Diffusion Anisotropy Indices Research Articles

Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging

Diffusion anisotropy in diffusion tensor imaging (DTI) is commonly quantified with normalized diffusion anisotropy indices (DAIs). Most often, the fractional anisotropy (FA) is used, but several alternative DAIs have been introduced in attempts to maximize the contrast-to-noise ratio (CNR) in diffusion anisotropy maps. Examples include the scaled relative anisotropy (sRA), the gamma variate anisotropy index (GV), the surface anisotropy (UAsurf), and the lattice index (LI). With the advent of multidimensional diffusion encoding it became possible to determine the presence of microscopic diffusion anisotropy in a voxel, which is theoretically independent of orientation coherence. In accordance with DTI, the microscopic anisotropy is typically quantified by the microscopic fractional anisotropy (μFA).In this work, in addition to the μFA, the four microscopic diffusion anisotropy indices (μDAIs) μsRA, μGV, μUAsurf, and μLI are defined in analogy to the respective DAIs by means of the average diffusion tensor and the covariance tensor. Simulations with three representative distributions of microscopic diffusion tensors revealed distinct CNR differences when differentiating between isotropic and microscopically anisotropic diffusion. q-Space trajectory imaging (QTI) was employed to acquire brain in-vivo maps of all indices. For this purpose, a 15min protocol featuring linear, planar, and spherical tensor encoding was used. The resulting maps were of good quality and exhibited different contrasts, e.g. between gray and white matter. This indicates that it may be beneficial to use more than one μDAI in future investigational studies.

A diffusion tensor imaging study of brain microstructural changes related to religion and spirituality in families at high risk for depression.

IntroductionPreviously in a three‐generation study of families at high risk for depression, we found that belief in the importance of religion/spirituality (R/S) was associated with thicker cortex in bilateral parietal and occipital regions. In the same sample using functional magnetic resonance imaging and electroencephalograph (EEG), we found that offspring at high familial risk had thinner cortices, increased default mode network connectivity, and reduced EEG power. These group differences were significantly diminished in offspring at high risk who reported high importance of R/S beliefs, suggesting a protective effect.MethodsThis study extends previous work examining brain microstructural differences associated with risk for major depressive disorder (MDD) and tests whether these are normalized in at‐risk offspring who report high importance of R/S beliefs. Diffusion tensor imaging (DTI) data were selected from 99 2nd and 3rd generation offspring of 1st generation depressed (high‐risk, HR) or nondepressed (low‐risk, LR) parents. Whole‐brain and region‐of‐interest analyses were performed, using ellipsoidal area ratio (EAR, an alternative diffusion anisotropy index comparable to fractional anisotropy). We examined microstructural differences associated with familial risk for depression within the groups of high and low importance of R/S beliefs (HI, LI).ResultsIn the LI group, HR individuals showed significantly decreased EAR in white matter regions neighboring the precuneus, superior parietal lobe, superior and middle frontal gyrus, and bilateral insula, supplementary motor area, and postcentral gyrus. In the HI group, HR individuals showed reduced EAR in white matter surrounding the left superior, and middle frontal gyrus, left superior parietal lobule, and right supplementary motor area. Microstructural differences associated with familial risk for depression in precuneus, frontal lobe, and temporal lobe were nonsignificant or less significant in the HI group.ConclusionR/S beliefs may affect microstructure in brain regions associated with R/S, potentially conferring resilience to depression among HR individuals.

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

Diffusion basis spectrum imaging detects and distinguishes coexisting subclinical inflammation, demyelination and axonal injury in experimental autoimmune encephalomyelitis mice.

