) (5,6). The anisotropic diffusion is described mathematically by means of a rank-two tensor, which is a generalization of the scalar diffusion coefficient for structured media (Fig 1).
In the past (1,7–14), tensor data were typically described solely by means of various anisotropic measures and the largest diffusion eigenvalue. Studies of the diffusion tensor orientation (1,6,8,9,11,14–16) have focused primarily on the direction of the major eigenvector, an approach that we refer to as the "uniaxial model." This model maintains that the leading eigenvector coincides with the mean orientation of the white matter fibers. The model fails to account, however, for cases of intersection and dispersion of fibers in a voxel.
Such complex intravoxel fiber orientation has been discussed briefly in other studies (1,14,17–19) but has been neither depicted nor described in terms of the underlying fiber anatomy. The purpose of this study was to provide an extended interpretation of the full eigenstructure of the diffusion tensor with use of what we call the "planar model." The planar model addresses angular dispersion of fiber orientations in a voxel established on the basis of the difference between the medium and minor eigenvalues (Fig 2). The expanded model described the spread of fiber orientations in terms of planar architecture, which is seen in (a) the crossing of fiber populations and (b) fanning of a fiber population in a preferred plane. We used a three-dimensional (3D) graphic display method to emphasize this phenomenon.
|
Materials and Methods
|
|---|
Diffusion Tensor MR Imaging
Whole-brain diffusion tensor MR imaging was performed in five healthy subjects (three men and two women; age range, 21–40 years; mean age, 25 years), with a 1.5-T imager (Vision; Siemens Medical Systems, Erlangen, Germany) equipped with echo-planar imaging. Written consent was obtained from each subject, and the protocol was approved by the Danish Ethical Committee (Copenhagen, Denmark). For each subject, data were acquired for 36 contiguous sections of the brain oriented either transversely or coronally. We used a single-shot interleaved spin-echo diffusion-weighted echo-planar MR imaging sequence (repetition time, 4,000 msec; echo time, 101 msec; matrix, 128 x 128; field of view, 230 mm; section thickness, 3 mm). One null image and six diffusion-weighted images were obtained, with the diffusion-encoding gradients directed along the following logical axes: (±1, 1, 0), (±1, 0, 1), and (0, ±1, 1). Diffusion encoding with gradient strengths of 12 mT/m each, duration of 
= 28.9 msec, separation of 
= 51.9 msec, and diffusion time of 42.6 msec was applied. The b matrix was calculated analytically (20), for an approximate b factor of 550 sec/mm2; the error on the b matrix caused by the readout gradient was less than 0.3%. The b value was chosen to give a relatively short echo time, adequate diffusion weighting, and high signal-to-noise ratio for the limited gradient strength available (maximum of 12 mT/m per direction when two gradients were applied simultaneously). Thirty-six images were averaged with an acquisition time of 2 minutes 53 seconds covering six sections. The signal-to-noise ratio obtained in the nonaveraged diffusion-weighted image was approximately 15, giving a signal-to-noise ratio for the averaged diffusion-weighted images of approximately 90.
The images were realigned to compensate for eddy current–induced morphing in the phase and readout directions, although eddy current effects were observed in only the phase direction. Images with motion artifacts were excluded. Maps of constant magnetic induction field, or B0, inhomogeneity (21) showed insignificant shifts in the brain and no more than two-pixel shifts around bone structures and air-filled cavities. The diffusion tensor was calculated on a voxel-by-voxel basis (20), taking the effects of preparation and imaging gradients on the b matrix into account. The tensor was represented in each voxel by three eigenvectors (v1, v2, v3) and their corresponding eigenvalues (
1, 2, 3), which were sorted according to the usual convention 1 
2 3 0. The major eigenvector (v1), according to the planar model, gives the mean direction of fiber populations, often referred to as the "principal diffusion direction." The planar model interprets the medium eigenvector (v2) as angular dispersion of the major eigenvector and the minor eigenvector (v3) as the normal to the plane of the mean direction and its angular dispersion (Fig 1). Both noise effects and the systematic bias introduced by the eigenvalue sorting were negligible for the observed signal-to-noise ratio, as previously shown in Monte Carlo simulations (15).
