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|NI FEATURE: THE EDITORIAL DEBATE III-- PROS AND CONS
|Year : 2018 | Volume
| Issue : 6 | Page : 1603-1611
Diffusion tensor imaging: An overview
Sikander Shaikh1, Anjani Kumar2, Abhishek Bansal2
1 Department of Radiology, Yashoda Hospitals, Somajiguda, Hyderabad Raj Bhavan Road; Department of Radiology, Shadan Medical College; Department of Biomedical Engineering, Indian Institute of Technology, Secunderabad, Hyderabad, Telangana, India
2 Department of Radiology, Yashoda Hospitals, Near Sri Hari Hara Kala Kala Bhavan, Secunderabad, Hyderabad, Telangana, India
|Date of Web Publication||28-Nov-2018|
Dr. Sikander Shaikh
Department of Radiology, Yashoda Hospitals, Somajiguda, Hyderabad Raj Bhavan Road, Hyderabad, Telangana
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Shaikh S, Kumar A, Bansal A. Diffusion tensor imaging: An overview. Neurol India 2018;66:1603-11
Technology, in all fields of science, has advanced by leaps and bounds. The largest component of this discipline is medical imaging that involves the production of an image used to diagnose medical conditions. Radiology has evolved from purely a diagnostic branch to an interventional specialty.
Diffusion MRI (or dMRI) is a magnetic resonance imaging (MRI) methodology which came into existence in the mid-1980s., It permits the noninvasive, in-vivo mapping of the diffusion process of molecules, mainly water, in biological tissues. The diffusion patterns of water molecules can, therefore, reveal microscopic details about tissue architecture, either in the normal or in the diseased state. The first diffusion MRI images of the normal and diseased brain were made public in 1985. Since then, diffusion MRI has been extraordinarily successful. Its main clinical application has been in the study and treatment of neurological disorders, especially for the management of patients with acute stroke.
Diffusion tensor imaging (DTI) is a magnetic resonance (MR) imaging technique that can be used to characterize the orientational properties of the diffusion process of water molecules., DTI enables the measurement of the restricted diffusion of water in tissues in order to produce neural tract images. Usually, the information is contracted to two types of parameters: diffusion anisotropy, which represents the amount of directionality, and orientation of the axis along which water molecules move preferentially.,,,,
Diffusion tensor imaging generates multiple parameters like apparent diffusion coefficient (ADC) and fractional anisotropy (FA), which can be used to study the pathological as well as the normal appearing areas of the brain. As diffusion tensor imaging can reveal abnormalities in white matter fiber structure and can provide models of brain connectivity, it is now rapidly becoming a standard for the radiological assessment of white matter disorders.
In DTI, each voxel has one or more pairs of parameters: a rate of diffusion, and a preferred direction of diffusion, described in terms of three dimensional space, for which that parameter is valid. DTI has been the cornerstone of white matter tract imaging since its inception into diagnostic radiology. The concepts of DTI and its potential clinical applications have triggered an enormous interest in researchers, with a significant amount of research being undertaken in this field at present.
The sample size for conducting the DTI study has been calculated using the Kish Leslie formula: N = Z2 [P (1-P)]/C2 where, N = sample size, Z = standard normal deviation of 1.96, corresponding to 95% confidence interval, P = prevalence rate which ranges between 0.02 – 0.05%, and C = the degree of accuracy of the results (marginal error), set at 0.07.
The required sample size with a prevalence rate of 0.05% was calculated to be 38; however, the researchers of the study in focus have opted to conduct the study on 50 patients.
Factors influencing the DTI examination
The factors influencing DTI examination were divided into the demographic data, which included the patient's age, gender, address, contact telephone number, and body mass index (BMI); and, the patient's history with special importance being attributed to symptoms of central nervous system manifestations like headache, seizures, altered sensorium and focal neurological deficits, as well as any other pre-existing medical/surgical conditions.
DTI was done on a 3.0 Tesla MRI scanner (SIEMENS 3T MagnetomSkyra). DTI data was acquired using a single-shot echo planar imaging (EPI) sequence with parallel imaging. The imaging matrix was 128 × 128 with a field of view of 220 × 220 mm (nominal resolution of 2.5 mm). The image orientation was axial with 2.5 mm slice thickness, which was aligned parallel to the anterior–posterior commissure line. A total of 50 slices covered the entire cerebral hemispheres and the brainstem. The diffusion weighting was encoded along 20 independent orientations with maximum b = 1000 mm2/s.
