MRI quantification of non-Gaussian water diffusion by kurtosis analysis

Neuromyelitis optica: a diffusional kurtosis imaging study

BACKGROUND AND PURPOSE

Conventional MR imaging typically yields normal images of the brain or indicates lesions in areas of high aquaporin expression in patients with neuromyelitis optica. Diffusional kurtosis imaging was applied in patients with neuromyelitis optica to determine whether this technique could detect alterations in diffusion and diffusional kurtosis parameters in normal-appearing white matter and to explore the relationship between diffusional kurtosis imaging and DTI parameters.

MATERIALS AND METHODS

Thirteen patients with neuromyelitis optica and 13 healthy controls underwent MR imaging of the brain with conventional and diffusional kurtosis imaging sequences. Tract-based spatial statistics and region-of-interest-based analyses were conducted to identify differences between patients with neuromyelitis optica and controls through conventional DTI and diffusional kurtosis imaging parameters. The parameters were correlated to determine the potential relationship between them.

RESULTS

Compared with healthy controls, several diffusional kurtosis imaging and DTI parameters were altered in various fiber tracts of patients with neuromyelitis optica (P < .05). A significant decrease (P < .05) in radial kurtosis was observed in the corpus callosum and anterior corona radiata and left optic radiation. Differences (P < .1) in mean kurtosis were found in patients with neuromyelitis optica. We found a negative correlation between diffusional kurtosis imaging (radial kurtosis, axial kurtosis, mean kurtosis) and the corresponding DTI parameters (radial diffusivity, axial diffusivity, mean diffusivity). Positive correlations were found for radial kurtosis and mean kurtosis with fractional anisotropy.

CONCLUSIONS

This study demonstrated differences in conventional diffusion and diffusional kurtosis parameters, especially radial kurtosis, in the normal-appearing white matter of patients with neuromyelitis optica compared with healthy controls. Larger studies of patients with neuromyelitis optica should be performed to assess the potential clinical impact of these findings.

In vivo assessment of white matter damage in neuromyelitis optica: a diffusion tensor and diffusion kurtosis MR imaging study

BACKGROUND AND PURPOSE

In patients with neuromyelitis optica (NMO), damage to extensive regions of normal-appearing WM has been observed. To investigate the possibility that microstructural alterations are present in these WM tracts, DTI and diffusion kurtosis imaging (DKI) techniques were applied and compared.

MATERIAL AND METHODS

Thirteen patients with NMO and 13 demographically and gender-matched controls underwent MRI using a 3T MR scanner, with DTI/DKI sequence acquired jointly fitted. Parametric fractional anisotropy maps were derived from diffusion tensor (FADTI) values using b-values of 0s/mm(2) and 1000s/mm(2). Parametric fractional anisotropy maps derived from diffusion kurtosis tensor (FADKI) values were also acquired using b-values of 0, 1000, and 2000s/mm(2). Mean FADTI and FADKI values were also calculated. A ROI analysis of the genu and splenium of the corpus callosum (CC), cerebral peduncle (CP), and optic radiation (OR) was also performed. Student's t-test and corrections for multiple comparisons were used to evaluate the data obtained.

RESULTS

A significant decrease in the FADTI values obtained for NMO patients versus controls was observed for the splenium of the CC and the left OR (p<0.05). However, just a positive trend was observed for the FADKI values associated with the same WM tracts.

CONCLUSIONS

To our knowledge, this is the first study to analyze WM tracts of NMO patients using DTI and DKI. These data indicate that DKI could have limitations in evaluating the WM integrity in NMO patients. Furthermore, the results obtained are consistent with the hypothesis that diffuse brain involvement characterizes NMO.

Diffusion tensor imaging for anatomical localization of cranial nerves and cranial nerve nuclei in pontine lesions: initial experiences with 3T-MRI

With continuous refinement of neurosurgical techniques and higher resolution in neuroimaging, the management of pontine lesions is constantly improving. Among pontine structures with vital functions that are at risk of being damaged by surgical manipulation, cranial nerves (CN) and cranial nerve nuclei (CNN) such as CN V, VI, and VII are critical. Pre-operative localization of the intrapontine course of CN and CNN should be beneficial for surgical outcomes. Our objective was to accurately localize CN and CNN in patients with intra-axial lesions in the pons using diffusion tensor imaging (DTI) and estimate its input in surgical planning for avoiding unintended loss of their function during surgery. DTI of the pons obtained pre-operatively on a 3Tesla MR scanner was analyzed prospectively for the accurate localization of CN and CNN V, VI and VII in seven patients with intra-axial lesions in the pons. Anatomical sections in the pons were used to estimate abnormalities on color-coded fractional anisotropy maps. Imaging abnormalities were correlated with CN symptoms before and after surgery. The course of CN and the area of CNN were identified using DTI pre- and post-operatively. Clinical associations between post-operative improvements and the corresponding CN area of the pons were demonstrated. Our results suggest that pre- and post-operative DTI allows identification of key anatomical structures in the pons and enables estimation of their involvement by pathology. It may predict clinical outcome and help us to better understand the involvement of the intrinsic anatomy by pathological processes.