Display Methods
Three-dimensional computer graphics were constructed (MATHEMATICA; Wolfram Research, Champaign, Ill) to represent the complete information contained in the diffusion tensor. The tensor in each voxel was represented by a right-angled octagonal cylinder. Although the cylinder is not the actual geometry of the isocontour of the displacement probability function, which is better represented by an ellipsoid, we found that the 3D orientation, as well as the dimensions (uniaxial or planar), was easier to depict by this means (Fig 2). The dimensions of the cylinders were with the eigenvalues and the size scaled by means of an anisotropy index. The rotational invariant fractional anisotropy (22) index and the difference between the eigenvalues (ij = i - j) were used to measure the anisotropy. The fractional anisotropy index gives a scalar metric of the eigenvalue variance normalized by their sum. For highly anisotropic (large variance) diffusion, the fractional anisotropy index is 1; for isotropic (no variance) diffusion, the fractional anisotropy index is 0. The difference measure was chosen because of its ability to discriminate uniaxial 1 
2 
3 (23 0) and planar 1 2 3 (23 0) eigenvalue systems. The direction of the cylinders was color coded with a red-green-blue color scheme (23–25) for the individual eigenvectors, which makes depiction of the directional course of the diffusion possible (Fig 3). In the figures, red corresponds to mediolateral, green to anteroposterior, and blue to superoinferior orientation. The color brightness was determined by means of the fractional anisotropy.
View larger version (67K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 3. Color sphere represents the colors related to the anatomic orientation of the brain. The color coding applies the three principal colors—red, green, and blue—to the elements of a vector, resulting in a color that represents its 3D direction.
|
|
Regions of Interest
Anatomic areas were identified in each subject in which the local fiber distribution was characterized as intersection or dispersion of fibers on the basis of anatomic atlases (26). Three-dimensional computer graphics were constructed to show the dispersion plane of the two leading eigenvectors, in addition to the uniaxial model, which showed the major eigenvector. Four regions of interest in planar architecture were selected by either of two authors (M.R.W., V.J.W.) for analysis with the 3D graphics. The following areas were chosen to show fiber crossing: the transverse view of the crossing between the corona radiata and corpus callosum (area 1), the coronal view of the same crossing (area 2), and the coronal view of the crossing between the pontocerebellar fibers and the corticospinal tracts (area 3). The areas selected to depict fanning of fibers included the transverse view of fanning of the arcuate fascicle (area 4) and the coronal view of fanning of the white matter in the middle and superior frontal gyri (area 5). The results are summarized in the
Table.
|
Results
|
|---|
Fiber anatomy is clearly seen in the uniaxial representations (12 0) (Figs 4, 5). However, the planar representations (23 0, right images) of the same sections show complementary anatomic information. The images of the uniaxial representation emphasize anatomic areas with dominantly unidirectional fiber structures, such as the corpus callosum, corona radiata, arcuate fascicle, corticospinal tracts, and some U fibers. In contrast, the images of the planar representation highlight complementary areas, including the corona radiata, the superior longitudinal fascicle, forceps major and forceps minor, the crossing between the corpus callosum and the corona radiata (areas 1 and 2, respectively), and the crossing between the pontine fibers and the corticospinal or corticobulbar tracts (area 3). In addition to areas with the intersection of two or more fiber bundles, the planar character of some of the uniaxial-dominant fiber bundles can be observed.