Clinicopathological paradox has hampered significantly the effective assessment of the efficacy of therapeutic intervention for multiple sclerosis. Neuroimaging biomarkers of tissue injury could guide more effective treatment by accurately reflecting the underlying subclinical pathologies. Diffusion tensor imaging-derived directional diffusivity and anisotropy indices have been applied to characterize white matter disorders. However, these biomarkers are sometimes confounded by the complex pathologies seen in multiple sclerosis and its animal models. Recently, a novel technique of diffusion basis spectrum imaging has been developed to quantitatively assess axonal injury, demyelination and inflammation in a mouse model of inflammatory demyelination. Lenaldekar, which inhibits T-cell expansion in a non-cytolytic manner, has been shown to suppress relapses and preserve white matter integrity in mice with experimental autoimmune encephalomyelitis. In this study, relapsing-remitting experimental autoimmune encephalomyelitis was induced through active immunization of SJL/J mice with a myelin proteolipid protein peptide. The therapeutic efficacy of Lenaldekar treatment was evaluated via daily clinical score, cross-sectional ex vivo diffusion basis spectrum imaging examination and histological analysis. Lenaldekar greatly reduced relapse severity and protected white matter integrity in these experimental autoimmune encephalomyelitis mice. Diffusion basis spectrum imaging-derived axial diffusivity, radial diffusivity and restricted diffusion tensor fraction accurately reflected axonal injury, myelin integrity and inflammation-associated cellularity change, respectively. These results support the potential use of diffusion basis spectrum imaging as an effective outcome measure for preclinical drug evaluation.

Age-related decline in white matter integrity in a mouse model of tauopathy: an in vivo diffusion tensor magnetic resonance imaging study

Elevated expression of human hyperphosphorylated tau is associated with neuronal loss and white matter (WM) pathology in Alzheimer's disease (AD) and related neurodegenerative disorders. Using in vivo diffusion tensor magnetic resonance imaging (DT-MRI) at 11.1 Tesla we measured age-related alterations in WM diffusion anisotropy indices in a mouse model of human tauopathy (rTg4510) and nontransgenic (nonTg) control mice at the age of 2.5, 4.5, and 8 months. Similar to previous DT-MRI studies in AD subjects, 8-month-old rTg4510 mice showed lower fractional anisotropy (FA) values in WM structures than nonTg. The low WM FA in rTg4510 mice was observed in the genu and splenium of the corpus callosum, anterior commissure, fimbria, and internal capsule and was associated with a higher radial diffusivity than nonTg. Interestingly, rTg4510 mice showed lower estimates for the mode of anisotropy than controls at 2.5 months suggesting that changes in this diffusivity metric are detectable at an early stage preceding severe tauopathy. Immunogold electron microscopy partly supports our diffusion tensor imaging findings. At the age of 4 months, rTg4510 mice show axonal tau inclusions and unmyelinated processes. At later ages (12 months and 14 months) we observed inclusions in myelin sheath, axons, and unmyelinated processes, and a “disorganized” pattern of myelinated fiber arrangement with enlarged inter-axonal spaces in rTg4510 but not in nonTg mice. Our data support a role for the progression of tau pathology in reduced WM integrity measured by DT-MRI. Further in vivo DT-MRI studies in the rTg4510 mouse should help better discern the detailed mechanisms of reduced FA and anisotropy mode, and the specific role of tau during neurodegeneration.

A new approach to depict anisotropy diffusion of water molecule in vivo

通过核磁共振扩散张量成像(DTI)得到的特定值域的扩散各向异性指数(DAI) 可用于揭示水分子扩散椭球的形态学特征, 定量反映被成像物体内部水分子扩散的优势方向和强度, 间接得到被成像物体内部的组织结构信息. DAI的可靠性直接影响对DTI数据的分析和理解. 本文基于扩散张量椭球的几何学信息, 提出利用扩散椭球几何比(EGR)定量描述水分子扩散的各向异性程度. 通过蒙特卡罗模拟实验和对人脑DTI数据进行分析, 并与当前广泛应用的水分子扩散各向异性分数(FA)和近期文献提出的扩散椭球面积比(EAR)进行对比. 实验发现EGR在不同级别噪声影响下的对比度效果和抗噪性都优于FA及EAR. 而且EGR 加入了体积修正, 增强了盘形扩散张量情况下的敏感性, 能够更好地鉴别神经纤维束交叉情况, 对于各向异性扩散程度较高的白质深层和相对均质的表层都有较好的量化区分结果.