Co-registered conventional clinical pulse sequences, including T1 weighted (W), T2W, and fluid attenuated inversion recovery (FLAIR) sequence in a combination of coronal, sagittal and axial planes with the same resolution were also recorded for anatomical guidance. Post contrast T1 images were also recorded. Advanced imaging techniques like magnetic resonance (MR) spectroscopy, MR perfusion studies, and MR cerebrospinal fluid (CSF) flow studies were performed in indicated cases.
The scanning time was 6 min 34 sec per DTI sequence, and the overall imaging time was approximately 50 minutes for routine sequences; however, it varied and increased up to 2 hours, as many other sequences were also needed in some patients, as sometimes, sequences had to be repeated and some patients needed general anesthesia/sedation.
The DTI datasets were transferred to a workstation and processed. All diffusion weighted images were visually inspected for the presence of apparent artefacts due to subject motion. Then, the 6 elements of the diffusion tensor were calculated for each pixel using multi-variant linear fitting. After diagonalization, three eigen values (λ1, λ2, λ3) and three eigenvectors (v1, v2, v3) were obtained. For the anisotropy map, the so-called fractional anisotropy (FA) parameter was calculated, which was scaled from 0 (isotropic) to 1 (anisotropic).
The eigenvector associated with the largest eigenvalue was utilized as an indicator for the fiber orientation. In the color map, red (R), green (G), and blue (B) colors were assigned to right-left, anterior-posterior, and superior-inferior orientations, respectively. For the color presentation, a 24-bit color scheme was used, in which each of RGB colors had an 8-bit (0–255) intensity level. In order to suppress orientation information in isotropic brain regions (there should not be a preferential orientation in isotropic areas and the calculated orientations in such areas are dominated by noise), the 24-bit color value was multiplied by the FA value.
DTI matrices are measured by FA and apparent diffusion coefficient (ADC) values, which were taken in the tumor core, peritumoral region and similar normal contralateral white matter tract in all the tumor patients included in the study, which showed the involvement of white matter fiber tracts of the brain.
Three-dimensional (3D) tract reconstruction
Thirteen tracts in each patient were reconstructed and analyzed. These included: 1. Projection fibres (right and left): Corticospinal tracts (CST); 2. Association fibres (right and left): Superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), superior fronto-occipital fasciculus (SFOF), inferior fronto-occipital fasciculus (IFOF), cingulate fasciculus (CF); and, 3. Commisural fibres: Corpus callosum (CC).,,,,,,,,,,,,,,,,,,,,
| ╗ Reconstruction Algorithm|| |
The 3D tract reconstruction was performed using the FACT (Fibre Assignment by Continuous Tracking) method described by Mori et al., and Xue et al., which performs a straightforward linear line propagation based on the predominant vector angle. An anisotropy threshold of FA >0.2 was used for starting the fibre tracking, which was stopped if the FA reached less than 0.20 and/or the fibre turning angle exceeded 80 degrees.
For fiber reconstruction, a tract of interest was identified along its entire length. whenever possible, based on the present anatomical knowledge, and multiple region of interests were strategically marked according to known fiber trajectories. Tracking of fibers were done using the ‘From regions of interest (ROI)’ approach,, wherein the tracking originated from the pixels within the ROI [Figure 1]. More elaborate and cumbersome “brute-force” approach (where in all pixels are visited and examined and all tracts originating from such pixels and penetrating the ROI are tracked) was not followed, as this approach is much more time-consuming.
|Figure 1: Principles of tract reconstruction using the ―From ROI and the ―Brute-force approaches. (A) Example of a tract structure with 2 branching points. (B) Results of DTI measurement, a vector field that shows the fibre orientation at each pixel. A bold box shows the anatomical landmark where a ROI is defined. (C) Results of the tract reconstruction using the “From ROI” approach, in which tracking is started from the ROI. This generally leads to an incomplete delineation of the tract. (D) Results of the “Brute force” approach, in which tracking is started from all pixels. Several initiation (seed) pixels from which the tracking can lead to the same ROI are demonstrated|
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We have used several fibre-editing techniques that employ multiple ROIs to delineate tracts of interest. There are three types of multi-ROI operations; AND, OR, and NOT [Figure 2]. These operations were employed diversely depending on the characteristic trajectory of each tract. The most commonly used function was the “AND” operation, applied to identify specific fibres that connect more than one anatomical landmark depicted by ROIs.
