Diffusion Kurtosis Imaging: An Emerging Technique for Evaluating the Microstructural Environment of the Brain

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1 Neuroradiology/Head and Neck Imaging Review Steven et al. Diffusion Kurtosis Imaging of the Brain Neuroradiology/Head and Neck Imaging Review FOCUS ON: Andrew J. Steven 1 Jiachen Zhuo Elias R. Melhem Steven AJ, Zhuo J, Melhem ER Keywords: diffusion, diffusion kurtosis imaging, kurtosis, MRI DOI: /AJR Received June 12, 2013; accepted after revision September 4, This work is partly supported by grants W81XWH and W81XWH from the US Army. The opinions and assertions contained herein are the private views of the authors and are not to be construed as official or as representing the views of the Department of the Army or the Department of Defense. 1 All authors: Department of Radiology and Nuclear Medicine, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD Address correspondence to A. J. Steven (asteven@umm.edu). WEB This is a web exclusive article. AJR 2014; 202:W26 W X/14/2021 W26 American Roentgen Ray Society Diffusion Kurtosis Imaging: An Emerging Technique for Evaluating the Microstructural Environment of the Brain OBJECTIVE. Diffusion kurtosis imaging is an emerging technique based on the nongaussian diffusion of water in biologic systems. The purpose of this article is to introduce and discuss the ongoing research and potential clinical applications of this technique. CONCLUSION. Diffusion kurtosis imaging provides independent and complementary information to that acquired with traditional diffusion techniques. The additional information is thought to indicate the complexity of the microstructural environment of the imaged tissue and may lead to broad-reaching applications in all aspects of neuroradiology. D iffusion imaging, which is based on brownian motion of water molecules, has been in clinical use for more than 25 years. Originally focused on stroke imaging, the modality has evolved rapidly, gaining numerous clinical applications in neuroradiology and beyond. Diffusion-tensor imaging (DTI) is a more recent application of diffusion imaging and is predicated on determining the directionality of diffusion. DTI has provided additional information on the microstructure of the brain and has been proved to be a particularly elegant, noninvasive method of displaying the anatomic features of the white matter tracts. The organized structure of the brain, including cellular membranes, myelinated axons, and intracellular organelles, affects the orientation of water diffusion. Fractional anisotropy (FA) is a measure of the extent of this directionality and has been a primary imaging metric used in the evaluation of a wide range of neuropathologic processes, from traumatic brain injury (TBI) to demyelinating disease. Although potentially useful in detecting subtle disease and changes not identifiable with conventional MRI sequences, the lack of specificity has limited the clinical applications of DTI. Recent developments in DTI research have extended the focus beyond the evaluation of a simple diffusion scalar into emphasizing the importance of the more complex 3D pattern of diffusion. Additional imaging metrics, such as axial diffusivity (AD), radial diffusivity (RD), and tumor infiltration index, are being used with greater frequency. Although exciting advances and an improved understanding of the brain and a wealth of disease processes have been made with both diffusion-weighted imaging (DWI) and DTI, these imaging modalities are based on the simplified premise of the gaussian distribution of water diffusion in biologic systems. Gaussian distribution is a mathematic model describing the normative distribution of a given population conforming to the wellknown bell curve. In reality, the complex intracellular and extracellular in vivo environment causes the diffusion of water molecules to deviate considerably from this pattern. In probability theory and statistics, alteration of a normative pattern of distribution is known as kurtosis. Diffusion kurtosis imaging (DKI) is an attempt to account for this variation to provide a more accurate model of diffusion and to capture the nongaussian diffusion behavior as a reflective marker for tissue heterogeneity [1]. Studies have shown that evaluation of the variant distribution pattern can provide important microstructural information about the brain and improve white matter characterization. DKI findings can also serve as an independent metric for improving both sensitivity and specificity in the evaluation of disease and as an important imaging parameter for assessing disease progression and treatment response. Figure 1 shows apparent diffusion coefficient and apparent diffusion kurtosis in a phantom model and dramatically shows that DKI provides an entirely independent and separate imaging metric from traditional diffusion imaging [2]. The purpose W26 AJR:202, January 2014