View larger version (72K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 5. Color-coded coronal images of mean diffusion direction (v1) masked by the 12 anisotropy (left) and the normal (v3) to the plane of the mean direction and its angular dispersion masked by the 23 anisotropy (right). Left: Uniaxial architecture is emphasized (corpus callosum [CC, red], corticospinal tracts [CS, blue], pontocerebellar fibers [Po, red], and cingulum [Ci, green]). Right: Planar architecture is emphasized by the intersection of the corpus callosum and corona radiata (area 2, green), the intersection of pontocerebellar fibers and corticospinal tracts (area 3, green), and fanning of the superior frontal gyrus (area 5, red).
|
|
Crossing of Corpus Callosum and Corona Radiata
Uniaxial model renderings show that the corpus callosum has a mediolateral orientation, as expected from the anatomy, and that the corona radiata has a superoinferior direction (Figs 6, 7). The area between the two structures can hardly be identified on the uniaxial rendering, whereas it appears clearly on the planar rendering, with the normal vector indicating the anteroposterior direction. The actual fiber plane shown by the end plane of the cylinders is the mediolateral-superoinferior plane. Neuroanatomic atlases indicate that this area does not contain one specific fiber bundle but several fiber populations. The fiber plane is defined by means of the superoinferior and left-to-right, or mediolateral, directions, where the superoinferior-directed fibers are accounted for by the corona radiata and the left-to-right–directed fibers by radiation of the corpus callosum.
View larger version (85K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 7. Three-dimensional renderings of area 2 show a coronal view of the crossing between the corona radiata and radiation of the corpus callosum, as viewed with the uniaxial model (left) and the planar model (right). The colors, orientations, and scaling of the cylinders were determined as in The mediolateral-superoinferior fiber plane (right), which can be explained in terms of left-to-right-directed collosal fibers and superoinferior-directed corticospinal tracts (left), determines the crossing area between the corpus callosum and corona radiata.
|
|
Pons
The uniaxial rendering shows the superoinferior orientation of the corticospinal tracts and the mediolateral direction of the pontocerebellar fibers (Fig 8). The area containing fibers from both corticospinal tracts and the pontine fibers is better characterized in the planar image, which shows that the tissue structure can be described by a plane. The normal in the anteroposterior direction in this area corresponds to a plane spanned by the uniaxial direction and its dispersion in the mediolateral-superoinferior plane, emphasized by the end plane of the cylinders. The fiber plane can be explained in terms of the crossing between the mediolateral pontocerebellar fibers and the superoinferior corticospinal tracts.
View larger version (66K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 8. Three-dimensional renderings of area 3 show a coronal view of the crossing between the pontocerebellar fibers and the corticospinal tracts, as viewed with the uniaxial model (left) and the planar model (right). The colors, orientations, and scaling of the cylinders were set as in The crossing area between the corticospinal tracts and the pontocerebellar fibers shows mediolateral-superoinferior planar architecture (right), which can be described in terms of the left-to-right-directed pontocerebellar fibers and the superoinferior-oriented corticospinal tracts (left).
|
|
Arcuate Fascicle
The arcuate fiber tract (superior longitudinal fascicle) is seen on the uniaxial rendering as an anteroposterior-directed bundle corresponding to its primary direction of association fibers from the temporal to the frontal lobe (Fig 9). On the planar rendering, angular dispersion of the arcuate fascicle in the anteroposterior and left-to-right directions can be observed. Fanning of the fascicle is due to the receiving and projecting of fibers from the gyri between the temporal and frontal lobes. The minor eigenvector has a superoinferior direction corresponding to fibers in the mediolateral-anteroposterior plane, as indicated by the orientations of the end plane of the cylinders. This fanning and intermixing of the fibers can be seen in the planar image, which reveals more unidirectional orientation at the bottom of the sulci but shows a dispersion of fibers in the areas of the gyri.
View larger version (89K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 9. Three-dimensional renderings of area 4 show a transverse view of fanning of the superior longitudinal fascicle, as viewed with the uniaxial model (left) and the planar model (right). The colors, orientations, and scaling of the cylinders were determined as in Right: Dispersion of the superior longitudinal fascicle can be appreciated as anteroposterior or mediolateral planar architecture, and curvature of the U fiber around the sulcus can be seen.
|
|
More subtle information can be found in the curvature of the U fibers around a sulcus. The U fiber is hard to follow on the uniaxial image, but the planar image depicts the curvature and fanning of the U fiber around the sulcus. Hence, the posterior limb of the U fiber has a medially projecting mediolateral-superoinferior plane that curves along the arcuate fascicle (anteroposterior) in a superoinferior or anteroposterior plane to end in the anterior limb of the U fiber once more with a mediolateral-superoinferior plane.
