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00004728-200407000-0001600004728_2004_28_533_abe_tractography_4miscellaneous-article< 81_0_13_7 >Journal of Computer Assisted Tomography© 2004 Lippincott Williams & Wilkins, Inc.Volume 28(4)July/August 2004pp 533-539Topography of the Human Corpus Callosum Using Diffusion Tensor Tractography[Neuroimaging]Abe, Osamu MD, DMSC*; Masutani, Yoshitaka PHD*; Aoki, Shigeki MD, DMSC*; Yamasue, Hidenori MD†; Yamada, Haruyasu MD*; Kasai, Kiyoto MD†; Mori, Harushi MD*; Hayashi, Naoto MD, DMSC*; Masumoto, Tomohiko MD*; Ohtomo, Kuni MD, DMSC*From the *Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan, and †Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.Supported in part by a Grant-in-Aid for Scientific Research (C) (2) 15591259 of the Ministry of Education, Science, Sports, and Culture of Japan.Reprints: Dr Osamu Abe, Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan (e-mail: abediag-tky@umin.ac.jp).AbstractObjective: To evaluate the crossing fiber trajectory through the corpus callosum using distortion-corrected diffusion tensor tractography in the human brain.Methods: After correcting distortion associated with large-diffusion gradients, T2-weighted echo planar images (EPIs) acquired from 10 right-handed healthy men were coregistered into T2-weighted fast spin echo images using linear through sixth-order nonlinear, 3-dimensional, polynomial warping functions. The optimal transformation parameters were also applied to the distortion-corrected diffusion-weighted EPIs. Diffusion tensor tractography through the corpus callosum was reconstructed, employing the “1 or 2 regions of interest” method.Results: Compared with the lines through the genu, those through the rostrum ran more inferiorly and seemed to enter the orbital gyrus. Those lines entering posterior temporal white matter (tapetum) crossed through the ventral portion of the splenium and were clearly distinguished from lines that reached parieto-occipital white matter (forceps major).Conclusion: Diffusion tensor tractography is a feasible noninvasive tool to evaluate commissural fiber trajectory.Diffusion tensor imaging is a newer noninvasive magnetic resonance imaging (MRI) method that provides a means of characterizing the diffusivity of water molecules in the human brain and more pertinent insights into tissue structure, orientation, and temperature not accessible with conventional MRI sequences.1,2 Diffusion tensor imaging has found application in a wide range of clinical situations such as acute cerebral ischemia, multiple sclerosis, brain maturation, and normal aging.3–6 It is difficult to visualize the 3-dimensional information of the diffusion tensor into a 2-dimensional imaging plane, however. The means to express diffusion tensor data sets are mean diffusivity, an anisotropy index such as fractional anisotropy (FA),7 a directionally encoded color map,8 a vector map, and diffusion tensor tractography (DTT).9–13 Diffusion tensor tractography reconstructs the 3-dimensional trajectories of white matter tracts, generally by following a continuous path of the greatest diffusivity through the brain from an initial set of seed points. It enables elicitation of white matter connectivity in vivo and is a promising tool to noninvasively elucidate normal neural fiber integrities and those changes in various disease processes.Diffusion tensor tractography, however, has a few weak points. The first problem is a false-positive line tracking. Water diffuses preferentially along the axis of the fiber bundles, and DTT traces lines depending on the principal axis of the eigenvector associated with the largest eigenvalue of the diffusion tensor. Therefore, DTT does reconstruct lines between the neural fibers, which exist closely and have similar principal axes even when those are not actually interconnected. The second problem is a false-negative line tracking. Because voxel size observed with MRI is much larger than neural fibers, 2 or more white matter fiber bundles with different principal axes intersect within a voxel. As a result, the estimated axis of the first eigenvector is different from the principal axis of each fiber, resulting in early termination or deviation of tracking. Third, there is no gold standard of verifying the accuracy of drawn lines obtained with DTT in individual subjects.Despite these problems, highly reproducible results in the corpus callosum consistent with