2 Diffusion Kurtosis Imaging of the Brain of this article is to illustrate the technical considerations for DKI, review the current research and clinical applications, and describe the limitations and potential future directions of the technique. Theory Model In the gaussian diffusion model (e.g., in DTI) it is assumed that water molecules diffuse uniformly in a certain direction, as in a bucket of water (Fig. 2). In DTI the diffusion-weighted signal follows a monoexponential decay (equation 1). However in real tissue with complex cellular structures, the water molecules within an imaging voxel (typically mm) diffuse through an environment that is highly heterogeneous in any direction, leading to deviation from the gaussian distribution (Fig. 2). The DKI model [2] includes an excess kurtosis term (K) to capture this deviation from the gaussian distribution (equation 2), 1n[S(b)/S 0 ] = bd app (1) 1n[S(b)/S 0 ] = bd app b2 2 D app K app (2) where D app and K app are the apparent diffusion coefficient and kurtosis along a certain diffusion direction, S(b) is the diffusionweighted signal along that direction with a certain b value, and S 0 is the non-diffusionweighted signal. In probability theory, kurtosis is a term for describing the peakedness of a probability distribution, compared with the gaussian bell shape. Positive kurtosis has a higher peak and heavier tails, and negative kurtosis has a lower Gaussian (DTI) Nongaussian (DKI) Uniform water diffusion peak and lighter tails (Fig. 3A). When K app = 0, the DKI model reduces to the DTI model. Although negative kurtosis is mathematically possible, multicompartment diffusion models and empirical evidence indicate that kurtosis is always nonnegative (K 0) [3]. Diffusion is always fastest initially, slowing as water molecules interact more and more with cellular structures. Higher kurtosis values imply more impediments to normal diffusion and greater complexity within the imaged system. Figure 3B shows the diffusion-weighted signal measured in the corpus callosum fitted by the diffusion and the kurtosis model. When stronger b values are used (b > 1500 s/ mm 2 ) as the technique becomes increasingly A Fig. 1 Parametric maps of phantom. (Reprinted with permission from [2] Copyright 2005 Wiley-Liss, Inc.) A and B, Maps of apparent diffusion coefficient (A) and apparent diffusional kurtosis (B) in slice direction. Scale bar for diffusion coefficient is in units of square meters per millisecond. Bottles A through E contain sucrose solutions with sucrose concentrations ranging from 5% to 25%. Bottle F contains pureed asparagus. Average kurtosis map clearly shows higher degree of structure in asparagus bottle, which is not evident in diffusion coefficient map. K = 0 sensitive to shorter molecular distances and the heterogeneous cellular structures, the diffusion-weighted signal decay deviates from the monoexponential decay predicted in the gaussian DTI model. The DKI model, however, fits the signal decay nicely. Diffusion Kurtosis Imaging Parameters Because diffusion is directional, diffusion kurtosis also varies across directions being measured. The directional diffusion profile can be captured by a 3 3 tensor matrix with three eigenvectors oriented along the three principal axes of the diffusion ellipsoid and the corresponding eigenvalues representing the diffusion coefficient along the each of the principal axes (Fig. 4). Directional kurtosis is characterized by a tensor matrix and represents a more complex spatial distribution. An interpretation of the full characterization of the kurtosis tensor has yet to be explored. The most commonly used DKI parameters are those that have more direct physical relevance to the diffusion tensor [1], namely, mean kurtosis (MK), the average of the diffusion kurtosis along all diffusion directions; axial kurtosis (AK), the kurtosis along the axial direction of the diffusion ellipsoid; and radial kurtosis (RK), the kurtosis along the radial direction of the diffusion ellipsoid. B Nonuniform water diffusion K > 0 Fig. 2 Diagram shows gaussian and nongaussian diffusion displacement in different diffusion environments. DTI = diffusion tensor imaging, DKI = diffusion kurtosis imaging. AJR:202, January 2014 W27