A fast schema for parameter estimation in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a new model in magnetic resonance imaging (MRI) characterizing restricted diffusion of water molecules in living tissues. We propose a method for fast estimation of the DKI parameters. These parameters - apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC) - are evaluated using an alternative iteration schema (AIS). This schema first roughly estimates a pair of ADC and AKC values from a subset of the DKI data acquired at 3 b-values. It then iteratively and alternately updates the ADC and AKC until they are converged. This approach employs the technique of linear least square fitting to minimize estimation error in each iteration. In addition to the common physical and biological constrains that set the upper and lower boundaries of the ADC and AKC values, we use a smoothing procedure to ensure that estimation is robust. Quantitative comparisons between our AIS methods and the conventional methods of unconstrained nonlinear least square (UNLS) using both synthetic and real data showed that our unconstrained AIS method can significantly accelerate the estimation procedure without compromising its accuracy, with the computational time for a DKI dataset successfully reduced to only 1 or 2min. Moreover, the incorporation of the smoothing procedure using one of our AIS methods can significantly enhance the contrast of AKC maps and greatly improve the visibility of details in fine structures.

A simple and robust test object for the assessment of isotropic diffusion kurtosis

PURPOSE

To create a robust test object for the assessment of isotropic diffusion kurtosis and to investigate the relationships between barrier concentration and kurtosis and diffusion coefficients.

THEORY AND METHODS

Diffusion kurtosis imaging is an extension of conventional diffusion-weighted magnetic resonance imaging which provides a means of assessing the degree to which diffusion processes of spin-bearing particles are non-Gaussian, a property that is quantified by the kurtosis. We present a set of test objects, each possessing a different concentration of colloidal dispersion, allowing barrier concentration of the dispersed colloidal particles to be related to the kurtosis of the water diffusion. Diffusion coefficients from the kurtosis model and the monoexponential model are compared.

RESULTS

A relationship between barrier concentration and kurtosis is found, demonstrating that the diffusion process becomes less Gaussian as the barrier concentration is increased. Differences in the two estimates for the diffusion coefficients are examined. The test object is robust, displaying long-term reproducibility of results.

CONCLUSIONS

Colloidal dispersions provide a suitable and stable test object for the assessment and reproducibility measurements of kurtosis.

Traumatic white matter injury and glial activation: from basic science to clinics

An improved understanding and characterization of glial activation and its relationship with white matter injury will likely serve as a novel treatment target to curb post injury inflammation and promote axonal remyelination after brain trauma. Traumatic brain injury (TBI) is a significant public healthcare burden and a leading cause of death and disability in the United States. Particularly, traumatic white matter (WM) injury or traumatic axonal injury has been reported as being associated with patients' poor outcomes. However, there is very limited data reporting the importance of glial activation after TBI and its interaction with WM injury. This article presents a systematic review of traumatic WM injury and the associated glial activation, from basic science to clinical diagnosis and prognosis, from advanced neuroimaging perspective. It concludes that there is a disconnection between WM injury research and the essential role of glia which serve to restore a healthy environment for axonal regeneration following WM injury. Particularly, there is a significant lack of non-invasive means to characterize the complex pathophysiology of WM injury and glial activation in both animal models and in humans. An improved understanding and characterization of the relationship between glia and WM injury will likely serve as a novel treatment target to curb post injury inflammation and promote axonal remyelination.

Fast and robust estimation of diffusional kurtosis imaging (DKI) parameters by general closed-form expressions and their extensions

Diffusional kurtosis imaging (DKI) for clinical imaging involves time-consuming computation and demonstrates low robustness. Standard estimation of DKI parameters is based on an extension of Stejskal-Tanner's signal model with squared b-value term and is a least-squares fitting problem. The use of numerical methods for computation requires time, and estimation of DKI parameters is noise sensitive and often produces noisy results, such as images with pepper noise.In this study, we propose general closed-form solutions for DKI parameters to avoid numerical computation for least-squares fitting, solutions that can be applied to diffusion weighted imaging (DWI) datasets with any number of b-values more than three. Solutions are obtained through stationary-point conditions of an objective function that are minimized for fitting. We use 3 techniques to extend the solutions to increase robustness-b-value-dependent weighting in fitting, removal of outliers, and addition of neighbor sampling. Based on synthetic datasets and clinical datasets that both consist of 6 b-value and 3 b-value datasets, we detail and compare the 3 methods including a method by Jensen et al. are compared and investigated in detail. The synthetic data consist of several combinations of DKI parameters and some Rician noise. In addition to visually assessing result images, we also performed quantitative evaluation using a range of estimated parameters, positive-definiteness of the objective function for fitting, and root-mean-square error including estimation bias from the true value (synthetic data only). Methods that added neighbor sampling outperformed others in terms of low errors and visual smoothness. Though the solution by our method is to estimate DKI parameters in a single MPG direction, it can contribute to anisotropic analysis of diffusional kurtosis such as kurtosis tensor. More robust estimation is expected by combining techniques.

Can diffusion kurtosis imaging improve the sensitivity and specificity of detecting microstructural alterations in brain tissue chronically after experimental stroke? Comparisons with diffusion tensor imaging and histology