those of previous postmortem investigations15–18 have been shown in the several DTT studies.10,11,13,14 There is no report to investigate the subdivided topography of the corpus callosum using DTT, however. The purpose of this study is to determine the topographic organization of fibers coursing through the human corpus callosum using DTT in comparison with neuroanatomic knowledge.MATERIALS AND METHODSSubjectsDiffusion tensor imaging data were acquired from the cranium of 10 right-handed healthy men (24–36 years old, average age: 29.1 ± 4.3 years) without brain morphologic abnormalities, neurologic illness, head trauma, loss of consciousness, or psychiatric disorders. Written informed consent was obtained from all subjects according to the Declaration of Helsinki, and the scanning protocol was approved by the local ethical committee at our institution.MRI Acquisition ProtocolAll scans were acquired on a 1.5-T Signa Echo Speed MRI system (General Electric Medical Systems, Milwaukee, WI). A circularly polarized head coil was used for radiofrequency transmission and reception of the nuclear magnetic resonance signal. The pulse sequence we used in this study was single-shot, diffusion-weighted, echo planar acquisition (repetition time/echo time = 6000/78.2 milliseconds; numbers of excitation (NEX) = 4; acquisition time = 5 minutes, 36 seconds; matrix = 128 × 128; field of view = 240 mm × 240 mm; slice thickness = 5 mm; no gap; b value = 1000 s/mm2; diffusion gradient directions = [0, 0, 0], [1/√2, 0, 1/√2], [−1/√2, 0, 1/√2], [0, 1/√2, 1/√2], [0, 1/√2, −1/√2], [1/√2, 1/√2, 0], and [−1/√2, 1/√2, 0]). We also acquired proton density–weighted and T2-weighted fast spin echo (FSE) images in the same session (repetition time/echo time = 3000/28, 84 milliseconds; NEX = 1; acquisition time = 9 minutes, 54 seconds; matrix = 256 × 256; field of view = 240 mm × 240 mm; slice thickness = 3 mm; no gap; echo train length = 8).Image Processing and Distortion CorrectionDuring the acquisition of the single-shot echo planar images (EPIs), structural distortion is a serious issue. This acquisition scheme is highly sensitive to susceptibility gradients between brain parenchyma and the surrounding tissues (eg, air, calvarium) as well as to eddy currents induced by large diffusion gradients, resulting in significant spatial distortions. To overcome these problems, we used 2 kinds of image postprocessing techniques. First, we applied distortion correction to diffusion-weighted raw images (b ≠ 0 s/mm2) based on the T2-weighted EPI (b = 0 s/mm2)19,20 on the workstation supplied by the manufacturer (Advantage Workstation 4.0; General Electric Medical Systems). This correction algorithm relies on the maximization of mutual information to estimate the 3 parameters of a geometric distortion model inferred from the acquisition principle. In brief, a residual gradient in the frequency-encoding direction X induces a shear parallel to the phase-encoding direction Y. A residual gradient in Y produces a uniform scaling in the Y direction. A residual gradient in the slice-encoding direction Z produces uniform translation along Y.The signal intensities of the diffusion-weighted images were then fitted using multivariate linear least-square fitting to obtain the 6 elements of the diffusion tensor on a voxel-by-voxel basis.1,7 The diffusion tensor was diagonalized to obtain eigenvalues (λ1>λ2>λ3) and eigenvectors for each voxel. We used the FA to investigate the warping function that best mapped the EPIs onto the corresponding T2-weighted FSE images, because the FA is a robust intravoxel measure.3,21 Based on the eigenvalues, the FA was calculated on the voxel-by-voxel basis as follows:Equation (Uncited)Second, distortion-corrected diffusion-weighted EPIs and T2-weighted FSE images as well as FA maps were transferred to a standard personal computer workstation (Dimension 8300, Pentium 4/3GHz; Dell). T2-weighted EPIs were coregistered into T2-weighted FSE images of each subject using an automated image registration program (AIR 5.2.5).22,23 The functions considered were successive linear (affine 12-parameter model) through sixth-order nonlinear (252-parameter model) 3-dimensional polynomial warping functions. Each warping function was then applied to transform the FA maps to evaluate the effects of spatial warping. We selected 2 regions of interest (ROIs), the genu and splenium of the corpus callosum, because our main purpose in this