3 Steven et al. Probability K = 0 K < 0 K > 0 Displacement Fig. 3 Kurtosis. A, Graph shows diffusion displacement probability distribution with different degrees of kurtosis. B, Graph shows measured diffusion-weighted signal attenuation ln(s(b)/s 0 ) (blue circles) clearly deviates from linear diffusion function (green line) and is well fit by kurtosis model (black line). Figure 4 shows the simplified distribution of diffusion kurtosis in relation to the diffusion tensor in typical white matter. Kurtosis of ideal gaussian distribution should measure 0, and the numeric value increases as diffusion further deviates from this pattern. In white matter, AK is typically low because the diffusion along the axial direction of the axons is free and relatively unrestricted, leading to the least deviation from the gaussian diffusion. Also in white matter, RK is typically high as cellular membranes and myelin sheaths cause highly nongaussian displacement distribution and a heterogeneous diffusion pattern. Clinical Applications Ischemia and Infarction DWI was a groundbreaking advance in stroke imaging in the 1990s. Restricted diffusion was once thought to be pathognomonic of cerebral infarction, but with experience this binary approach to infarction has become outdated. The extent of tissue damage is often quite heterogeneous within ischemic brain, having so-called restricted diffusion. Improved differentiation between ischemic and infarcted tissue for purposes of identifying potentially salvable brain tissue has become of particular importance as intraarterial and IV reperfusion techniques continue to improve. Hui et al. [4] found heterogeneous kurtosis imaging characteristics within ischemic tissue that was not evident with conventional diffusion imaging measuring apparent diffusion coefficient. Using a retrospective review of the A Fig. 4 Diagram shows kurtosis distribution in relation to diffusion ellipsoid. V 1, V 2, V 3 are eigenvectors; λ 1, λ 2, and λ 3 are eigenvalues of diffusion tensor; and K 1, K 2, and K 3 are kurtosis values along principle directions of diffusion ellipsoid. Signal Attenuation In(S(b)/S 0 ) Kurtosis Distribution Measured data Diffusion fit Kurtosis fit b Value V 1, λ 1, K Diffusion Ellipsoid V 3, λ 3, K 3 V 2, λ 2, K 2 imaging data for patients with acute and subacute strokes, those authors also found significantly higher absolute percentage change using kurtosis imaging and specifically that AK has the greatest increase. This finding is thought to be due to increased tissue heterogeneity and an osmotic imbalance from enlargement of axons and dendrites during infarction. AK (parallel to the direction of the axons) is thought to be primarily affected by intracellular structures, whereas RK (perpendicular to the direction of the axons) is thought more influenced by cellular membranes and myelin sheaths. The findings of greatest change in AK would be consistent with the proposed mechanism of axonal beading in ischemic tissue and support the theory that changes in kurtosis patterns in ischemia are primarily due to the intracellular microenvironment. Cheung et al. [5] used a middle cerebral artery occlusion model in rats to compare a kurtosis model with traditional diffusion data. Areas of abnormal mean diffusion (MD) and MK were measured during arterial occlusion and 20 minutes after reperfusion and were compared with a T2-weighted lesion on a 24-hour follow-up image. The authors found the MD lesion was significantly larger than the MK lesion during middle cerebral artery occlusion, and the MD lesion significantly decreased after reperfusion, while the MK lesion showed little change. Areas with concordant MD and MK signal abnormality show poor recovery on follow-up images. B W28 AJR:202, January 2014

4 Diffusion Kurtosis Imaging of the Brain These findings would suggest that inclusion of kurtosis data could complement traditional diffusion imaging to provide a more accurate model for infarction and aid in ischemic tissue characterization. Traumatic Brain Injury Considerable attention has been given to the use of imaging in TBI with an emphasis on recognizing mild TBI and the development of biomarkers to aid diagnosis, prognosis, and response to therapy. Advances have been made with DTI in that it has been found that decreased FA, presumably related to the disruption of white matter tracts, can indicate mild TBI in patients with otherwise normal findings at conventional MRI [6]. This evaluation, however, was limited to the examination of white matter because gray matter diffusion is close to isotropic. In a rat model of experimental TBI [7] increased MK was associated with increased glial activity and possibly with inflammation. Our preliminary