Frontal Lobes
The middle and superior frontal gyri show fiber orientation on the uniaxial rendering in the radial direction of the gyrus (Fig 10). On the planar rendering, however, the white matter fibers fan out in the longitudinal direction of the gyri, as expected on the basis of the neuroanatomy.
View larger version (82K):
[in this window]
[in a new window]
[Download PPT slide]
|
Figure 10. Three-dimensional renderings of area 5 show a coronal view of fanning of the middle and superior frontal gyri, as viewed with the uniaxial model (left) and the planar model (right). Left: The color coding and longitudinal orientation of the cylinders were determined with the first eigenvector, and scaling was determined with fractional anisotropy. Right: The planar model represents the color coding and longitudinal axis of the cylinder with the third eigenvector. The plane of maximal diffusion, spanned by the first and second eigenvectors can be appreciated as the end plane of the cylinder. The scaling was determined with fractional anisotropy. Radial dispersion of the frontal gyri is found to be in the superoinferior or anteroposterior direction, in accordance with the directions of the gyri.
|
|
|
Discussion
|
|---|
Three-dimensional depiction demonstrates that not only the unidirectional orientation (major eigenvector) but also angular dispersion (medium eigenvector) help describe features of the anatomic structure. The plane spanned by these two eigenvectors, whose normal is characterized by the minor eigenvector, revealed abundant white matter regions containing fiber heterogeneity. (Note that the importance of such planar architecture for muscle function has already been addressed in diffusion tensor studies of cardiac sheet architecture [27] and bovine tongue [28].) Last, the ability to resolve fiber orientation heterogeneity with the planar model should benefit anatomic tracking of fiber pathways through regions of crossing and dispersion.
Neither the uniaxial nor the planar model of the diffusion tensor can resolve the component fiber orientations in a heterogeneous voxel, in which case extended sampling schemes of the diffusion function are necessary (29).The planar model can indicate the presence of fiber heterogeneity, however, but the uniaxial model cannot owing to its one-dimensional structure. Given the presence of fiber heterogeneity, the orientation of the component fibers can be indirectly inferred from the surrounding fiber anatomy. Hence, in areas with known anatomy, the plane of intersection of two fiber populations could be accounted for by their individual uniaxial orientations. Likewise, fanning could be explained in terms of the radial dispersion of the tract in areas with fiber dispersion.
Knowledge about fiber complexity in a voxel is important not only when fiber orientations are evaluated but also when diffusion coefficients and anisotropy measures are reported (30). Measurements of diffusion values and anisotropy indexes depend greatly on the fiber composition; therefore, careful positioning of regions of interest in healthy subjects and patients is necessary to obtain reproducible results. The planar method provided herein offers a simple interpretation of the intravoxel variance in fiber geometry in terms of the individual fiber components.
In pathologic cases (18,19,31), this new information may improve the ability to determine the effect of the disease or lesion on the specific fiber bundles. Increased knowledge about disease development may be available that previously could be obtained, if at all, with only invasive methods.
Thus, the planar approach facilitates evaluation of a considerably larger amount of anatomic structures and their directional courses in the human brain than was previously possible. It also improves the applicability of diffusion tensor MR imaging, beyond the principal fiber directions, to a broader range of complex fiber architectures.
|
ACKNOWLEDGMENTS
|
|---|
The authors thank David S. Tuch, BA, for many helpful discussions.
|
FOOTNOTES
|
|---|
Abbreviation: 3D = three-dimensional
Author contributions: Guarantors of integrity of entire study, M.R.W., V.J.W.; study concepts, M.R.W., V.J.W., H.B.W.L.; study design, M.R.W.; definition of intellectual content, M.R.W., V.J.W.; literature research, M.R.W.; clinical studies, M.R.W., H.B.W.L.; experimental studies, M.R.W., H.B.W.L.; data acquisition, M.R.W.; data analysis, M.R.W., V.J.W.; manuscript preparation, editing, and review, M.R.W., V.J.W.