Imaging techniques that provide detailed insights into structural tissue changes after stroke can vitalize development of treatment strategies and diagnosis of disease. Diffusion-weighted MRI has been playing an important role in this regard. Diffusion kurtosis imaging (DKI), a recent addition to this repertoire, has opened up further possibilities in extending our knowledge about structural tissue changes related to injury as well as plasticity. In this study we sought to discern the microstructural alterations characterized by changes in diffusion tensor imaging (DTI) and DKI parameters at a chronic time point after experimental stroke. Of particular interest was the question of whether DKI parameters provide additional information in comparison to DTI parameters in understanding structural tissue changes, and if so, what their histological origins could be. Region-of-interest analysis and a data-driven approach to identify tissue abnormality were adopted to compare DTI- and DKI-based parameters in post mortem rat brain tissue, which were compared against immunohistochemistry of various cellular characteristics. The unilateral infarcted area encompassed the ventrolateral cortex and the lateral striatum. Results from region-of-interest analysis in the lesion borderzone and contralateral tissue revealed significant differences in DTI and DKI parameters between ipsi- and contralateral sensorimotor cortex, corpus callosum, internal capsule and striatum. This was reflected by a significant reduction in ipsilateral mean diffusivity (MD) and fractional anisotropy (FA) values, accompanied by significant increases in kurtosis parameters in these regions. Data-driven analysis to identify tissue abnormality revealed that the use of kurtosis-based parameters improved the detection of tissue changes in comparison with FA and MD, both in terms of dynamic range and in being able to detect changes to which DTI parameters were insensitive. This was observed in gray as well as white matter. Comparison against immunohistochemical stainings divulged no straightforward correlation between diffusion-based parameters and individual neuronal, glial or inflammatory tissue features. Our study demonstrates that DKI allows sensitive detection of structural tissue changes that reflect post-stroke tissue remodeling. However, our data also highlights the generic difficulty in unambiguously asserting specific causal relationships between tissue status and MR diffusion parameters.

Characterizing the microstructural basis of "unidentified bright objects" in neurofibromatosis type 1: A combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis

Introduction

The histopathological basis of “unidentified bright objects” (UBOs) (hyperintense regions seen on T2-weighted magnetic resonance (MR) brain scans in neurofibromatosis-1 (NF1)) remains unclear. New in vivo MRI-based techniques (multi-exponential T2 relaxation (MET2) and diffusion MR imaging (dMRI)) provide measures relating to microstructural change. We combined these methods and present previously unreported data on in vivo UBO microstructure in NF1.

Methods

3-Tesla dMRI data were acquired on 17 NF1 patients, covering 30 white matter UBOs. Diffusion tensor, kurtosis and neurite orientation and dispersion density imaging parameters were calculated within UBO sites and in contralateral normal appearing white matter (cNAWM). Analysis of MET2 parameters was performed on 24 UBO–cNAWM pairs.

Results

No significant alterations in the myelin water fraction and intra- and extracellular (IE) water fraction were found. Mean T2 time of IE water was significantly higher in UBOs. UBOs furthermore showed increased axial, radial and mean diffusivity, and decreased fractional anisotropy, mean kurtosis and neurite density index compared to cNAWM. Neurite orientation dispersion and isotropic fluid fraction were unaltered.

Conclusion

Our results suggest that demyelination and axonal degeneration are unlikely to be present in UBOs, which appear to be mainly caused by a shift towards a higher T2-value of the intra- and extracellular water pool. This may arise from altered microstructural compartmentalization, and an increase in ‘extracellular-like’, intracellular water, possibly due to intramyelinic edema. These findings confirm the added value of combining dMRI and MET2 to characterize the microstructural basis of T2 hyperintensities in vivo.

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We examine MRI white matter T2-weighted hyperintense lesions, “UBOs” in NF1.

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Myelin water and intra- and extracellular water fractions are unchanged in UBOs.

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Diffusivity is higher, while mean kurtosis and neurite density are lower in UBOs.

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The combined measures suggest that UBOs may arise from intramyelinic edema.

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Combining diffusion MRI and multi-exponential T2 relaxation has added value.

A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging

PURPOSE

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).

RESULTS

The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).

CONCLUSION

The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Time course of axial and radial diffusion kurtosis of white matter infarctions: period of pseudonormalization

BACKGROUND AND PURPOSE

Diffusion kurtosis is a statistical measure for quantifying the deviation of the water diffusion profile from a Gaussian distribution. The current study evaluated the time course of diffusion kurtosis in patients with cerebral infarctions, including perforator, white matter, cortical, and watershed infarctions.

MATERIALS AND METHODS

Subjects were 31 patients, representing 52 observations of lesions. The duration between the onset and imaging ranged from 3 hours to 122 days. Lesions were categorized into 4 groups listed above. Diffusion kurtosis images were acquired with b-values of 0, 1000, and 2000 s/mm(2) applied in 30 directions; variables including DWI signal, ADC, fractional anisotropy, radial diffusivity, axial diffusivity, radial kurtosis, and axial kurtosis, were obtained. The time courses of the relative values (lesion versus contralateral) for these variables were evaluated, and the pseudonormalization period was calculated.

RESULTS

Diffusion kurtosis was highest immediately after the onset of infarction. Trend curves showed that kurtosis decreased with time after onset. Pseudonormalization for radial/axial kurtosis occurred at 13.2/59.9 days for perforator infarctions, 33.1/40.6 days for white matter infarctions, 34.8/35.9 days for cortical infarctions, and 34.1/28.2 days after watershed infarctions. For perforator infarctions, pseudonormalization occurred in the following order: radial kurtosis, ADC, axial kurtosis, and DWI.

CONCLUSIONS

Diffusion kurtosis variables in lesions increased early after infarction and decreased with time. Information provided by diffusion kurtosis imaging, including axial and radial kurtosis, seems helpful in conducting a detailed evaluation of the age of infarction, in combination with T2WI, DWI, and ADC.

Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods

Malignant gliomas consist of glioblastomas, anaplastic astrocytomas, anaplastic oligodendrogliomas and anaplastic oligoastrocytomas, and some less common tumors such as anaplastic ependymomas and anaplastic gangliogliomas. Malignant gliomas have high morbidity and mortality. Even with optimal treatment, median survival is only 12–15 months for glioblastomas and 2–5 years for anaplastic gliomas. However, recent advances in imaging and quantitative analysis of image data have led to earlier diagnosis of tumors and tumor response to therapy, providing oncologists with a greater time window for therapy management. In addition, improved understanding of tumor biology, genetics, and resistance mechanisms has enhanced surgical techniques, chemotherapy methods, and radiotherapy administration. After proper diagnosis and institution of appropriate therapy, there is now a vital need for quantitative methods that can sensitively detect malignant glioma response to therapy at early follow-up times, when changes in management of nonresponders can have its greatest effect. Currently, response is largely evaluated by measuring magnetic resonance contrast and size change, but this approach does not take into account the key biologic steps that precede tumor size reduction. Molecular imaging is ideally suited to measuring early response by quantifying cellular metabolism, proliferation, and apoptosis, activities altered early in treatment. We expect that successful integration of quantitative imaging biomarker assessment into the early phase of clinical trials could provide a novel approach for testing new therapies, and importantly, for facilitating patient management, sparing patients from weeks or months of toxicity and ineffective treatment. This review will present an overview of epidemiology, molecular pathogenesis and current advances in diagnoses, and management of malignant gliomas.

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 non-gaussian 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.

Double-pulsed diffusional kurtosis imaging

Diffusional kurtosis imaging (DKI) is extended to double-pulsed-field-gradient (d-PFG) diffusion MRI sequences. This gives a practical approach for acquiring and analyzing d-PFG data. In particular, the leading d-PFG effects, beyond what conventional single-pulsed field gradient (s-PFG) provides, are interpreted in terms of the kurtosis for a diffusion displacement probability density function (dPDF) in a six-dimensional (6D) space. The 6D diffusional kurtosis encodes the unique information provided by d-PFG sequences up to second order in the b-value. This observation leads to a compact expression for the signal magnitude, and it suggests novel data acquisition and analysis methods. Double-pulsed DKI (DP-DKI) is demonstrated for in vivo mouse brain with d-PFG data obtained at 7 T. Copyright © 2014 John Wiley & Sons, Ltd.

Axonal deficits in young adults with High Functioning Autism and their impact on processing speed

Microstructural white matter deficits in Autism Spectrum Disorders (ASD) have been suggested by both histological findings and Diffusion Tensor Imaging (DTI) studies, which show reduced fractional anisotropy (FA) and increased mean diffusivity (MD). However, imaging reports are generally not consistent across studies and the underlying physiological causes of the reported differences in FA and MD remain poorly understood. In this study, we sought to further characterize white matter deficits in ASD by employing an advanced diffusion imaging method, the Diffusional Kurtosis Imaging (DKI), and a two-compartment diffusion model of white matter. This model differentially describes intra- and extra-axonal white matter compartments using Axonal Water Fraction (f_axon) a measure reflecting axonal caliber and density, and compartment-specific diffusivity measures. Diagnostic utility of these measures and associations with processing speed performance were also examined. Comparative studies were conducted in 16 young male adults with High Functioning Autism (HFA) and 17 typically developing control participants (TDC). Significantly decreased f_axon was observed in HFA compared to the control group in most of the major white matter tracts, including the corpus callosum, cortico-spinal tracts, and superior longitudinal, inferior longitudinal and inferior fronto-occipital fasciculi. Intra-axonal diffusivity (D_axon) was also found to be reduced in some of these regions. Decreased axial extra-axonal diffusivity (AD_extra) was noted in the genu of the corpus callosum. Reduced processing speed significantly correlated with decreased f_axon and D_axon in several tracts. f_axon of the left cortico-spinal tract and superior longitudinal fasciculi showed good accuracy in discriminating the HFA and TDC groups. In conclusion, these findings suggest altered axonal microstructure in young adults with HFA which is associated with reduced processing speed. Compartment-specific diffusion metrics appear to improve specificity and sensitivity to white matter deficits in this population.

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White matter microstructure is altered in young adults with High Functioning Autism.

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We report decreased Axonal Water Fraction and intra-axonal diffusivity.

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Changes in these DKI metrics are consistent with axonal deficits.

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Alterations in axonal diffusion metrics correlate with reduced processing speed.

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DKI may yield promising biomarkers for autism diagnosis and characterization.

Leading non-Gaussian corrections for diffusion orientation distribution function

An analytical representation of the leading non-Gaussian corrections for a class of diffusion orientation distribution functions (dODFs) is presented. This formula is constructed from the diffusion and diffusional kurtosis tensors, both of which may be estimated with diffusional kurtosis imaging (DKI). By incorporating model-independent non-Gaussian diffusion effects, it improves on the Gaussian approximation used in diffusion tensor imaging (DTI). This analytical representation therefore provides a natural foundation for DKI-based white matter fiber tractography, which has potential advantages over conventional DTI-based fiber tractography in generating more accurate predictions for the orientations of fiber bundles and in being able to directly resolve intra-voxel fiber crossings. The formula is illustrated with numerical simulations for a two-compartment model of fiber crossings and for human brain data. These results indicate that the inclusion of the leading non-Gaussian corrections can significantly affect fiber tractography in white matter regions, such as the centrum semiovale, where fiber crossings are common.