findings in severe TBI in humans also showed distinct MK contrast compared with MD and FA maps (Fig. 3). The high MK values around the lesion may be indicative of severe astrogliosis with compact glial scar formation in the case of severe contusive trauma [8], which is not easily visible with conventional imaging (Fig. 5). In addition to possibly being a more sensitive tool for identifying brain injury, kurtosis imaging may prove helpful in predicting cognitive outcome among TBI patients. Grossman et al. [9] found that reduced MK is associated with loss of cellular structures. Furthermore, reduced MK within the thalamus in patients with mild TBI was associated with cognitive impairment on 1-year follow-up images, suggesting evaluation of this region may serve as an early predictor of brain damage [9]. Neoplasm The World Health Organization grading system for astrocytoma is based on tumor cellularity, mitosis, neovascularity, and necrosis. The prognosis and treatment of this heterogeneous group of tumors vary widely with the pathologic grade. To further complicate the matter, individual tumors are often quite heterogeneous in their makeup, and the ultimate pathologic grade is based on the region with the most aggressive features. Much work has been done to preoperatively differentiate low-grade and high-grade gliomas and to identify the most suspicious-appearing regions for biopsy or resection. Much of Fig year-old man with traumatic brain injury sustained in fall from roof (Glasgow coma score, 3t) who underwent MRI 2 days after injury. Diffusion kurtosis parameter maps (MK = mean kurtosis, FA = fractional anisotropy, MD = mean diffusion) and conventional MR images (FLAIR, T2-weighted, SWI = susceptibility weighted) at same axial location. Extremely high degrees of MK may indicate formation of glial scars around lesions (yellow arrows), which are otherwise not noticeable on conventional images or FA map. High MK regions are accompanied by restricted diffusion as indicated on MD map, but different degrees of contrast (red arrows) on MD and MK maps are evident. the focus has been on the enhancement pattern, perfusion data, and DTI parameters of lesions, but DKI offers an additional imaging biomarker for detecting microstructural differences within and between gliomas. Van Cauter et al. [10] prospectively compared diffusion imaging data, including MD, FA, MK, RK, and AK, of the solid components of primary brain tumors in 28 patients. They found significant differences in kurtosis parameters between high-grade and lowgrade gliomas. High-grade tumors had higher kurtosis values, presumably due to increased cellular density, decreased cell size, and increased complexity of the intracellular microenvironment. Van Cauter et al. wrote, Better separation was achieved with [kurtosis] parameters than with conventional diffusion imaging parameters. Mean kurtosis normalized to the value in the contralateral normal-appearing white matter showed the greatest differentiation between low- and high-grade lesions (100% sensitivity and 73% specificity). Improved performance of MK relative to FA is likely due to the reliance of FA on spatially oriented tissue structures. Raab et al. [11] differentiated low- and high-grade gliomas on the basis of their diffusion patterns. Those authors saw increased MK values with higher-grade gliomas. Normalized MK provided greater discriminatory power between low- and high-grade tumors than did apparent diffusion coefficient and FA (Fig. 6). Comparison of the kurtosis parameters of gliomas with those of other neoplastic and nonneoplastic lesions has yet to be reported. This is a potentially beneficial area of research that may lead to increased diagnostic confidence and decreased surgical intervention. Neurodegenerative Disease DKI is used in an attempt to measure the complexity of brain tissue to an extent beyond the resolution of conventional MRI techniques. It has been found [7] that increased microstructural complexity results from glial activity and reactive astrogliosis. It also has been suggested that neuronal loss can result in decreased kurtosis values. Therefore, it is reasonable to presume that DKI can potentially serve in the evaluation of the aging brain and as a surrogate marker for a variety of neurodegenerative disorders. Falangola et al. [12] compared diffusion and kurtosis imaging patterns in healthy volunteers and found distinct mean kurtosis patterns (in the prefrontal cortex) for patients AJR:202, January 2014 W29