|Figure 2: Schematic diagram of three ROI operations. When the first ROI is drawn, all tracts that penetrate the ROI are retrieved [black, red and blue fibres] (a). If the second ROI is applied as an “AND” operation, only the fibres that penetrate both ROIs are retained (black and red fibres). If a “NOT” operation is applied to the second ROI, a subset of the fibres penetrating the first ROI (ROI #1) but not the second ROI (ROI #2) is selected (black and blue fibres). When the “OR” operation is used, multiple tracking results are combined (b)|
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1. Three-dimensional (3D) visualization: The trajectories of the white matter tracts were visualized using Neuro3D (software provided by Siemens), as described above; and, 2. Two dimensional (2D) presentation: Using Neuro3D, two types of two-dimensional presentations were used: DTI-based color-coded orientation maps (color maps), and white matter parcellation maps. In the latter, 3D reconstruction results are superimposed on T1/T2 based anatomical maps.
White matter tracts were classified into four groups: tracts in the brainstem; and, the association, and commissural tracts in the cerebral hemispheres. Pojection fibres connect cortical and subcortical gray matter, the association tracts connect the two cortical areas, and commissural tracts connect the right and left hemispheres.
Tracts in the brainstem
Five major white matter tracts are present in the brainstem. These are: the superior, middle, and inferior cerebellar peduncles, the corticospinal tract and the medial lemniscus. The superior cerebellar peduncle is the main efferent pathway from the dentate nucleus of the cerebellum toward the thalamus. At a superior level in the brainstem, its decussation can be found as a circular red (right–left orientation) structure. The inferior cerebellar peduncle contains afferent and efferent connections to the cerebellum. It originates in the caudal medulla, traverses the pons, and branches into the cerebellar cortex. In the color map, it can be readily identified at the dorsal area of the medulla and the pons. The middle cerebellar peduncle also contains efferent fibres from the pons to cerebellum, forming a massive sheet-like structure that wraps around the pons. The medial lemniscus is a major pathway for ascending sensory fibres. It decussates at the level of the ventral medulla.
Two classes of projection fibres were reconstructed for this Atlas More Details: The corticothalamic/thalamocortical fibers (collectively called thalamic radiations) and the long corticofugal (corticoefferent) fibres. The corticofugal fibers include such fibres as the corticopontine, corticoreticular, corticobulbar, and corticospinal tracts.
The thalamus is known to have reciprocal connections to a wide area of the cortex. The different parts of the thalamic radiation that penetrate the anterior limb, posterior limb, and retrolenticular part of the internal capsule are labelled as anterior, superior (central), and posterior thalamic radiations, respectively.
Association fibres connect different areas of the cortex and are classified into short and long association fibres. The former connect areas within the same lobe and include the fibres connecting adjacent gyri, called the U-fibres. The long association fibres connect different lobes, forming prominent fibre bundles. These include the superior longitudinal fasciculus, inferior longitudinal fasciculus, superior fronto-occipital fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus. The three major association fibres connecting the limbic system are the cingulum, fornix, and stria terminalis.
The corpus callosum interconnects the two cerebral hemispheres. Most of these fibres interconnect homologous cortical areas in roughly mirror-image sites. The normal tractography images of tracts of the association, commissural and projection fibres included in our study are shown in [Figure 3].
