Diffusion MR Imaging of White Matter Pathways ...

Current Topics Update on Diagnostic Medical Imaging Juntendo Medical Journal 2014. 60 (2), 100〜106

Diffusion MR Imaging of White Matter Pathways: Visualization and Quantitative Evaluation KOJI KAMAGATA＊1), MASAAKI HORI＊1), KOUHEI KAMIYA＊1), MICHIMASA SUZUKI＊1), AKIRA NISHIKORI＊1) 2), FUMITAKA KUMAGAI＊1) 2), MARIKO YOSHIDA＊1), SHINSUKE KYOGOKU＊1), SHIGEKI AOKI＊1) ＊1)

Department of Radiology, Juntendo University Faculty of Medicine, Tokyo, Japan,

＊2)

Department of Radiological

Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan

Diffusion tensor imaging (DTI) is one of the most promising MRI techniques for characterizing microstructural changes of cerebral white matter in brain research and clinical applications. The number of DTI studies is increasing, and more than 7500 articles have been published over the last decade. DTI enables visualization and characterization of white matter tracts in vivo by providing a unique image contrast on white matter that is unavailable with routine MR techniques; this allows three-dimensional (3D) visualization of neuronal pathways and quantification of the diffusion properties of white matter. Since its introduction in 1994, DTI has been used to study the structure of white matter and changes to its integrity within normal brains and brains affected by aging, stroke, dementia, psychiatric disorder, tumor, and other conditions. In this paper, the technical aspects and clinical applications of DTI are reviewed with a focus on clinical use and in vivo studies. The strengths and weaknesses of the approach are discussed and current extensions of the technology (q-space imaging and diffusional kurtosis imaging) are summarized. Key words: diffusion tensor imaging, diffusion tensor tractography, diffusional kurtosis imaging, q-space imaging

Introduction Diffusion tensor imaging (DTI), originally introduced in 1994 1), enables researchers to measure the characteristics of local microstructural water diffusion in brain tissue 1) 2). DTI takes advantage of the macroscopic geometrical arrangement of white matter bundles that becomes clear through diffusion MR imaging, which measures the translational displacement of water molecules 2) 3). The anisotropic behavior of water displacement in white matter has been observed since the earliest studies of conventional diffusion MR imaging 4). In these studies, the motion of water molecules parallel to

the white matter fibers was found to be much faster than that perpendicular to the fibers 1) 3). This difference in motion between the two directions (termed diffusion anisotropy) is the basis of DTI, which describes the magnitude and orientation of diffusion anisotropy. Estimates of white matter connectivity patterns in the brain may be obtained from the diffusion anisotropy and principal diffusion directions through three-dimensional tract reconstruction (diffusion tensor tractography). The diffusion of water within living tissues is altered by changes in tissue microstructure and organization; therefore, DTI is potentially a powerful tool for characterizing the effects of disease and

Corresponding author: Koji Kamagata Department of Radiology, Juntendo University Faculty of Medicine, Tokyo, Japan 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan TEL: + 81-3-3813-3111 〔Received

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FAX: + 81-3-5684-0476

Dec. 4, 2013〕

E-mail: kkamagat@juntendo.ac.jp

Juntendo Medical Journal 60 (2), 2014

major projection pathways (corticospinal tract, corona radiata, etc.), association pathways (superior longitudinal fasciculus, inferior longitudinal fasciculus, uncinate fasciculus, etc.), and commissural pathways (corpus callosum, anterior commissure, etc.) 8). An example of tractography-based segmentation is shown in Figure-1. Diffusion tensor tractography provides exciting opportunities to assess the impact of diseases on specific white matter tracts 9)〜13).

aging on microstructure. Indeed, the use of DTI is rapidly increasing because the technique is highly sensitive to changes at the cellular and microstructural levels. In this paper, we review the interpretation and clinical applications of DTI, discuss the strengths and weaknesses of DTI, and summarize two current extensions of the technique, q-space imaging and diffusional kurtosis imaging. White matter tractography

Interpretation of DTI measures DTI-based diffusion tensor tractography, which allows researchers to visualize white matter tracts and quantitatively evaluate the DTI parameters of individual tracts, is becoming a useful tool for studying human white matter anatomy 5)〜7). Diffusion tensor tractography has been used to generate anatomically appropriate tract reconstructions of

DTI is a powerful tool for detecting microscopic differences in tissue properties. However, the interpretation of changes in measured diffusion tensor parameters is complicated and should be performed with care. In DTI, diffusion measurements are taken in multiple directions, and tensor

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Figure-1

Three-dimensional tractography images

A: Corona radiata. ATR, anterior thalamic radiation (light orange) ; CPT, corticopontine tract (blue) ; CST, corticospinal tract (light blue) ; PTR, posterior thalamic radiation (yellow) ; STR, superior thalamic radiation (light yellow). B: Inferior fronto-occipital fasciculus. C: Cingulum. SP, subgenual portion (red) ; RP, retrosplenial portion (blue) ; PP, parahippocampal portion (green). D: Uncinate fasciculus.