5 Steven et al. Fig. 6 Grade 2 astrocytoma (AS 2), grade 3 astrocytoma (AS 3), and glioblastoma multiforme (GBM). Transverse T2-weighted MR images (T2), mean kurtosis (MK) parameter maps, and histograms show MK regions of interest (yellow outline) on T2-weighted images are downscaled to MK matrix of X-axis of histograms represents mean kurtosis values between 0 and 2 (unitless), and y-axis represents number of voxels per MK value within selected ROI. (Reprinted with permission of the Radiological Society of North America from [11]) of different age-ranges, with significant agerelated correlation for mean kurtosis and MK peak position. Wang et al. [13] investigated the use of DKI in Parkinson disease and found a statistically significant increase in MK in the putamen and substantia nigra in patients with Parkinson disease in comparison with a control group. Falangola et al. [14] also evaluated for regional changes in DKI-derived metrics in patients with mild cognitive impairment and Alzheimer disease in comparison with a control group. Gong et al. [15] performed similar investigations into the use of DKI in patients with mild cognitive impairment and Alzheimer disease. Both groups of authors found decreased regional MK and RK associated with disease. This may be due to loss of neuron cell bodies, synapses and dendrites and increased extracellular space [15]. Results of both of these studies were encouraging that kurtosis information may show early microstructural alterations in mild cognitive impairment and Alzheimer disease before morphologic changes are seen with conventional MRI techniques. Although expansion and validation of this work is required, DKI may serve as a biomarker for disease detection, facilitating the assessment of severity of cognitive deficit and the evaluation of disease progression. Fig. 7 Diagram shows 3D surfaces of exact (from simulation) and estimated orientation distribution functions (ODFs) for diffusion models with two (top row) and three (bottom row) equally contributing intersecting fibers. Directions of component fibers are shown by green lines. Fiber orientations are (θ1, ϕ1) = (50, 90 ) and (θ2, ϕ2) = (130, 90 ) for n = 2 and (θ1, ϕ1) = (60, 90 ), (θ2, ϕ2) = (120, 40 ), (θ3, ϕ3) = (120, 130 ) for n = 3. (Reprinted with permission from [23] Copyright 2005 Wiley-Liss, Inc.) A, Exact ODF. B, Diffusion kurtosis (DK) estimation of ODF. C, Exact ODF from nongaussian component in DK model (NG-ODF). D, DK estimation of NG-ODF. E, Q-ball estimation of ODF (minimum-maximum scaled) for b value of 4000 s/mm 2. F, Gaussian estimation of ODF. W30 AJR:202, January 2014

6 Diffusion Kurtosis Imaging of the Brain Demyelinating Diseases DWI and DTI have long been used for the evaluation of demyelinating diseases. There is strong directional dependence of water distribution within myelinated white matter tracts, and inflammation and demyelination tend to increase diffusivity and decrease directionality. Studies have focused on RD (diffusion perpendicular to the long axis) as a marker for disease. Although DTI with a moderate b value (1000 s/mm 2 ) is mostly sensitive to extracellular water diffusion, DKI with a stronger b value (2500 s/mm 2 ) has the potential to probe further into intracellular space and membrane interactions [16]. Results with multiple compartment diffusion models suggest that DKI may be more sensitive than diffusion coefficient to the changes in the water exchange rate between two compartments, which may be directly related to myelin integrity and intraaxonal-extraaxonal water exchange [17]. In the setting of demyelination, there should be increased intraaxonal-extraaxonal water exchange, which will have a greater effect on the diffusion kurtosis measure (RK) than the diffusion coefficient (RD). Yoshida et al. [18] investigated changes in kurtosis imaging parameters in patients with multiple sclerosis to determine whether DKI may serve as a more sensitive indicator of disease. Although the sample size was small, the group found decreased MK between multiple sclerosis patients and controls in normal-appearing white matter. Further evaluation with additional kurtosis parameters, including RK, may prove beneficial. Fiber Tracking The 3D modeling of white matter tracts, known as tractography, has become increasingly used in the clinical arena, most often in the presurgical assessment of patients with tumors or epilepsy [19]. The visualization of important functional tracts helps with operative planning with the goal of both minimizing postoperative morbidity and improving neurologic outcomes. DTI has allowed excellent spatial visualization of these tracts and improved understanding of the functional architecture within the brain. Tractography works by connecting, voxel