|
REFERENCES
|
|---|
-
Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996; 201:637-648.[Abstract/Free Full Text]
-
Hajnal JV, Doran M, Hall AS, et al. MR imaging of anisotropically restricted diffusion of water in the nervous system: technical, anatomic, and pathologic considerations. J Comput Assist Tomogr 1991; 15:1-18.[Medline]
-
Rutherford MA, Cowan FM, Manzur AY, et al. MR imaging of anisotropically restricted diffusion in the brain of neonates and infants. J Comput Assist Tomogr 1991; 15:188-198.[Medline]
-
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.[Abstract/Free Full Text]
-
Beaulieu C, Allen PS. Diffusional anisotropy in nerve cords without myelination (abstr) In: Book of abstracts: Society of Magnetic Resonance in Medicine 1992. Berkeley, Calif: Society of Magnetic Resonance in Medicine, 1992; 1728.
-
van Doorn A, Bovendeerd PHM, Nicolay K, Drost MR, Janssen JD. Determination of muscle fibre orientation using diffusion-weighted MRI. Eur J Morphol 1995; 34:5-10.
-
Neil JJ, Shiran SI, McKinstry RC, et al. Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology 1998; 209:57-66.[Abstract/Free Full Text]
-
Hüppi 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.[Medline]
-
Peled S, Gudbjartsson H, Westin CF, Kikinis R, Jolesz FA. Magnetic resonance imaging shows orientation and asymmetry of white matter fiber tracts. Brain Res 1998; 780:27-33.[Medline]
-
Werring DJ, Clark CA, Barker GJ, et al. The structural properties of multiple sclerosis (MS) lesions demonstrated by diffusion tensor imaging (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1998; 119.
-
Makris N. Diffusion weighted MRI-based atlas of the human brainstem in vivo (abstr). Proceedings of the Fifth Annual Meeting of the Organization for Human Brain Mapping : , 1999; 232.
-
Erikson SH, Symms MR, Barker GJ, Weismann UC, Woermann FG, Duncan JS. Investigation of white matter tracts in malformation of cortical development using MR diffusion tensor imaging and statistical parametric mapping (abstr). Proceedings of the Fifth Annual Meeting of the Organization for Human Brain Mapping : , 1999; 562.
-
Werring DH, Clark CA, Barker GJ, Thompson AJ, Miller DH. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 1999; 52:1626-1632.[Abstract/Free Full Text]
-
Werring DJ, Clark CA, Barker GJ, et al. The structural and functional mechanisms of motor recovery: complementary use of diffusion tensor and functional magnetic resonance imaging in a traumatic injury of the internal capsule. J Neurol Neurosurg Psychiatry 1998; 65:863-869.[Abstract/Free Full Text]
-
Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996; 36:893-906.[Medline]
-
Igarashi H, Katayama Y, Tsuganezawa T, Yamamuro M, Terashi A, Owan C. Three-dimensional anisotropy contrast (3DAC) magnetic resonance imaging of the human brain: application to assess Wallerian degeneration. Int Med 1998; 37:662-668.
-
Lim KO, Hedehus M, Moseley M, de Crespigny A, Sullivan EV, Pfefferbaum A. Compromised white matter tract integrity in schizophrenia inferred from diffusion tensor imaging. Arch Gen Psychiatry 1999; 56:367-374.[Abstract/Free Full Text]
-
Wiegell MR, Reese TG, Larsson HBW, Tievsky A, Paulson OB, Wedeen VJ. Diffusion tensor MRI of CNS and white matter pathology: importance of secondary structure (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1998; 1306.
-
Wiegell MR, Langkilde AR, Larsson HBW. Diffusion tensor imaging of multiple sclerosis plaques (abstr). Proceedings of the Seventh Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1999; 959.
-
Mattiello J, Basser PJ, Le Bihan D. The b matrix in diffusion tensor echo-planar imaging. Magn Reson Med 1997; 37:292-300.[Medline]
-
Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 inhomogeneities (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1995; 104.