|Figure 3: Pictorial representation of the normal white matter tracts included in our study (arrow) (a) bilateral superior longitudinal fasciculus; (b) bilateral superior fronto-occipital fasciculus; (c) bilateral inferior longitudinal fasciculus; (d) bilateral inferior fronto-occipital fasciculus; (e) bilateral cingulate fasciculus;( f) corpus callosum; and, (g) bilateral corticospinal tracts|
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Most of the patients are usually referred for MRI brain from the neurology, neurosurgery, medicine and paediatric out-patient department and emergency casualty room. A brief clinical history, correlating with their examination findings, should be noted down from the patient. Based on the clinical findings, further laboratory investigations and imaging studies using DTI should be planned. The following observations are tabulated:
White matter tract involvement has been categorized as those associated with edema, infiltration, displacement, or disruption [Figure 4]. According to the method described by Witwer et al., white matter tracts have been characterized as “displaced” if they maintained normal anisotropy relative to the corresponding tract in the contralateral hemisphere but were situated in an abnormal location or with an abnormal orientation on color-coded orientation maps; “edematous,” if they maintained normal anisotropy and orientation but demonstrated a high signal intensity on T2-weighted MR images; “infiltrated,” if they showed reduced anisotropy but remained identifiable on orientation maps; and, “disrupted,” if anisotropy was markedly reduced such that the tract could not be identified on orientation maps.
The following tracts are analyzed in detail
Projection fibres: Corticospinal tracts (CST); Association fibres: Superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF), superior fronto-occipital fasciculus (SFOF), inferior fronto-occipital fasciculus (IFOF), cingulate fasciculus (CF); and Commisural fibres: corpus callosum (CC) [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15].,,,,,,,,
|Figure 5: Case 1: (a) Axial T2WI, and (b) post-contrast TIWI images reveal a well-defined peripheral enhancing lesion in the right posterosuperior temporo-parietal region with perilesional vasogenic edema and internal areas of necrosis and hemorrhage; (c) MR spectroscopy reveals increased lipid lactate peak with a relatively preserved N-acetyl aspartate (NAA) peak; (d) DTI color map; (e) Regions of interest (ROI) drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core, and reduced FA and decreased mean ADC in the peritumoral region as compared to contralateral tracts; (f) Tractography images reveal infiltration and disruption of posterior fibres of right superior fronto-occipital fasciculus (arrow). The histopathology revealed a high grade glioma|
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|Figure 6: Case 2. (a) Axial T2WI, and (b) post contrast TIWI images reveal a non-enhancing mass lesion in the left frontal lobe extending into the frontal horn of left lateral ventricle. (c) MR spectroscopy reveal reduced NAA, elevated choline and creatine peaks. (d) Diffusion color map. (e) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts shows reduced FA and increased mean ADC in the tumor core and increased FA and increased mean ADC in the peritumoral region, as compared to the contralateral tracts. (f and g) Tractography images reveal infiltration of the corpus callosum and anterior fibres of the left inferior fronto-occipital fasciculus (arrow). The histopathology revealed a low grade glioma|
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|Figure 7: Case 3. (a) Axial T2WI, and (b) post contrast T1WI images reveal a non-enhancing lesion in the left temporal lobe on the medial aspect; (c) MR spectroscopy reveal elevation of choline and creatine peak with decreased NAA peak within the lesion; (d) Diffusion color map; (e) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core, and increased FA and increased mean ADC in the peritumoral region as compared to the contralateral tracts; (f) Tractography images reveal displacement of left inferior fronto-occipital fasciculus.(arrow). The histopathology revealed a low grade glioma|
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|Figure 8: Case 4. (a) Post contrast T1WI reveals a homogenous moderately enhancing lesion in the right superior frontal convexity; (b) diffusion color map; (c) ROI drawn in the peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the peritumoral region as compared to the contralateral tract; (d) tractography image reveals displacement of the right cingulate fasciculus (arrow). The histopathology revealed a meningioma|
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|Figure 9: Case 5: (a) Axial T2WI, and (b) post contrast T1WI images reveal a peripherally enhancing, large, well-defined lesion in the right external capsule, anterior insular cortex and lentiform nucleus; (c) MR spectroscopy reveals reduced NAA with increased choline peak; (d and e) MR perfusion images reveal increased r CBV and r CBF in the peripheral enhancing region of the tumor; (f) Diffusion color map; (g) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core and peritumoral region as compared to the contralateral tracts; h) Tractography image reveals displacement of the right corticospinal tract (arrow). The histopathology revealed a high grade glioma|