by voxel, the direction of maximal diffusion. This is a robust technique when large bundles of myelinated fibers are oriented in the same direction; however, it becomes limited in complex areas of the brain because of the inability to resolve crossing fibers within a given voxel. The averaging of two strong anisotropic tensors results in a disk-shaped tensor with ambiguous directionality. Several techniques have been used to overcome this limitation, including modeling a 3D orientation distribution function (ODF) in lieu of the traditional simple diffusion tensor [20]. These techniques are typically referred to as higher angular resolution diffusion imaging, such as q- ball imaging (QBI) [21] and diffusion spectrum imaging [22], in which strong diffusion weightings (maximum b value, s/ mm 2 ) are typically used with large numbers of diffusion encoding directions (> 40). For diffusion spectrum imaging, multiple b values are needed to fill a q-space of diffusion weightings, leading to long imaging times (> 30 minutes). For QBI, only a single strong b value (3000 s/mm 2 ) is needed and hence is more clinically applicable. The disadvantage, however, is that owing to the stronger diffusion weighting used, QBI cannot provide clinically meaningful diffusion parameters (MD or FA), because the diffusion coefficient is b value dependent [16]. Lazar et al. [23] described an alternative approach to approximating the ODF that entails the nongaussian diffusion components in the DKI model and apparent improved resolution of two and three intersecting fibers in a given voxel. Figure 7 shows the 3D ODF surface for simulated two- to three-fiber crossing models. The ODF estimated with the nongaussian portion of the DK model (Fig. 7D), although not as cleanly delineated as the QBI approximation (Fig. 7E), provides excellent correspondence with the direction of the fibers and the peaks of the approximated ODF. Compared with QBI, DKI has the benefit that lower b values (< 2500 s/mm 2 ) are used, which provides better signal-to-noise ratio for diffusionweighted signal and adds both diffusion- and kurtosis-related parameters. Limitations There are a few limitations to DKI. The first is the relatively long image acquisition time compared with that for DTI: A minimum of two nonzero b values and at least 15 diffusion directions have to be acquired to estimate the diffusion and kurtosis tensor. The long image acquisition time increases susceptibility to patient motion and decreases throughput. However, clinically feasible imaging protocols (7 10 minutes) have been suggested for DKI [17, 24]. Furthermore, if only the mean kurtosis is of clinical interest, instead of the full tensors, then a fast DKI acquisition can be performed within 1 2 minutes [25]. This capability could make the clinical application of DKI highly feasible when only a quick assessment based on a MK map is needed, as analogous to DWI versus DTI. Another limitation of DKI is that the model is more complex (21 independent parameters) than DTI (six independent parameters). When a short imaging protocol is used (e.g., the 7-minute protocol), the DKI parameters can be variable, and the variability differs across brain regions [26], so attention has to be given to study design and statistical power evaluation in DKI studies; that is, the lack of significance may be due to high variability in the data. Furthermore, the exact meaning of MK, AK, and RK is still under investigation. More studies are required to further validate the emerging data and link changes in DKI parameters to pathologic findings. Summary of Literature Findings and Future Directions The literature findings are summarized in Table 1. Future directions will include more studies to link changes in DKI parameters to histologic findings and the underlying cellular changes, which is still lacking in the current literature. Interesting questions include the cellular basis for increased versus reduced MK and MK-MD mismatches. Lee et al. [27] have been attempting to address these questions through Monte Carlo simulation of diffusion-weighted signals, but experimental models with correlations of imaging data with histologic proof will be especially helpful. Most studies have focused only on the MK parameter, because one of the advantages of DKI is the added sensitivity of MK in tissues with relatively isotropic diffusion. However, RK, which captures the diffusion heterogeneity arising from axonal membranes and myelination, may potentially serve as a more sensitive marker in white matter. More studies are needed to link changes in RK directly to membrane and myelin integrity. Much of the work done thus far has been in small patient populations. More extensive studies are required to confirm the initial findings of these experiments. Correlation with alternative imaging modalities already in use will also be necessary to confirm the value and further the understanding of kurtosis imaging data. Summary Evaluation of the nongaussian diffusion characteristic in the brain is a new and promising diffusion technique that can be performed AJR:202, January 2014 W31