Symmetric positive semi-definite Cartesian Tensor fiber orientation distributions (CT-FOD)

A novel method for estimating a field of fiber orientation distribution (FOD) based on signal de-convolution from a given set of diffusion weighted magnetic resonance (DW-MR) images is presented. We model the FOD by higher order Cartesian tensor basis using a parametrization that explicitly enforces the positive semi-definite property to the computed FOD. The computed Cartesian tensors, dubbed Cartesian Tensor-FOD (CT-FOD), are symmetric positive semi-definite tensors whose coefficients can be efficiently estimated by solving a linear system with non-negative constraints. Next, we show how to use our method for converting higher-order diffusion tensors to CT-FODs, which is an essential task since the maxima of higher-order tensors do not correspond to the underlying fiber orientations. Finally, we propose a diffusion anisotropy index computed directly from CT-FODs using higher order tensor distance measures thus consolidating the whole analysis pipeline of diffusion imaging solely using CT-FODs. We evaluate our method qualitatively and quantitatively using simulated DW-MR images, phantom images, and human brain real dataset. The results conclusively demonstrate the superiority of the proposed technique over several existing multi-fiber reconstruction methods.

TH‐D‐201C‐03: Correlation between MR Diffusion Tensor Imaging Derived Dimensional Ellipsoid Ratio as a Function of Tissue Anisotropy and Multiple Sclerosis Induced Variation

Purpose: The diffusion fractional ellipsoids derived in ratios ((XMS −XNC)/XNC) are compared from both the Monte Carlo simulation and the deviated diffusion profile between normal brains and Secondary Progressive Multiple Sclerosis (SPMS) patients. We construct a map to evaluate the water diffusion in (1) standard sorted (2) non‐sorted and (3) normalized space of directional diffusivity and diffusion anisotropy indices. Method and Materials: The sensitivity coefficients are derived analytically between the anisotropy indices and diffusivities in the symmetric prolate λ1>λ2=λ3 and oblate λ1<λ2=λ3 diffusion and non‐symmetric geometry. The contrast‐to‐scatter ratios are calculated from the Monte Carlo simulation conducted in Matlab (MathWorks Natick MA) in addition to the mean contrast comparison of diffusion tensor derived parameter from white and gray matter of a SPMS study acquired in GE Signa 1.5T scanner with 21 gradient weighted direction scheme TR/TE=10800/80ms FOV=24cm voxel size=1×1×3 mm3 matrix size = 128×128 and b = 1000 s mm−2. Statistical comparison was performed in SPSS (SPSS Inc Chicago Illinois) to determine the significant Pearson correlation and difference of both within and across diseased and normal fiber bundles. Results: The sensitivity of the diffusion anisotropy indices as expressing from a ratio of standard deviation to magnitude/average is higher than the diffusivities in low anisotropy region. The diffusion ellipsoid of low anisotropy tissue is observed to be prolonged as SNR<20. The contrast‐to‐scatter ratio of FA decreases slightly with increasing anisotropy for SNR>20. Conclusion: Sensitivity analysis is demonstrated as a standard to derive the dimensional ellipsoid ratio as a function of anisotropy level and tissue type. A multiple sclerosis induced oblate ratio ellipsoid was observed in both of the genu and splenium corpus callosum while prolate ratio was found in the internal capsule and thalamus. In contrast both the radial and axial diffusivity decrease in the caudate nucleus.

Validation of the anisotropy index ellipsoidal area ratio in diffusion tensor imaging

A new diffusion anisotropy index, ellipsoidal area ratio (EAR), was described recently and proved to be less noise-sensitive than fractional anisotropy (FA) by theory and simulation. Here we show that EAR has higher signal-to-noise ratios than FA in average diffusion tensor imaging data from 40 normal subjects. EAR was also more sensitive than FA in detecting white matter abnormalities in a patient with widespread diffuse axonal injury. Monte Carlo simulation showed that EAR's mean values are more biased by noise than FA when anisotropy is small, both for single fiber tracts and when fiber tracts cross. However, the improved signal-to-noise ratio of EAR relative to FA suggests that EAR may be a superior measure of anisotropy both in quantifying both deep white matter with relatively uniform fiber tracts and pericortical white matter structure with relatively low anisotropy and fiber crossings.