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|Figure 10: Case 6: (a) Axial T2WI, and (b) post contrast T1WI images reveal a heterogeneously enhancing lesion in midbrain and the pons on the left side; (c) MR spectroscopy reveals elevated choline with reduced NAA peaks; (d) diffusion color map; (e) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts shows reduced FA and increased mean ADC in the tumor core and in the peritumoral region as compared to the contralateral tracts; (f) tractography image reveals infiltration of the corticospinal tract on the left side (arrow). The histopathology revealed a low grade glioma|
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|Figure 11: Case 7. (a) Axial T2WI, and (b) Post-contrast T1WI images reveal a large moderately enhancing lesion in the midline at the floor of the anterior cranial fossa; (c) MR spectroscopy reveals an increase in the choline and creatine peak with decreased NAA peak within the lesion; (d and e) MR perfusion reveals an increased r CBV and r CBF within the lesion; (f) diffusion color map; (g) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and reduced mean ADC in the tumor core, and reduced FA and increased mean ADC in the peritumoral region as compared to the contralateral tracts; (h) the tractography image reveals infiltration and disruption of anterior fibres of the right inferior longitudinal fasciculus (arrow). The histopathology revealed a meningioma|
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|Figure 12: Case 8: (a) Post-contrast T1WI reveals a heterogeneously enhancing lesion in the left temporal lobe; (b and c) MR perfusion images reveal an increased r CBV value within the lesion; (d) diffusion color map; (e) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core and in the peritumoral region as compared to the contralateral tracts; (f) tractography image reveals infiltration and disruption of the left inferior fronto-occipital fasciculus (arrow). The histopathology revealed a high grade glioma|
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|Figure 13: Case 9: (a) Post-contrast T1WI reveals a peripherally enhancing lesion in the left temporal lobe with internal cystic areas; (b) diffusion color map; (c) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core, and reduced FA and reduced mean ADC in the peritumoral region as compared to the contralateral tracts; (d) tractography image reveals displacement and infiltration of the left superior fronto-occipital fasciculus (arrow). The histopathology revealed metastasis|
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|Figure 14: Case 10: (a) Axial T2WI and, (b) post contrast T1WI images reveal a well-defined enhancing lesion in the left temporal lobe and the left cerebellar hemisphere; (c) MR spectroscopy inside the cerebellar lesion reveals an increased choline with reduced NAA peak; (d) diffusion color map; (e) ROI drawn in the tumor core, peritumoral region and corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core and the peritumoral region as compared to the contralateral tracts; (f) tractography image reveals infiltration and disruption of the left inferior longitudinal fasciculus (arrow). The patient was a known case of carcinoma breast. The histopathology revealed a metastasis|
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|Figure 15: Case 11. (a) Axial T2WI image reveals gyral swelling and T2 hyperintensity with vasogenic edema in the left temporal lobe. (b) Post-contrast T1WI image reveals no significant enhancement; (c and d) MR perfusion images reveal no increase in the r CBV and r CBF in the lesion as compared to the normal brain parenchyma; (e) diffusion color map; (f) ROI drawn in the tumor core, the peritumoral region and the corresponding contralateral tracts show reduced FA and increased mean ADC in the tumor core and peritumoral region as compared to the contralateral tracts; (g) tractography image reveals infiltration and disruption of the left inferior longitudinal fasciculus (arrow); The histopathology revealed a low grade glioma|
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| ╗ Conclusion|| |
MRI is an excellent modality to image pathology of the brain. Diffusion tensor imaging and fibre tractography, a state-of-the-art technology, when used as an adjunct with the conventional MRI sequences, reveals significant vital information about the white matter and the white matter tracts. MR tractography has been very useful in the non-invasive identification of trajectories of important white fibre tracts of the brain. Tractography is able to show the orientation and association of white matter fibre tracts in the presence of brain edema; and their displacement, infiltration and/or disruption, that often occurs due to brain tumors.
Uses of DTI in future clinical scenarios
- The efficacy of DTI derived FA in differentiating a high grade and a low grade glioma may be performed in a larger case control study
- By including diffusion kurtosis parameters and fibre density index (FDi) with other DTI matrics like the FA and ADC, the efficacy of DTI in differentiating a high grade from a low grade glioma may be increased
- The possibility of differentiation of a high grade and low grade glioma may also be increased by combining MR DTI and MR perfusion.
| ╗ References|| |
Bihan D Le, Breton E. Imagerie de diffusion in-vivo
par résonance magnétique nucléaire. Comptes-Rendus l'Académie des Sci. 1985;93:27-34.