Non-Gaussian water diffusion kurtosis imaging of prostate cancer

PURPOSE

To evaluate the non-Gaussian water diffusion properties of prostate cancer (PCa) and determine the diagnostic performance of diffusion kurtosis (DK) imaging for distinguishing PCa from benign tissues within the peripheral zone (PZ), and assessing tumor lesions with different Gleason scores.

MATERIALS AND METHODS

Nineteen patients who underwent diffusion weighted (DW) magnetic resonance imaging using multiple b-values and were pathologically confirmed with PCa were enrolled in this study. Apparent diffusion coefficient (ADC) was derived using a monoexponential model, while diffusion coefficient (D) and kurtosis (K) were determined using a DK model. Differences between the ADC, D and K values of benign PZ and PCa, as well as those of tumor lesions with Gleason scores of 6, 7 and ≥8 were assessed. Correlations between parameters D and K in PCa were analyzed using Pearson's correlation coefficient. ADC, D and K values were correlated with Gleason scores of 6, 7 and ≥8, respectively.

RESULTS

ADC and D values were significantly (p<0.001) lower in PCa (0.79±0.14μm(2)/ms and 1.56±0.23μm(2)/ms, respectively) compared to benign PZ (1.23±0.19μm(2)/ms and 2.54±0.24μm(2)/ms, respectively). K values were significantly (p<0.001) greater in PCa (0.96±0.20) compared to benign PZ (0.59±0.08). D and K showed fewer overlapping values between benign PZ and PCa compared to ADC. There was a strong negative correlation between D and K values in PCa (Pearson correlation coefficient r=-0.729; p<0.001). ADC and K values differed significantly in tumor lesions with Gleason scores of 6, 7 and ≥8 (p<0.001 and p=0.001, respectively), although no significant difference was detected for D values (p=0.325). Significant correlations were found between the ADC value and Gleason score (r=-0.828; p<0.001), as well as the K value and Gleason score (r=0.729; p<0.001).

CONCLUSION

DK model may add value in PCa detection and diagnosis. K potentially offers a new metric for assessment of PCa.

Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging

PURPOSE

Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies and six sparsifying transforms to show their impact when applied to accelerate compressive sensing-diffusion spectrum imaging.

METHODS

We propose a novel sampling scheme that assures uniform angular and random radial q-space samples. We also compare and implement six discrete sparse representations of the EAP and thoroughly evaluate them on synthetic and real data using metrics from the full EAP, kurtosis, and orientation distribution function.

RESULTS

The discrete wavelet transform with Cohen-Daubechies-Feauveau 9/7 wavelets and uniform angular sampling in combination with random radial sampling showed to be better than other tested techniques to accurately reconstruct the EAP and its features.

CONCLUSION

It is important to jointly optimize the sampling scheme and the sparsifying transform to obtain accelerated compressive sensing-diffusion spectrum imaging. Experiments on synthetic and real human brain data show that one can robustly recover both radial and angular EAP features while undersampling the acquisition to 64 measurements (undersampling factor of 4).

A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imaging

INTRODUCTION

Diffusional kurtosis imaging (DKI) is a more sensitive technique than conventional diffusion tensor imaging (DTI) for assessing tissue microstructure. In particular, it quantifies the microstructural integrity of white matter, even in the presence of crossing fibers. The aim of this preliminary study was to compare how DKI and DTI show white matter alterations in Parkinson disease (PD).

METHODS

DKI scans were obtained with a 3-T magnetic resonance imager from 12 patients with PD and 10 healthy controls matched by age and sex. Tract-based spatial statistics were used to compare the mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA) maps of the PD patient group and the control group. In addition, a region-of-interest analysis was performed for the area of the posterior corona radiata and superior longitudinal fasciculus (SLF) fiber crossing.

RESULTS

FA values in the frontal white matter were significantly lower in PD patients than in healthy controls. Reductions in MK occurred more extensively throughout the brain: in addition to frontal white matter, MK was lower in the parietal, occipital, and right temporal white matter. The MK value of the area of the posterior corona radiata and SLF fiber crossing was also lower in the PD group.

CONCLUSION

DKI detects changes in the cerebral white matter of PD patients more sensitively than conventional DTI. In addition, DKI is useful for evaluating crossing fibers. By providing a sensitive index of brain pathology in PD, DKI may enable improved monitoring of disease progression.

Non-Gaussian analysis of diffusion weighted imaging in head and neck at 3T: a pilot study in patients with nasopharyngeal carcinoma

PURPOSE

To technically investigate the non-Gaussian diffusion of head and neck diffusion weighted imaging (DWI) at 3 Tesla and compare advanced non-Gaussian diffusion models, including diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), intravoxel incoherent motion (IVIM) and statistical model in the patients with nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

After ethics approval was granted, 16 patients with NPC were examined using DWI performed at 3T employing an extended b-value range from 0 to 1500 s/mm(2). DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models on primary tumor, metastatic node, spinal cord and muscle. Non-Gaussian parameter maps were generated and compared to apparent diffusion coefficient (ADC) maps in NPC.

RESULTS

Diffusion in NPC exhibited non-Gaussian behavior at the extended b-value range. Non-Gaussian models achieved significantly better fitting of DWI signal than the mono-exponential model. Non-Gaussian diffusion coefficients were substantially different from mono-exponential ADC both in magnitude and histogram distribution.

CONCLUSION

Non-Gaussian diffusivity in head and neck tissues and NPC lesions could be assessed by using non-Gaussian diffusion models. Non-Gaussian DWI analysis may reveal additional tissue properties beyond ADC and holds potentials to be used as a complementary tool for NPC characterization.