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Kamagata, et al: Diffusion MR imaging of white matter pathways: Visualization and quantitative evaluation

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Figure-2

Quantitative maps of diffusion tensor-based images

A: Apparent diffusion coefficient (ADC) map. ADC maps are similar in contrast to T2-weighted (T2W) images, with cerebrospinal fluid (CSF) appearing hyperintense. B: Fractional anisotropy (FA) map. FA maps show high contrast between gray and white matter. C: Color-coded FA map. Red, green, and blue represent fibers crossing from left to right, oriented in the posterior-anterior direction, and oriented in the inferior-superior direction, respectively. D: λ1 map. E: λ2 map. F: λ3 map.

decomposition is used to extract the axial and radial diffusivities (parallel and perpendicular to the fibers, repectively) 1) 3) 14). These diffusivities are used to calculate summation parameters such as the apparent diffusion coefficient (ADC, the mean of the diffusivities) or fractional anisotropy (FA, the normalized standard deviation of the diffusivities) 3) 15). Although many diffusion tensor parameters have been proposed, FA is the DTI-based parameter most widely used in brain research and clinical applications to represent the diffusional anisotropy of water molecules, because it is sensitive to the integrity of white matter fibers 15). FA provides enhancing diffusion anisotropy differences with intensity limits between zero and one. FA is high in white matter, reflecting fast diffusivity along the fibers and slow diffusivity perpendicular to them 15). In gray matter and cerebrospinal fluid (CSF), FA approaches zero because the diffusivity is similar in all directions 15). Sample maps of the mean diffusivity (MD), FA, major eigenvector direction, and eigenvalues are shown in Figure-2. Many researchers focus on FA as a measure of tissue changes. Although in some analyses FA

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appears to be quite sensitive to a broad spectrum of pathological conditions, it may be insufficient to characterize some tissue changes 16). Although FA may be insufficient to characterize tissue changes, many researchers primarily focus on this parameter. For instance, white matter alteration often reduces the anisotropy, perhaps because of reduced axial diffusivity, increased radial diffusivity, or both. Measurements of ADC may help to better understand how the diffusion tensor changes. In addition, the radial and axial diffusivities have been examined directly in recent studies to provide more specific information on the diffusion tensor 17) 18). FA is a highly sensitive biomarker of neuropathological change, but it is also fairly nonspecific; therefore, the interpretation of DTI measurements for diagnostic and clinical applications is challenging. Although numerous researchers have suggested that FA is an imaging marker of white matter integrity, these proposals are far from decisive. In many studies, reduced FA has been observed in a broad spectrum of diseases, whereas increased FA has rarely been reported. However, if the neuropathological basis for a specific disease is under-


stood, the results may be interpreted with greater specificity. For example, increased tissue water in edema will increase the ADC, whereas cell proliferation in neoplasia may decrease the MD. Demyelination might increase the radial diffusivity with minimal influence on the axial diffusivity, whereas axonal loss might decrease the axial diffusivity 19)〜21). In the following section, we explain the relationships between DTI measures and neuropathology (e. g., edema, demyelination, inflammation), and the clinical applications of these relationships. Edema Edema (elevation in water content) changes the diffusion anisotropy of a tissue; in general, it increases the ADC and reduces the anisotropy. Edema can be categorized into three types: vasogenic (a typical reaction to inflammation), cytotoxic, and cellular (as occurs immediately following ischemia). In vasogenic edema (caused by inflammation or subacute stroke), a marked increase in extracellular volume might lead to reduction of axonal density per voxel. Therefore, vasogenic edema is characterized by a significant reduction of FA 22). In cytotoxic edema, the intracellular volume in white matter increases at the expense of the extracellular space and axons. This might lead to a reduction in axonal density. However, in cytotoxic edema, the changes of diffusion anisotropy are not uniform, and the behavior of DTI remains controversial. Demyelination/ Dysmyelination Because it is unique to white matter, myelin is believed to be one of the main modulators of DTI measures. In an early study, Beaulieu et al. reported that anisotropy is similar in myelinated and nonmyelinated nerves 23). Although myelin is not necessary for observing diffusion anisotropy in neuronal tissue 23), subsequent studies have shown that diffusion anisotropy indeed decreases significantly when myelin is damaged or absent (either in a demyelination condition such as multiple sclerosis (MS) or in the premyelination condition of normal brain development) 24)〜26). During early neuronal development, axial and radial diffusivities appear to decrease with age, although the decline in radial diffusivity is more