Taylor DG, Bushell MC, Search H, Journals C, Contact A, Iopscience M, et al
. The spatial mapping of translational diffusion coefficients by the NMR imaging technique. Phys Med Biol 1985;30:345-9.
Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 1986;161:401-7.
Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259-67.
Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 2002;15:435-55.
Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, et al
. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 1990;176:439-45.
Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996;201:637-48.
Makris N, Worth AJ, Papadimitriou GM, Stakes JW, Caviness VS, Kennedy DN, et al
. Morphometry of in vivo
human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann Neurol 1997;42:951-62.
Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994;31:394-400.
Henkelman RM, Stanisz GJ, Kim JK, Bronskill MJ. Anisotropy of NMR properties of tissues. Magn Reson Med 1994;32:592-601.
Lee S-K, Kim DI, Kim J, Kim DJ, Kim HD, Kim DS, et al
. Diffusion-tensor MR imaging and fiber tractography: A new method of describing aberrant fiber connections in developmental CNS anomalies. RadioGraphics 2005;25:53-68.
Hagmann P, Jonasson L, Maeder P, Thiran J-P, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: From scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. RadioGraphics. 2006;26(suppl_1):S205-23.
Filler A. Magnetic resonance neurography and diffusion tensor imaging: Origins, history, and clinical impact of the first 50,000 cases with an assessment of efficacy and utility in a prospective 5000-patient study group. Neurosurgery 2009;65 (4 Suppl):A29-43.
Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al
. Diffusion tensor imaging: Concepts and applications. J Magn Reson Imaging 2001;13:534-46.
Melhem ER, Mori S, Mukundan G, Kraut MA, Pomper MG, van Zijl PC. Diffusion tensor MR imaging of the brain and white matter tractography. Am J Roentgenol 2002;178:3-16.
Duffau H, Capelle L, Sichez N, Denvil D, Lopes M, Sichez JP, et al
. Intraoperative mapping of the subcortical language pathways using direct stimulations. An anatomo-functional study. Brain. 2002;125(Pt 1):199-214.
Masutani Y, Aoki S, Abe O, Hayashi N, Otomo K. MR diffusion tensor imaging: Recent advance and new techniques for diffusion tensor visualization. Eur J Radiol 2003;46:53-66.
Zhai G, Lin W, Wilber KP, Gerig G, Gilmore JH. Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit. Radiology 2003;229:673-81.
Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: A pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol 2004;25:356-69.
Holodny AI, Gor DM, Watts R, Gutin PH, Uluğ AM. Diffusion-tensor MR tractography of somatotopic organization of corticospinal tracts in the internal capsule: Initial anatomic results in contradistinction to prior reports. Radiology. 2005;234:649-53.
Okada T, Miki Y, Fushimi Y, Hanakawa T, Kanagaki M, Yamamoto A, et al
. Diffusion-tensor fiber tractography: Intraindividual comparison of 3.0-T and 1.5-T MR imaging. Radiology 2006;238:668-78.
Okada T, Mikuni N, Miki Y, Kikuta K, Urayama S, Hanakawa T, et al
. Corticospinal tract localization: Integration of diffusion-tensor tractography at 3-T MR imaging with intraoperative white matter stimulation mapping—preliminary results. Radiology 2006;240:849-57.
Nucifora PG, Verma R, Lee SK, Melhem ER. Diffusion-tensor MR imaging and tractography: Exploring brain microstructure and connectivity. Radiology 2007;245:367-84.
Smits M, Vernooij MW, Wielopolski PA, Vincent AJ, Houston GC, van der Lugt A. Incorporating functional MR imaging into diffusion tensor tractography in the preoperative assessment of the corticospinal tract in patients with brain tumors. Am J Neuroradiol 2007;28:1354-61.