Spatial Mapping of Translational Diffusion Coefficients Using Diffusion Tensor Imaging: A Mathematical Description

In this article, we discuss the theoretical background for diffusion weighted imaging and diffusion tensor imaging. Molecular diffusion is a random process involving thermal Brownian motion. In biological tissues, the underlying microstructures restrict the diffusion of water molecules, making diffusion directionally dependent. Water diffusion in tissue is mathematically characterized by the diffusion tensor, the elements of which contain information about the magnitude and direction of diffusion and is a function of the coordinate system. Thus, it is possible to generate contrast in tissue based primarily on diffusion effects. Expressing diffusion in terms of the measured diffusion coefficient (eigenvalue) in any one direction can lead to errors. Nowhere is this more evident than in white matter, due to the preferential orientation of myelin fibers. The directional dependency is removed by diagonalization of the diffusion tensor, which then yields a set of three eigenvalues and eigenvectors, representing the magnitude and direction of the three orthogonal axes of the diffusion ellipsoid, respectively. For example, the eigenvalue corresponding to the eigenvector along the long axis of the fiber corresponds qualitatively to diffusion with least restriction. Determination of the principal values of the diffusion tensor and various anisotropic indices provides structural information. We review the use of diffusion measurements using the modified Stejskal-Tanner diffusion equation. The anisotropy is analyzed by decomposing the diffusion tensor based on symmetrical properties describing the geometry of diffusion tensor. We further describe diffusion tensor properties in visualizing fiber tract organization of the human brain.

High-field small animal magnetic resonance oncology studies

This review focuses on the applications of high magnetic field magnetic resonance imaging (MRI) and spectroscopy (MRS) to cancer studies in small animals. High-field MRI can provide information about tumor physiology, the microenvironment, metabolism, vascularity and cellularity. Such studies are invaluable for understanding tumor growth and proliferation, response to treatment and drug development. The MR techniques reviewed here include (1)H, (31)P, chemical exchange saturation transfer imaging and hyperpolarized (13)C MRS as well as diffusion-weighted, blood oxygen level dependent contrast imaging and dynamic contrast-enhanced MRI. These methods have been proven effective in animal studies and are highly relevant to human clinical studies.

Sex dimorphism of cortical water diffusion in normal aging measured by magnetic resonance imaging

Background: The purpose of this study was to examine sex dimorphism in water diffusion in the brain throughout the normal aging process by magnetic resonance imaging.

Methods: Diffusion-weighted images covering the majority of the brain were acquired from 77 healthy participants. Both the mean water diffusivity and diffusion kurtosis were calculated from the cortical regions and parcellated according to the template in anatomical automatic labeling. The mean water diffusivity and diffusion kurtosis from both sexes were examined and subsequently correlated with age. Statistical significance was set at a threshold of p < 0.01 after correction for multiple comparisons. In regions that reached statistical significance, a linear regression model was performed. Analysis of variance was conducted to determine the interaction between aging and sex.

Results: Sex differences were observed for three aspects. First, compared to females, males presented increased mean water diffusivity and a decreased diffusion kurtosis in the frontal and temporal lobes. Second, a widespread age-related increase in mean water diffusivity was observed, which was more significant in the frontal, occipital, and temporal areas and in the cingulum in females. Third, the diffusion kurtosis decreased with aging but only in restricted areas for both sexes. For the interaction of aging and sex, the most significant change was observed with regards to mean diffusivity, mostly in the right amygdala.

Conclusions: A sex-related dimorphism in water diffusion throughout the aging process was observed in the cortex using magnetic resonance imaging.

Non-Gaussian water diffusion in aging white matter

Age-associated white matter degeneration has been well documented and is likely an important mechanism contributing to cognitive decline in older adults. Recent work has explored a range of noninvasive neuroimaging procedures to differentially highlight alterations in the tissue microenvironment. Diffusional kurtosis imaging (DKI) is an extension of diffusion tensor imaging (DTI) that accounts for non-Gaussian water diffusion and can reflect alterations in the distribution and diffusion properties of tissue compartments. We used DKI to produce whole-brain voxel-based maps of mean, axial, and radial diffusional kurtoses, quantitative indices of the tissue microstructure's diffusional heterogeneity, in 111 participants ranging from the age of 33 to 91 years. As suggested from prior DTI studies, greater age was associated with alterations in white-matter tissue microstructure, which was reflected by a reduction in all 3 DKI metrics. Prominent effects were found in prefrontal and association white matter compared with relatively preserved primary motor and visual areas. Although DKI metrics co-varied with DTI metrics on a global level, DKI provided unique regional sensitivity to the effects of age not available with DTI. DKI metrics were additionally useful in combination with DTI metrics for the classification of regions according to their multivariate "diffusion footprint", or pattern of relative age effect sizes. It is possible that the specific multivariate patterns of age-associated changes measured are representative of different types of microstructural pathology. These results suggest that DKI provides important complementary indices of brain microstructure for the study of brain aging and neurologic disease.

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: a long-range point of view beyond 2020

Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD.

Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling

PURPOSE

To employ the stretched exponential and diffusional kurtosis models to study the non-Gaussian behavior of diffusion-related signal decay of the liver in an animal model of hepatic fibrosis.

MATERIALS AND METHODS

High b-value diffusion imaging data (up to 3500 s/mm(2) ) of ex vivo murine liver specimens was acquired using a 9.4 T MRI scanner. A simple monoexponential model as well as the stretched exponential and diffusional kurtosis models were employed to analyze the diffusion data, the results of which were correlated with liver histopathology.