-
Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Res Ser B 1996; 111:209-211.[Medline]
-
Jones D, Williams S, Horsfield M. Full representation of white-matter fibre direction on one map via diffusion tensor analysis (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1998; 1743.
-
Pierpaoli C. Oh, no! one more method for colormapping of fiber tract direction using diffusion MR imaging data (abstr). Proceedings of the Fifth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1997; 1741.
-
Pajevic S, Pierpaoli C. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med 1999; 42:526-540.[Medline]
-
Nieuwenhuys R, Voogd J, van Huijzen C. The human central nervous system: a synopsis and atlas 3rd ed. New York, NY: Springer-Verlag, 1988.
-
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; 34:768-791.
-
Napadow V, Chen Q, Wedeen V, Gilbert R. Characterization of lingual mechanics during swallowing by strain mapping with tagging MRI (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1998; 1127.
-
Tuch DS, Weisskoff RM, Belliveau JW, Wedeen VJ. High angular resolution diffusion imaging of the human brain (abstr). Proceedings of the Seventh Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1999; 321.
-
Virta A, Barnett A, Pierpaoli C. Visualizing and characterizing white matter fiber structure and architecture in the human pyramidal tract using diffusion tensor MRI. Magn Reson Imaging 1999; 17:1121-1133.[Medline]
-
Wiegell MR, Krabbe K, Schmiegelow M, et al. Fiber structure changes in long term survivors of radiotherapy elucidated by diffusion tensor imaging (abstr). Proceedings of the Sixth Meeting of the International Society for Magnetic Resonance in Medicine Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 1998; 40.
This article has been cited by other articles:
|
|
|
|
 
C.-H. Toh, M. Castillo, A.M.-C. Wong, K.-C. Wei, H.-F. Wong, S.-H. Ng, and Y.-L. Wan
Primary Cerebral Lymphoma and Glioblastoma Multiforme: Differences in Diffusion Characteristics Evaluated with Diffusion Tensor Imaging
AJNR Am. J. Neuroradiol.,
March 1, 2008;
29(3):
471 - 475.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. Wahl, B. Lauterbach-Soon, E. Hattingen, P. Jung, O. Singer, S. Volz, J. C. Klein, H. Steinmetz, and U. Ziemann
Human Motor Corpus Callosum: Topography, Somatotopy, and Link between Microstructure and Function
J. Neurosci.,
November 7, 2007;
27(45):
12132 - 12138.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
N. Mikuni, T. Okada, R. Enatsu, Y. Miki, S.-i. Urayama, J. A Takahashi, K. Nozaki, H. Fukuyama, and N. Hashimoto
Clinical significance of preoperative fibre-tracking to preserve the affected pyramidal tracts during resection of brain tumours in patients with preoperative motor weakness
J. Neurol. Neurosurg. Psychiatry,
July 1, 2007;
78(7):
716 - 721.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A Anwander, M Tittgemeyer, D. von Cramon, A. Friederici, and T. Knosche
Connectivity-Based Parcellation of Broca's Area
Cereb Cortex,
April 1, 2007;
17(4):
816 - 825.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Yamamoto, Y. Miki, S. Urayama, Y. Fushimi, T. Okada, T. Hanakawa, H. Fukuyama, and K. Togashi
Diffusion Tensor Fiber Tractography of the Optic Radiation: Analysis with 6-, 12-, 40-, and 81-Directional Motion-Probing Gradients, a Preliminary Study
AJNR Am. J. Neuroradiol.,
January 1, 2007;
28(1):
92 - 96.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Stadlbauer, O. Ganslandt, R. Buslei, T. Hammen, S. Gruber, E. Moser, M. Buchfelder, E. Salomonowitz, and C. Nimsky
Gliomas: Histopathologic Evaluation of Changes in Directionality and Magnitude of Water Diffusion at Diffusion-Tensor MR Imaging