Foroni RI, Ricciardi GK, Sboarina A, Lovato C, De Simone A, Lupidi FL, et al
. Combined use of tractography and Gamma Knife radiosurgery three dimensional treatment planning: Initial experience” in “CARS 2007 Computer Assisted Radiology and Surgery.” Proceeding of the 21st
International Congress and Exhibition Berlin, Germany, June 27-30,2007;2(suppl. 1): S54-S56.
Santhosh K, Thomas B, Radhakrishnan VV., Saini J, Kesavadas C, Gupta AK, et al
. Diffusion tensor and tensor metrics imaging in intracranial epidermoid cysts. J Magn Reson Imaging. 2009;29:967-70.
Gulati S, Berntsen E, Solheim O, Kvistad K, Håberg A, Selbekk T, et al
. Surgical resection of high-grade gliomas in eloquent regions guided by blood oxygenation level dependent functional magnetic resonance imaging, diffusion tensor tractography, and intraoperative navigated 3D ultrasound. Minim Invasive Neurosurg 2009;52:17-24.
Ahn S, Lee SK. Diffusion tensor imaging: Exploring the motor networks and clinical applications. Korean J Radiol 2011;12:651.
Abdullah KG, Lubelski D, Nucifora PGP, Brem S. Use of diffusion tensor imaging in glioma resection. Neurosurg Focus 2013;34:E1.
Brandão LA, Shiroishi MS, Law M. Brain tumors: A multimodality approach with diffusion-weighted imaging, diffusion tensor imaging, magnetic resonance spectroscopy, dynamic susceptibility contrast and dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Imaging Clin N Am 2013;21:199-239.
Server A, Graff BA, Josefsen R, Orheim TE, Schellhorn T, Nordhøy W, et al
. Analysis of diffusion tensor imaging metrics for gliomas grading at 3T. Eur J Radiol 2014;83:e156-65.
Liang R, Wang X, Li M, Yang Y, Luo J, Mao Q, et al
. Potential role of fractional anisotropy derived from diffusion tensor imaging in differentiating high-grade gliomas from low-grade gliomas: A meta-analysis. Int J Clin Exp Med. 2014;7:3647-53.
Shan W, Wang X-L. Clinical application value of 3.0T MR diffusion tensor imaging in grade diagnosis of gliomas. Oncol Lett 2017;14:2009-14.
Dubey A, Kataria R, Sinha VD. Role of Diffusion Tensor Imaging in Brain Tumor Surgery. Asian J Neurosurg 2018;13:302-6.
] [Full text]
Mori S, van Zijl PC. Fiber tracking: Principles and strategies-a technical review. NMR Biomed 2002;15:468-80.
Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo
three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn Reson Med 1999;42:1123-7.
Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999;45:265-9.
Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., et al
. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 2008; 40: 570-82.
Witwer BP, Moftakhar R, Hasan KM, Deshmukh P, Haughton V, Field A, et al
. Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 2002;97:568-75.
Goebell E, Paustenbach S, Vaeterlein O, Ding XQ, Heese O, Fiehler J, et al
. Low-grade and anaplastic gliomas: Differences in architecture evaluated with diffusion-tensor MR imaging. Radiology 2006;239:217-22.
Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, et al
. Gliomas: Diffusion kurtosis MR imaging in grading. Radiology 2012;263:492-501.
Toh CH, Castillo M, Wong AM, Wei KC, Wong HF, Ng SH, et al
. Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. Am J Neuroradiol 2008;29:1630-5.
De Belder FE, Oot AR, Van Hecke W, Venstermans C, Menovsky T, Van Marck V, et al
. Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema. J Comput Assist Tomogr 2012;36:577- 82.
Tsuchiya K, Fujikawa A, Nakajima M, Honya K. Differentiation between solitary brain metastasis and high-grade glioma by diffusion tensor imaging. Br J Radiol 2005;78:533-7.
Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Physica Medica: European Journal of Medical Physics. 2017;40:24-32.
Davanian F, Faeghi F, Shahzadi S, Farshifar Z. Diffusion tensor imaging for glioma grading: Analysis of fiber density index. Basic Clin Neurosc 2017;8:13.
Geneidi EA, Habib LA, Chalabi NA, Haschim MH. Potential role of quantitative MRI assessment in differentiating high from low-grade gliomas. Egyptian J Radiol Nuclear Med. 2016;47:243-53.
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