study was to investigate the topography of the corpus callosum. Spherical ROIs 6 mm in diameter were positioned on the slice of T2-weighted FSE images rather than on the FA maps, in which each anatomic structure was visualized to be of maximal thickness (Fig. 1). After the application of each warping function, the mean and standard deviation (SD) of the FA for each region were measured. Although neuronal fibers in these structures align in a highly coherent manner, resulting in a high FA, they were surrounded by low FA regions (eg, cerebrospinal fluid, gray matter). Therefore, improved registration was determined by increased mean and/or decreased SD of the FA.24 The statistical significance of difference in the FA mean and SD for order of spatial warping was analyzed by repeated-measures analyses of variance (ANOVA), followed by the post hoc Scheffe test for multiple comparisons. Statistical significance was established at P < 0.05.FIGURE 1. Regions of interest (ROIs) in the genu and splenium of the corpus callosum. Spherical ROIs (shown in white) 6 mm in diameter were positioned on the slice of T2-weighted fast spin echo images (genu: upper left, splenium: lower left) rather than on fractional anisotropy (FA) maps, in which each anatomic structure was visualized to be of maximal thickness. The ROIs were also overlaid on spatially warped FA maps.Diffusion Tensor TractographyAfter determining the optimal warping function, the computed transformation parameters were also applied to the distortion-corrected diffusion-weighted EPIs. Diffusion tensor tractography was analyzed with home-built software developed by 1 of the authors (Y.M.).25 This software is available on the web (http://www.ut-radiology.umin.jp/people/masutani/dTV_frame-e.htm ). The eigenvector associated with the largest eigenvalue or the principal axis was assumed to represent the local fiber direction. A set of locations for the initiation of the tracking algorithm or “seed-volumes” was first selected on reconstructed midsagittal T2-weighted images. The tracking algorithm then moved a distance of 0.66 mm along the principal axis. The diffusion tensor at the next location was determined from the adjacent voxels by trilinear filtering, and its principal axis was subsequently estimated. The tracking then traveled a further 0.66 mm along this direction. Tracking lines were traced in this way and were propagated in antegrade and retrograde directions until the FA fell below an assigned threshold (FA = 0.2).Seed-volumes were located on the dorsal and ventral halves of the genu and the rostrum. The body and the splenium of the corpus callosum were composed of neural fibers that run closely to each other but are destined to different targets, however. To separate these fibers, the “2 regions of interest” method was used as described in previous articles.9,25,26 This method was used to define the second ROI or target volume that exclusively contained fibers of interest at a certain distance from the seed-volume. Freehand-drawn ROIs were set as starting points in the areas, including the body and splenium of the corpus callosum, and tracking lines were retained if those lines entered target volumes. Target volumes were located lateral or superior to the body at temporal or parieto-occipital white matter, respectively, in terms of the tracking in the splenium. Typical computation time to reconstruct trajectories per analysis was 5–10 seconds running on a standard personal computer workstation (Dimension 8300).RESULTSOptimal Order of Spatial Warping FunctionFor the genu, repeated-measures ANOVA revealed statistical differences in the mean (P < 0.0001) and SD (P < 0.0001) between groups. The Scheffe test showed that there were significant increases in the mean with the fourth-order (P = 0.0085), fifth-order (P = 0.0005), and sixth-order (P = 0.0007) spatial warps compared with the second-order warps and that there was no other significant change in the mean (Fig. 2A). The post hoc test showed that there were significant decreases in the SD with the fifth-order and sixth-order spatial warps compared with the linear-order (P = 0.015 for fifth-order, P = 0.014 for sixth order), second-order (P = 0.0001 for fifth-order and sixth-order), and third-order (P = 0.043 for fifth-order, P = 0.042 for sixth-order) warps (see Fig. 2B). In contrast, for the splenium, repeated-measures ANOVA revealed no significant change between groups (Fig. 3). Thus, we