7 Steven et al. TABLE 1: Summary of Research Articles Reference Category Study Subjects Summary Hui et al. [4] Ischemia, infarction 44 patients with acute or subacute stroke Retrospective review comparing alteration of kurtosis and conventional diffusion imaging parameters relative to normal contralateral white matter. Found greater heterogeneity and percentage change in kurtosis metrics, which they attributed to alterations in the intraaxonal environment. Cheung et al. [5] Ischemia, infarction Rat model of MCA occlusion Prospective study comparing conventional diffusion, kurtosis, and perfusion parameters at multiple time points by use of a T2-weighted image at 24 h as the endpoint. Found diffusion-kurtosis mismatch recovery with reperfusion but poor recovery with concordant lesions. Zhuo et al. [7] TBI Rat model of controlled cortical impact injury Prospective study evaluating temporal changes in diffusion and kurtosis parameters after injury. Found increased MK in the contralateral cortex subacutely after injury while MD and FA appeared normal. The increase in MK was associated with increased reactive astrogliosis from immunohistochemistry analysis. Grossman et al. [9] TBI 22 mild TBI patients, 14 controls Study comparing kurtosis and diffusion tensor imaging parameters between patients with mild TBI and uninjured controls. Found alterations in kurtosis parameters in the thalamus and several white matter tracts and correlated cognitive impairment with MK in the thalamus and internal capsule. Van Cauter et al. [10] Neoplasm 28 patients with cerebral glioma Prospective analysis of the kurtosis and diffusion parameters of primary brain tumors. MK, RK, and AK were all increased in high-grade gliomas, but traditional diffusion measures MD and FA did not change significantly. Raab et al. [11] Neoplasm 34 patients with cerebral glioma Prospective analysis of the kurtosis and diffusion parameters of primary brain tumors. Found increased MK with higher-grade gliomas and that kurtosis afforded the greatest ability to discriminate between low- and high-grade gliomas. Falangola et al. [12] Neurodegenerative 24 controls ranging from 13 to 85 y old MD, FA, and MK were measured in the prefrontal brain cortex of healthy volunteers. Found distinct kurtosis patterns for different age ranges with changes in both MK and peak position. Findings varied between gray and white matter. Wang et al. [13] Neurodegenerative 30 PD patients, 30 controls Comparison of kurtosis and diffusion parameters in the basal ganglia of PD patients with those in healthy controls. Found increased MK in all major basal ganglia regions of PD patients, but the extent kurtosis was not related to disease severity. Falangola et al. [14] Neurodegenerative 13 AD patients, 13 MCI patients, 16 controls Gong et al. [15] Neurodegenerative 18 AD patients, 12 patients with MCI Comparison of kurtosis imaging data in multiple regions of interest between patients with mild cognitive impairment, AD, and healthy controls. Found evaluation of MK and RK in the anterior corona radiata could best differentiate MCI from normal findings, followed by prefrontal white matter. Evaluation of hippocampus was helpful for differentiating AD and MCI. Comparison of AD and MCI patients by use of kurtosis and conventional diffusion parameters in each lobe of the brain. Found decreased kurtosis in parietal and occipital lobes (gray and white matter) in AD patients. Yoshida et al. [18] Demyelinating 11 MS patients, 6 controls Evaluation of kurtosis and diffusion data in 24 ROIs in normal-appearing white matter comparing MS patients with controls. Found decreased average MK in MS patients with three ROIs noted to be statistically significant. Note MCA = middle cerebral artery, TBI = traumatic brain injury, MK = mean kurtosis, MD = mean diffusion, FA = fractional anisotropy, RK = radial kurtosis, AK = axial kurtosis, PD = Parkinson disease, AD = Alzheimer disease, MCI = mild cognitive impairment, MS = multiple sclerosis, ROIs = regions of interest. within a clinically feasible time frame. Adding imaging parameters to a traditional diffusion technique, including a minimum of 15 