Radiology,
September 1, 2006;
240(3):
803 - 810.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. J. Gilbert, L. H. Magnusson, V. J. Napadow, T. Benner, R. Wang, and V. J. Wedeen
Mapping Complex Myoarchitecture in the Bovine Tongue with Diffusion-Spectrum Magnetic Resonance Imaging
Biophys. J.,
August 1, 2006;
91(3):
1014 - 1022.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. Okada, Y. Miki, Y. Fushimi, T. Hanakawa, M. Kanagaki, A. Yamamoto, S.-i. Urayama, H. Fukuyama, M. Hiraoka, and K. Togashi
Diffusion-Tensor Fiber Tractography: Intraindividual Comparison of 3.0-T and 1.5-T MR Imaging
Radiology,
February 1, 2006;
238(2):
668 - 678.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. S. Tuch, D. H. Salat, J. J. Wisco, A. K. Zaleta, N. D. Hevelone, and H. D. Rosas
Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention
PNAS,
August 23, 2005;
102(34):
12212 - 12217.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. Konishi, K. Yamada, O. Kizu, H. Ito, K. Sugimura, K. Yoshikawa, M. Nakagawa, and T. Nishimura
MR tractography for the evaluation of functional recovery from lenticulostriate infarcts
Neurology,
January 11, 2005;
64(1):
108 - 113.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Nimsky, O. Ganslandt, P. Hastreiter, R. Wang, T. Benner, A. G. Sorensen, and R. Fahlbusch
Intraoperative Diffusion-Tensor MR Imaging: Shifting of White Matter Tracts during Neurosurgical Procedures--Initial Experience
Radiology,
January 1, 2005;
234(1):
218 - 225.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. J. Jellison, A. S. Field, J. Medow, M. Lazar, M. S. Salamat, and A. L. Alexander
Diffusion Tensor Imaging of Cerebral White Matter: A Pictorial Review of Physics, Fiber Tract Anatomy, and Tumor Imaging Patterns
AJNR Am. J. Neuroradiol.,
March 1, 2004;
25(3):
356 - 369.
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. Yamada, O. Kizu, S. Mori, H. Ito, H. Nakamura, S. Yuen, T. Kubota, O. Tanaka, W. Akada, H. Sasajima, et al.
Brain Fiber Tracking with Clinically Feasible Diffusion-Tensor MR Imaging: Initial Experience
Radiology,
April 1, 2003;
227(1):
295 - 301.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Mukherjee, J. H. Miller, J. S. Shimony, J. V. Philip, D. Nehra, A. Z. Snyder, T. E. Conturo, J. J. Neil, and R. C. McKinstry
Diffusion-Tensor MR Imaging of Gray and White Matter Development during Normal Human Brain Maturation
AJNR Am. J. Neuroradiol.,
October 1, 2002;
23(9):
1445 - 1456.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. J. Lowe, M. D. Phillips, J. T. Lurito, D. Mattson, M. Dzemidzic, and V. P. Mathews
Multiple Sclerosis: Low-Frequency Temporal Blood Oxygen Level-Dependent Fluctuations Indicate Reduced Functional Connectivity—Initial Results
Radiology,
July 1, 2002;
224(1):
184 - 192.
[Abstract]
[Full Text]
|
|
|
|
|
|
|
S. Albayram, E. R. Melhem, S. Mori, S. J. Zinreich, A. J. Barkovich, and S. L. Kinsman
Holoprosencephaly in Children: Diffusion Tensor MR Imaging of White Matter Tracts of the Brainstem—Initial Experience
Radiology,
June 1, 2002;
223(3):
645 - 651.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. H. Taber, C. Pierpaoli, S. E. Rose, F. J. Rugg-Gunn, J. B. Chalk, D. K. Jones, and R. A. Hurley
The Future for Diffusion Tensor Imaging in Neuropsychiatry
J Neuropsychiatry Clin Neurosci,
February 1, 2002;
14(1):
1 - 5.
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. R. Melhem, S. Mori, G. Mukundan, M. A. Kraut, M. G. Pomper, and P. C. M. van Zijl
Diffusion Tensor MR Imaging of the Brain and White Matter Tractography
Am. J. Roentgenol.,
January 1, 2002;
178(1):
3 - 16.
[Full Text]
[PDF]
|
|
|
TOP
ABSTRACT
INTRODUCTION