How does angular resolution affect diffusion imaging measures?

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6≤N≤94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9.13 for GA and tGA, 10.57 for FA, 9.17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.

Diffusion Tensor Imaging for the Differentiation of Microangiopathy, Infarction and Perfusion-Diffusion Mismatch Lesions

Purpose: This study was designed to evaluate the usefulness of diffusion tensor imaging (DTI) and the DTI indices for differentiating between microangiopathy lesions, acute infarction lesions and perfusion-diffusion mismatch areas. Materials and Methods: DTI was performed in 35 patients with the use of a 1.5 Tesla MRI system. The MRI parameters were as follows: a spin echo EPI sequence with a bvalue = 1000 s/mm 2 , 25 diffusion directions, a repetition time of 8400 msec, an echo time of 75 msec, a matrix size of 128 128, a FOV of 22 cm and a 4 mm slice thickness. From the diffusion tensor images, the apparent diffusion coefficient (ADC), fractional anisotropy (FA), volume ratio (VR), relative anisotropy (RA), anisotropy index (AI), exponential ADC (eADC) and magnitude diffusion coefficient (MDC) were measured for the contra-lateral normal area (28 cases), the microangiopathy lesions (10 cases), the infarction lesions (17 cases) and the perfusion-diffusion mismatch area (8 cases). Results: As compared to the normal area, the microangiopathy lesions showed increased ADC and MDC values and decreased FA, VR, RA, AI and eADC values. The infarction lesions showed increased VR, RA and eADC values, a normal FA, a decreased AI and decreased ADC and MDC values. The mismatch area showed a similar pattern as that for the microangiopathy lesions; however, the differences were not prominent, with an increase of the ADC and MDC values and a decrease of FA, VR, RA, AI and eADC values. Conclusion: The DTI indices could have a role in making the differential diagnosis of microangiopathy, acute infarction and perfusion-diffusion mismatch lesions.

The ellipsoidal area ratio: an alternative anisotropy index for diffusion tensor imaging.

In the processing and analysis of diffusion tensor imaging (DTI) data, certain predefined morphological features of diffusion tensors are often represented as simplified scalar indices, termed diffusion anisotropy indices (DAIs). When comparing tensor morphologies across differing voxels of an image, or across corresponding voxels in different images, DAIs are mathematically and statistically more tractable than are the full tensors, which are probabilistic ellipsoids consisting of three orthogonal vectors that each has a direction and an associated scalar magnitude. We have developed a new DAI, the "ellipsoidal area ratio" (EAR), to represent the degree of anisotropy in the morphological features of a diffusion tensor. The EAR is a normalized geometrical measure of surface curvature in the 3D diffusion ellipsoid. Monte Carlo simulations and applications to the study of in vivo human data demonstrate that, at low noise levels, EAR provides a similar contrast-to-noise ratio (CNR) but a higher signal-to-noise ratio (SNR) than does fractional anisotropy (FA), which is currently the most popular anisotropy index in active use. Moreover, at the high noise levels encountered most commonly in real-world DTI datasets, EAR compared with FA is consistently much more robust to perturbations from noise and it provides a higher CNR, features useful for the analysis of DTI data that are inherently noise sensitive.