substantial, consistent with the development of myelination 27). In a recent study, Song et al. found that the absence of myelin appeared to increase the radial diffusivity but did not significantly affect the axial diffusivity in a mouse model of dysmyelination 28). In patients with MS, reduced FA was reported in areas of MS lesions, although the effects were less dramatic in areas of normal-appearing white matter (NAWM) where demyelination is known to occur 24) 26) 29). In addition, when DTI was used to characterize different types of lesions (enhancing or nonenhancing) and MS (primary progressive or relapsing/ remitting), the groups differed in diffusion anisotropy 30)〜32). Inflammation Thus far, the relationships between DTI measures and inflammation have been characterized in only a few studies. In general, an increase in the water content of a tissue caused by inflammation also increases the ADC, which consequently decreases the diffusion anisotropy. Tievsky et al. found highly elevated ADC in acute MS lesions, whereas chronic or sub-acute lesions exhibited less ADC elevation 33). In the other study, Werring et al. reported that T1-weighted image (T1WI) hypointense lesions showed significant elevation in ADC and that contrast-enhancing lesions (inflammatory) showed the greatest reduction in FA 26). Clinical applications Cerebral ischemia In the acute phase of acute cerebral ischemia, ADC values significantly decrease in the lesion site owing to cell depolarization and cytotoxic edema over the course of minutes to an hour. After 1 to 10 days, the ADC starts to renormalize (pseudonormalization) owing to increased extracellular volume (vasogenic edema). Chronic ischemic lesions (> 2 weeks) typically exhibit significantly increased ADC due to cell lysis and necrosis 34) 35). Changes in FA have often been used to evaluate acute cerebral ischemia. Reduced anisotropy at the lesion site was reported in several DTI studies of acute stroke 22) 36), whereas increased anisotropy at the lesion site was reported in some patients 37) 38). In recent studies, FA has been reported to increase in acute ischemia

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and decrease below baseline levels in the chronic phase 36) 39), the inverse of the changes seen with ADC values. Kunimatsu et al. evaluated small lacunar infarctions near the corticospinal tracts, and found that the proximity of the corticospinal tract to the fresh infarction correlated well with motor function 2 weeks later 40). However, the changes of DTI parameters in acute cerebral ischemia remain controversial, and the radiological and clinical benefits of DTI in acute cerebral ischemia appear to be limited 29). Neoplasia The second most common clinical application of DTI may be in characterizing brain tumors. DTI has been used to characterize tumor tissues, albeit with limited success. The characteristic heterogeneity of diffusion anisotropy in normal white matter is disturbed by the heterogeneity of brain tumors in the presence of complex environments such as edema and mass effects. In general, increased cellular densities are assumed to decrease the ADC; hence, in areas of tissue necrosis, the ADC is expected to be significantly elevated. The ADC has been reported to be associated with tumor grade and cellularity 41) ̶a characteristic that could help with the differential diagnosis of brain tumors. However, the clinical usefulness of DTI remains limited because of overlap in the regional ADC among gliomas of differing grades 42) 43). In some papers, ADC measurements have been compared between lymphomas and high-grade gliomas, which have different cellularities 44)〜46). Guo et al. found that the diffusivities were significantly higher in the lymphomas than in normal tissue, whereas those in the gliomas were only slightly or not elevated relative to the diffusivity of normal tissue 44). However, ADC measurements overlap between gliomas and lymphomas 47). Diffusion tensor tractography, which has also been used for DTI studies of brain tumors, can help evaluate and localize white matter fiber tracts that are important for critical functions such as motion, language, and vision 48)〜50). Armed with this technique, the neurosurgeon can plan surgical procedures that will minimize injury to critical tracts such as the corticospinal tract 51).

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The future of DTI DTI is a powerful tool for visualizing the structure of white matter, and the growing number of DTI studies shows the great potential of the method. However, the assumption of Gaussian diffusion limits the applications of DTI. In DTI, water is assumed to undergo Gaussian diffusion. However, water in biological tissues is restricted by its interactions with other molecules and cell membranes; consequently, the assumption of Gaussian water diffusion may be inadequate to describe the actual diffusion process in biological tissues. Therefore, more dedicated, non-Gaussian diffusion methods for diffusion have been introduced, and reports of their clinical use have recently increased in number. The most popular methods are q-space imaging (QSI) and diffusional kurtosis imaging (DKI) 52). The so-called“q-space”framework-a nonGaussian diffusion analysis that uses multiple b values including high b values̶has been introduced to estimate real structural information 53). Although this method is theoretically superior to conventional DTI, there have been few reports in which it has been applied to human studies 54) 55). DKI is closely related to QSI; indeed, QSI methods have recently been employed to estimate diffusional kurtosis 56), a dimensionless statistical metric for quantifying the non-Gaussianity of an arbitrary probability distribution. In recent studies, DKI has been reported to improve the sensitivity for detecting developmental and pathological neural changes relative to that of conventional DTI for conditions such as age-related degeneration, cerebral infarctions, Parkinson disease, attention-deficit hyperactivity disorder, gliomas, and MS 57)〜61). These methodologies (i. e., QSI, DKI) provide a better theoretical basis on which to address the complicated diffusion of water in white matter. However, they are time-consuming and require high computational power, so they cannot replace the fast data acquisition and analysis capabilities of DTI. Nevertheless, they can yield additional complementary information that is inaccessible with DTI.


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