directions and an additional b value, allows measurement of kurtosis as a complement to the traditional DTI dataset. Kurtosis is a unitless measure of the nongaussian distribution of water, a unique and totally independent imaging metric. MK is believed to be generally proportional to the heterogeneity and complexity of the microstructure of the brain, where increased MK may indicate more densely packed cells or higher cellular complexity, and decreased MK may indicate loss of cellular structure. Furthermore, evaluation of RK may serve as a new method for examining the integrity of the cell membrane and surrounding myelin sheaths. Kurtosis imaging allows assessment of isotropic structures, including the cortex and basal ganglia, an important limitation of DTI. DKI can also improve estimation of the ODF for optimizing white matter fiber tracking and the resolution of crossing fibers. Although more W32 AJR:202, January 2014

8 Diffusion Kurtosis Imaging of the Brain studies are needed to further understand the importance of kurtosis measurements and to link changes in DKI parameters to histopathologic findings, the technique has potentially broad applications in the further characterization of neural tissues in a wide variety of disease processes. References 1. Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed 2010; 23: Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53: Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011; 65: Hui ES, Fieremans E, Jensen JH, et al. Stroke assessment with diffusional kurtosis imaging. Stroke 2012; 43: Cheung JS, Wang E, Lo EH, et al. Stratification of heterogeneous diffusion MRI ischemic lesion with kurtosis imaging. Stroke 2012; 43: Rutgers DR, Toulgoat F, Cazejust J, Fillard P, Lasjaunias P, Ducreux D. White matter abnormalities in mild traumatic brain injury: a diffusion tensor imaging study. AJNR 2008; 29: Zhuo J, Xu S, Proctor JL, et al. Diffusion kurtosis as an in vivo imaging marker for reactive astrogliosis in traumatic brain injury. Neuroimage 2012; 59: Sofroniew MV, Vinters HV. Astrocytes: biology and pathology. Acta Neuropathol 2010; 119: Grossman EJ, Ge Y, Jensen JH. Thalamus and cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study. J Neurotrauma 2012; 29: Van Cauter S, Veraart J, Sijbers J, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology 2012; 263: Raab P, Hattingen E, Frank K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 2010; 254: Falangola MF, Jensen JH, Babb JS, et al. Age-related non-gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging 2008; 28: Wang JJ, Lin WY, Lu CS, et al. Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology 2011; 261: Falangola MF, Jensen JH, Tabesh A, et al. Nongaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer s disease. Magn Reson Imaging 2013; 31: Gong NJ, Wong CS, Chan CC, Leung LM, Chu YC. Correlations between microstructural alterations and severity of cognitive deficiency in Alzheimer s disease and mild cognitive impairment: a diffusional kurtosis imaging study. Magn Reson Imaging 2013; 31: Le Bihan D. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 2013; 268: Jensen JH, Helpern JA. MRI quantification of non-gaussian water diffusion by kurtosis analysis. NMR Biomed 2010; 23: Yoshida M, Hori M, Yokoyama K, et al. Diffusional kurtosis imaging of normal-appearing white matter in multiple sclerosis: preliminary clinical experience. Jpn J Radiol 2013; 31: Nucifora PG, Verma R, Lee SK, Melhem ER. Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. Radiology 2007; 245: Tuch DS, Reese TG, Wiegell MR, Wedeen VJ. Diffusion MR of complex neural architecture. Neuron 2003; 40: Tuch DS. Q-ball imaging. Magn Reson Med 2004; 52: Wedeen VJ, Wang RP, Schmahmann JD, et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 2008; 41: Lazar M, Jensen JH, Xuan L, Helpern JA. Estimation of the orientation distribution function from diffusional kurtosis imaging. Magn Reson Med 2008; 60: Yan X, Zhou M, Ying L, et al. Evaluation of optimized b-value sampling schemas for diffusion kurtosis imaging with an application to stroke patient data. Comput Med Imaging Graph 2013; 37: Hansen B, Lund TE, Sangill R, Jespersen SN. Experimentally and computationally fast method for estimation of a mean kurtosis. Magn Reson Med 2013; 69: Szczepankiewicz F, Lätt J, Wirestam R, et al. Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation. Neuroimage 2013; 76: Lee CY, Bennett KM, Debbins JP. Sensitivities of statistical distribution model and diffusion kurtosis model in varying microstructural environments: a Monte Carlo study. J Magn Reson 2013; 230:19 26 AJR:202, January 2014 W33