concluded that the fifth-order polynomial warping function provided the optimal distortion-correction scheme to investigate the topography of the corpus callosum.FIGURE 2. The mean and standard deviation for the fractional anisotropy in the genu after linear through sixth-order spatial warping. *P < 0.05, **P < 0.01, and ***P < 0.001 indicates statistical significance with the post hoc Scheffe test. The data are expressed as mean ± SD.FIGURE 3. The mean and standard deviation for the fractional anisotropy in the splenium after linear through sixth-order spatial warping. No statistical significant differences were revealed with repeated-measures ANOVA. The data are expressed as mean ± SD.Diffusion Tensor TractographyThe tracking lines (forceps minor) crossing through the ventral half of the genu commonly passed along the medial course to the frontal lobe, and those through the dorsal half passed along the lateral course (Fig. 4). In contrast, the lines crossing through the rostrum of the corpus callosum ran along the more inferior course and seemed to go into the orbital surfaces of the frontal lobes. The tracking lines through the body mainly passed along the upper course to the frontal or parietal lobe (Fig. 5). A smaller number of lines ran along the lateral course, probably because of partial volume averaging in the corona radiata or semiovale center, where white matter tracts with a wide variety of directions were intermingled (eg, superior longitudinal fasciculus). The tracking lines that entered posterior temporal white matter (tapetum) crossed through the ventral portion of the splenium. In the splenium of the corpus callosum, the lines representing the tapetum were surrounded by the lines that reached parieto-occipital white matter (forceps major) and were clearly distinguished (Fig. 6). These results were consistent in all subjects, and there was no exceptional case.FIGURE 4. Diffusion tensor tractography through the genu and the rostrum of the corpus callosum. The frontal (A) and lateral (B) views are overlaid on axial and sagittal T2-weighted fast spin echo images, respectively. The tracking lines crossing through the ventral half of the genu (shown in red) commonly passed along the medial course to frontal lobe, and those through the dorsal half (shown in blue) passed along the lateral course. The lines crossing through the rostrum (shown in yellow) ran along the more inferior course compared with the forceps minor and seemed to go into the orbital surfaces of the frontal lobes.FIGURE 5. Diffusion tensor tractography through the body of the corpus callosum. The superoinferior (A) and lateral (B) views are overlaid on axial and sagittal T2-weighted fast spin echo images, respectively. The tracking lines through the body mainly passed along the upper course to the frontal or parietal lobe (shown in red). A smaller number of lines (shown in blue) ran along the lateral course, probably because of partial volume averaging in the corona radiata or semiovale center, where white matter tracts with a wide variety of directions were intermingled (eg, superior longitudinal fasciculus).FIGURE 6. Diffusion tensor tractography through the caudal portion of the body and splenium of the corpus callosum. A, Three-dimensional projections of the tracking lines overlaid on axial and sagittal T2-weighted fast spin echo images. The lines that entered posterior temporal white matter (tapetum) crossed through the ventral portion of the splenium of the corpus callosum (shown in red). B, In-plane location overlaid on a sagittal T2-weighted fast spin echo image. In the splenium of the corpus callosum, the lines representing the tapetum were surrounded by the lines that reached parieto-occipital white matter (forceps major, shown in blue) and were clearly distinguished.DISCUSSIONWe have shown the crossing fiber trajectories through the corpus callosum with distortion-corrected DTT. The neuroanatomic investigations have shown that the corpus callosum consists of 4 parts.15–18 The genu of the corpus callosum connects the lateral and medial surfaces of the frontal lobes, forming the forceps minor. The rostrum of the corpus callosum is the thin ventral tapering portion extending from the genu to the lamina terminalis, connecting the orbital surfaces of the frontal lobes. The body of the corpus callosum extends between the genu and the splenium, connecting wide neocortical homotopic regions of the cerebral