RESULTS

Strong correlations between histopathological assessments of hepatic fibrosis and parameters derived from the stretched exponential and diffusional kurtosis models were found. Using Akaike's Information Criterion (AIC) analyses, the kurtosis model was found to result in an improved fit of the high b-value diffusion data when compared to both the monoexponential and stretched exponential models.

CONCLUSION

The use of diffusional kurtosis or stretched exponential models, applied to the characterization of the non-Gaussian behavior of the molecular diffusion of liver exhibited over an extended b-factor range, affords the potential for an increased capability of magnetic resonance imaging (MRI) in the characterization of chronic liver disease.

Diffusional kurtosis imaging of the developing brain

BACKGROUND AND PURPOSE

Diffusional kurtosis imaging is an extension of DTI but includes non-Gaussian diffusion effects, allowing more comprehensive characterization of microstructural changes during brain development. Our purpose was to use diffusional kurtosis imaging to measure age-related microstructural changes in both the WM and GM of the developing human brain.

MATERIALS AND METHODS

Diffusional kurtosis imaging was performed in 59 subjects ranging from birth to 4 years 7 months of age. Diffusion metrics, fractional anisotropy, and mean kurtosis were collected from VOIs within multiple WM and GM structures and subsequently analyzed with respect to age. Diffusional kurtosis tractography images at various stages of development were also generated.

RESULTS

Fractional anisotropy and mean kurtosis both showed age-related increases in all WM regions, reflecting progression of diffusional anisotropy throughout development, predominantly in the first 2 years of life (eg, 70% and 157% increase in fractional anisotropy and mean kurtosis, respectively, from birth to 2 years for the splenium). However, mean kurtosis detected continued microstructural changes in WM past the fractional anisotropy plateau, accounting for more delayed isotropic changes (eg, 90% of maximum fractional anisotropy was reached at 5 months, whereas 90% of maximum mean kurtosis occurred at 18 months for the external capsule). Mean kurtosis may also provide greater characterization of GM maturation (eg, the putamen showed no change in fractional anisotropy but an 81% change in mean kurtosis from birth to 4 years 7 months).

CONCLUSIONS

Mean kurtosis detects significant microstructural changes consistent with known patterns of brain maturation. In comparison with fractional anisotropy, mean kurtosis may offer a more comprehensive evaluation of age-related microstructural changes in both WM and GM and is potentially a valuable technique for studying brain development.

White matter tract integrity metrics reflect the vulnerability of late-myelinating tracts in Alzheimer's disease

Post-mortem and imaging studies have observed that white matter (WM) degenerates in a pattern inverse to myelin development, suggesting preferential regional vulnerabilities influencing cognitive decline in AD. This study applied novel WM tract integrity (WMTI) metrics derived from diffusional kurtosis imaging (DKI) to examine WM tissue properties in AD within this framework. Using data from amnestic mild cognitive impairment (aMCI, n = 12), AD (n = 14), and normal control (NC; n = 15) subjects, mixed models revealed interaction effects: specific WMTI metrics of axonal density and myelin integrity (i.e. axonal water fraction, radial extra-axonal diffusivity) in late-myelinating tracts (i.e. superior and inferior longitudinal fasciculi) changed in the course of disease, but were stable in the initial stages for early-myelinating tracts (i.e. posterior limb of the internal capsule, cerebral peduncles). WMTI metrics in late-myelinating tracts correlated with semantic verbal fluency, a cognitive function known to decline in AD. These findings corroborate the preferential vulnerability of late-myelinating tracts, and illustrate an application of WMTI metrics to characterizing the regional course of WM changes in AD.

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We investigated the vulnerability of late-myelinating tracts in AD using WMTI metrics.

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WMTI metrics are derived from the biophysical modeling of the DKI signal.

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These metrics indicate pathological features like axonal density and myelin integrity.

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WMTI metrics were largely stable in early-myelinating tracts through the course of AD.

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Axonal density loss & myelin breakdown were observed in late-myelinating tracts.

Novel white matter tract integrity metrics sensitive to Alzheimer disease progression

BACKGROUND AND PURPOSE

Along with cortical abnormalities, white matter microstructural changes such as axonal loss and myelin breakdown are implicated in the pathogenesis of Alzheimer disease. Recently, a white matter model was introduced that relates non-Gaussian diffusional kurtosis imaging metrics to characteristics of white matter tract integrity, including the axonal water fraction, the intra-axonal diffusivity, and the extra-axonal axial and radial diffusivities.

MATERIALS AND METHODS

This study reports these white matter tract integrity metrics in subjects with amnestic mild cognitive impairment (n = 12), Alzheimer disease (n = 14), and age-matched healthy controls (n = 15) in an effort to investigate their sensitivity, diagnostic accuracy, and associations with white matter changes through the course of Alzheimer disease.

RESULTS

With tract-based spatial statistics and region-of-interest analyses, increased diffusivity in the extra-axonal space (extra-axonal axial and radial diffusivities) in several white matter tracts sensitively and accurately discriminated healthy controls from those with amnestic mild cognitive impairment (area under the receiver operating characteristic curve = 0.82-0.95), while widespread decreased axonal water fraction discriminated amnestic mild cognitive impairment from Alzheimer disease (area under the receiver operating characteristic curve = 0.84). Additionally, these white matter tract integrity metrics in the body of the corpus callosum were strongly correlated with processing speed in amnestic mild cognitive impairment (r = |0.80-0.82|, P < .001).