Assessing optic nerve pathology with diffusion MRI: from mouse to human

The optic nerve is often affected in patients with glaucoma and multiple sclerosis. Conventional MRI can detect nerve damage, but it does not accurately assess the underlying pathologies. Mean diffusivity and diffusion anisotropy indices derived from diffusion tensor imaging have been shown to be sensitive to a variety of central nervous system white matter pathologies. Despite being sensitive, the lack of specificity limits the ability of these measures to differentiate the underlying pathology. Directional (axial and radial) diffusivities, measuring water diffusion parallel and perpendicular to the axonal tracts, have been shown to be specific to axonal and myelin damage in mouse models of optic nerve injury, including retinal ischemia and experimental autoimmune encephalomyelitis. The progression of Wallerian degeneration has also been detected using directional diffusivities after retinal ischemia. However, translating these findings to human optic nerve is technically challenging. The current status of diffusion MRI of human optic nerve, including imaging sequences and protocols, is summarized herein. Despite the lack of a consensus among different groups on the optimal sequence or protocol, increased mean diffusivity and decreased diffusion anisotropy have been observed in injured optic nerve from patients with chronic optic neuritis. From different mouse models of optic nerve injuries to the emerging studies on patients with optic neuritis, directional diffusivities show great potential to be specific biomarkers for axonal and myelin injury.

Novel diffusion anisotropy indices: An evaluation

To systematically evaluate diffusion anisotropy (DA) using newly defined indices based on the diffusion deviation and mean diffusivity approach. Measures of amplitude, area, and volume of the DA index (DAI) were measured and compared with regard to their sensitivity to changes in DA, susceptibility to noise in the original diffusion-weighted (DW) images, and contrast-to-noise ratio (CNR) in homogenous regions. Simulations were performed under different levels of noise and DA. Human DTI data were acquired from eight normal volunteers. Indices of area and volume measures provided improved resolution for characterizing the DA compared to the eigenvalue ratio. The amplitude measure showed consistent performances with good CNR and less susceptibility to noise in the original data. These indices are rotationally invariant without the requirement of eigenvalue sorting. At low anisotropy, all indices have a similar CNR. For larger DA, the first index (the deviation tensor divided by the DT) shows improved sensitivity, contrast-to-noise ratio (CNR), and noise immunity compared to the other indices.

Retrospective measurement of the diffusion tensor eigenvalues from diffusion anisotropy and mean diffusivity in DTI

A simple theoretical framework to compute the eigenvalues of a cylindrically symmetric prolate diffusion tensor (D) from one of the rotationally-invariant diffusion anisotropy indices and average diffusivity is presented and validated. Cylindrical or axial symmetry assumes a prolate ellipsoid shape (lambdaparallel=lambda1>lambdaperpendicular=(lambda2+lambda3)/2; lambda2=lambda3). A prolate ellipsoid with such symmetry is largely satisfied in a number of white matter (WM) structures, such as the spinal cord, corpus callosum, internal capsule, and corticospinal tract. The theoretical model presented is validated using in vivo DTI measurements of rat spinal cord and human brain, where eigenvalues were calculated from both the set of diffusion coefficients and a tensor analysis. This method was used to retrospectively analyze literature data that reported tensor-derived average diffusivity, anisotropy, and eigenvalues, and similar eigenvalue measurements were obtained. The method provides a means to retrospectively reanalyze literature data that do not report eigenvalues. Other potential applications of this method are also discussed.

Contrast‐to‐noise ratios of diffusion anisotropy indices

The degree of diffusion anisotropy in different brain regions is usually measured by a diffusion anisotropy index (DAI) such as relative anisotropy (RA) and fractional anisotropy (FA). FA has been reported to have a higher contrast-to-noise ratio (CNR) than RA. The present work compares the CNRs of seven DAIs in theoretical propagation-of-error calculations, in simulations, and in human brain measurements over small and large anisotropy differences. In simulations all seven CNRs were similar for small anisotropy differences. Small differences among the DAIs appeared at higher anisotropy levels and lower signal-to-noise ratios with certain tensor orientations. The DAIs fell into three groups based on algebraic relationships and small CNR differences. The group with RA and FA had the best CNR. Human brain regions with small anisotropy differences had similar CNR for all seven DAIs, and the scatter in the data was greater than any expected differences. With large anisotropy differences, a small advantage appeared for RA over FA in some simulations and for FA over RA in other simulations. The CNR between brain regions with very different anisotropies was different for each DAI. The apparent reported advantage of FA over RA is explained by biologic heterogeneity and by noise-induced bias in the DAI values and their standard deviations.

“Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data

New diffusion-weighted imaging (DWI) methods, including high-b, q-space, and high angular resolution MRI methods, attempt to extract information about non-Gaussian diffusion in tissue that is not provided by low-b-value (b approximately 1000 s mm(-2)) diffusion or diffusion tensor magnetic resonance imaging (DT-MRI). Additionally, DWI data with higher spatial resolution are being acquired to resolve fine anatomic structures, such as white matter fasciculi. Increasing diffusion-weighting or decreasing voxel size can reduce the signal-to-noise ratio so that some DWI signals are close to the background noise level. Here we report several new artifacts that can be explained by considering how background noise affects the peanut-shaped angular apparent diffusion coefficient (ADC) profile. These include an orientationally dependent deviation from Gaussian behavior of the ADC profile, an underestimation of indices of diffusion anisotropy, and a correlation between estimates of mean diffusivity and diffusion anisotropy. We also discuss how noise can cause increased gray/white matter DWI contrast at higher b values and an apparent elevation of diffusion anisotropy in acute ischemia. Importantly, all of these artifacts are negligible in the b-value range typically used in DT-MRI of brain (b approximately 1000 s mm(-2)). Finally, we demonstrate a strategy for ameliorating the rectified noise artifact in data collected at higher b values.

Comparison of single-shot echo-planar and line scan protocols for diffusion tensor imaging 1

Rationale and Objectives Both single-shot diffusion-weighted echo-planar imaging (EPI) and line scan diffusion imaging (LSDI) can be used to obtain magnetic resonance diffusion tensor data and to calculate directionally invariant diffusion anisotropy indices, ie, indirect measures of the organization and coherence of white matter fibers in the brain. To date, there has been no comparison of EPI and LSDI. Because EPI is the most commonly used technique for acquiring diffusion tensor data, it is important to understand the limitations and advantages of LSDI relative to EPI. Materials and methods Five healthy volunteers underwent EPI and LSDI diffusion on a 1.5 Tesla magnet (General Electric Medical Systems, Milwaukee, WI). Four-mm thick coronal sections, covering the entire brain, were obtained. In addition, one subject was tested with both sequences over four sessions. For each image voxel, eigenvectors and eigenvalues of the diffusion tensor were calculated, and fractional anisotropy (FA) was derived. Several regions of interest were delineated, and for each, mean FA and estimated mean standard deviation were calculated and compared. Results Results showed no significant differences between EPI and LSDI for mean FA for the five subjects. When intersession reproducibility for one subject was evaluated, there was a significant difference between EPI and LSDI in FA for the corpus callosum and the right uncinate fasciculus. Moreover, errors associated with each FA measure were larger for EPI than for LSDI. Conclusion Results indicate that both EPI- and LSDI-derived FA measures are sufficiently robust. However, when higher accuracy is needed, LSDI provides smaller error and smaller inter-subject and inter-session variability than EPI.

Eddy current correction in diffusion‐weighted imaging using pairs of images acquired with opposite diffusion gradient polarity

In echo-planar-based diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), the evaluation of diffusion parameters such as apparent diffusion coefficients and anisotropy indices is affected by image distortions that arise from residual eddy currents produced by the diffusion-sensitizing gradients. Correction methods that coregister diffusion-weighted and non-diffusion-weighted images suffer from the different contrast properties inherent in these image types. Here, a postprocessing correction scheme is introduced that makes use of the inverse characteristics of distortions generated by gradients with reversed polarity. In this approach, only diffusion-weighted images with identical contrast are included for correction. That is, non-diffusion-weighted images are not needed as a reference for registration. Furthermore, the acquisition of an additional dataset with moderate diffusion-weighting as suggested by Haselgrove and Moore (Magn Reson Med 1996;36:960-964) is not required. With phantom data it is shown that the theoretically expected symmetry of distortions is preserved in the images to a very high degree, demonstrating the practicality of the new method. Results from human brain images are also presented.