hemispheres. Our DTT results in these regions were concordant with those of previous neuroanatomic studies.15–18 Unlike postmortem studies, however, DTT is completely noninvasive in nature, and it is possible to sequentially compare in vivo alterations in tracking results in normal maturation and aging as well as in patients with various disorders affecting the corpus callosum.The most intriguing finding is that the trajectories destined to temporal and parieto-occipital white matter (these fibers are referred to as the tapetum and forceps major, respectively) were clearly separated in the splenium of the corpus callosum. The neuroanatomic studies have revealed some fibers from the splenium of the corpus callosum that cover the roofs of the lateral ventricle, further extending to cover the lateral aspect of the temporal horns of the lateral ventricles on each side, thereby forming the tapetum.17,18 To our knowledge, however, no neuroanatomic description has reported what portion of the corpus callosum the fiber bundles that go into the tapetum or forceps major pass through. Although the problem caused by 2 or more crossing fibers within a voxel, as described previously in this report, might obscure a few tracking lines that intersect in the corpus callosum, we believe that most fiber bundles destined to go into the tapetum and forceps major exist separately in the corpus callosum. This finding is to be elucidated in the future, but if this is the case, the disruption of the fiber bundles originating from the tapetum and the forceps major may occur asynchronously and should be evaluated separately. In such a situation, DTT may help to determine the position of the ROI.There are a few limitations to this study. First, the nominal spatial resolution is 1.9 mm × 1.9 mm × 5 mm, which is larger than the size of the neural fibers, ranging from 10 to 100 μm. Even if the slice thickness is 2.5–3 mm, each voxel measured with MRI or DTT contains a large number of neural fibers with different principal axes that intersect within a voxel. As a result, when the principal axis of each fiber is not oriented in a highly coherent manner, the estimated axis of the first eigenvector is different from the actual direction of each fiber, resulting in early termination or deviation of tracking. Furthermore, the normal subjects we investigated in this study were enrolled in the ongoing cohort study of patients with psychiatric disorders, and it was necessary to share the scanning protocol in the normal volunteers and patients. Some patients may be sensitive to nerve-racking noise during EPI acquisition, and longer acquisition time is not likely to be feasible in the clinical setting.The second limitation rests in how to determine the normal and pathologic variations in patterns of the reconstructed fiber lines and their association with different cortical areas. It is important to establish the procedures to reconstruct the fiber trajectories in different human subjects reproducibly and to validate them qualitatively through comparison with neuroanatomic results, as in this study. Nevertheless, it can be hard to assess the interindividual variability of these reconstructed axonal tracts in the individual’s frame of reference. Recently, spatially normalized diffusion tensor data have been presented for analyzing the spatial distribution of neural fibers in the brain and enabled to evaluate the interindividual variability in a common spatial reference frame (Talairach space).11–13 Although this is a promising methodology to elucidate changes in fiber integrity and connectivity during pathologic processes, spatial normalization may obscure subtle interindividual variation. The aim of our study is to clarify the crossing fiber trajectory through the corpus callosum, and spatial normalization is beyond the scope of this study. It is an issue in our ongoing research, however.In summary, notwithstanding a few limitations, DTT is a feasible noninvasive tool to evaluate the commissural fiber trajectory, especially in the rostrum, genu, and splenium of the corpus callosum, where these results were concordant with the classic descriptions of postmortem neuroanatomic studies. 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Yoshitaka PHD; Aoki, Shigeki MD, DMSC; Yamasue, Hidenori MD; Yamada, Haruyasu MD; Kasai, Kiyoto MD; Mori, Harushi MD; Hayashi, Naoto MD, DMSC; Masumoto, Tomohiko MD; Ohtomo, Kuni MD, DMSCNeuroimaging428