CONCLUSIONS

These findings have implications for the course and spatial progression of white matter degeneration in Alzheimer disease, suggest the mechanisms by which these changes occur, and demonstrate the viability of these white matter tract integrity metrics as potential neuroimaging biomarkers of the earliest stages of Alzheimer disease and disease progression.

Diffusion kurtosis imaging study of prostate cancer: preliminary findings

PURPOSE

To evaluate the differences in parameters of diffusion kurtosis imaging (DKI) between prostate cancer, benign prostatic hyperplasia (BPH), and benign peripheral zone (PZ).

MATERIALS AND METHODS

Twenty-four foci of prostate cancer, 41 BPH nodules (14 stromal and 27 nonstromal hyperplasia), and 20 benign PZ from 20 patients who underwent radical prostatectomy were investigated. Diffusion-weighted imaging (DWI) was performed using 11 b-values (0-1500 s/mm(2) ). DKI model relates DWI signal decay to parameters that reflect non-Gaussian diffusion coefficient (D) and deviations from normal distribution (K). A mixed model analysis of variance and receiver operating characteristic (ROC) analyses were performed to assess the statistical significance of the metrics of DKI and apparent diffusion coefficient (ADC).

RESULTS

K was significantly higher in prostate cancer and stromal BPH than in benign PZ (1.19 ± 0.24 and 0.99 ± 0.28 versus 0.63 ± 0.23, P < 0.001 and P < 0.001, respectively). K showed a trend toward higher levels in prostate cancer than in stromal BPH (1.19 ± 0.24 versus 0.99 ± 0.28, P = 0.051). On the ROC analyses, a significant difference in area under the curve was not observed between K and ADC, however, K showed the highest sensitivity among three parameters.

CONCLUSION

DKI may contribute to the imaging diagnosis of prostate cancer, especially in the differential diagnosis of prostate cancer and BPH.

Characterization of Anomalous Diffusion in Porous Biological Tissues Using Fractional Order Derivatives and Entropy

In this high-resolution magnetic resonance imaging (MRI) study at 17.6 Tesla of a fixed rat brain, we used the continuous time random walk theory (CTRW) for Brownian motion to characterize anomalous diffusion. The complex mesoporus structure of biological tissues (membranes, organelles, and cells) perturbs the motion of the random walker (water molecules in proton MRI) introducing halts between steps (waiting times) and restrictions on step sizes (jump lengths). When such waiting times and jump lengths are scaled with probability distributions that follow simple inverse power laws (t^-(1+α), |x|^-(1+β)) non-Gaussian motion gives rise to sub- and super- diffusion. In the CTRW approach, the Fourier transform yields a solution to the generalized diffusion equation that can be expressed by the Mittag-Leffler function (MLF), E_α (- D_{α, β}|q|^βΔ^α). We interrogated both white and gray matter regions in a 1 mm slice of a fixed rat brain (190 μm in plane resolution) with diffusion weighted MRI experiments using b-values up to 25,000 s/mm², by independently varying q and Δ. When fitting these data to our model, the fractional order parameters, α and β, and the entropy measure, [Formula: see text], were found to provide excellent contrast between white and gray matter and to give results that were sensitive to the type of diffusion experiment performed.

Microstructural integrity of early- versus late-myelinating white matter tracts in medial temporal lobe epilepsy

PURPOSE

Patients with medial temporal lobe epilepsy (MTLE) exhibit structural brain damage involving gray matter (GM) and white matter (WM). The mechanisms underlying tissue loss in MTLE are unclear and may be associated with a combination of seizure excitotoxicity and WM vulnerability. The goal of this study was to investigate whether late-myelinating WM tracts are more vulnerable to injury in MTLE compared with early myelinating tracts.

METHODS

Diffusional kurtosis imaging scans were obtained from 25 patients with MTLE and from 36 matched healthy controls. Diffusion measures from regions of interest (ROIs) for both late- and early myelinating WM tracts were analyzed. Regional Z-scores were computed with respect to normal controls to compare WM in early myelinating tracts versus late-myelinating tracts.

KEY FINDINGS

We observed that late-myelinating tracts exhibited a larger decrease in mean, axial, and radial kurtosis compared with early myelinating tracts. We also observed that the change in radial kurtosis was more pronounced in late-myelinating tracts ipsilateral to the side of seizure onset.

SIGNIFICANCE

These results suggest a developmentally based preferential susceptibility of late-myelinating WM tracts to damage in MTLE. Brain injury in epilepsy may be due to the pathologic effects of seizures in combination with regional WM vulnerability.

A better characterization of spinal cord damage in multiple sclerosis: a diffusional kurtosis imaging study

BACKGROUND AND PURPOSE

The spinal cord is a site of predilection for MS lesions. While diffusion tensor imaging is useful for the study of anisotropic systems such as WM tracts, it is of more limited utility in tissues with more isotropic microstructures (on the length scales studied with diffusion MR imaging) such as gray matter. In contrast, diffusional kurtosis imaging, which measures both Gaussian and non-Gaussian properties of water diffusion, provides more biomarkers of both anisotropic and isotropic structural changes. The aim of this study was to investigate the cervical spinal cord of patients with MS and to characterize lesional and normal-appearing gray matter and WM damage by using diffusional kurtosis imaging.

MATERIALS AND METHODS

Nineteen patients (13 women, mean age = 41.1 ± 10.7 years) and 16 controls (7 women, mean age = 35.6 ± 11.2-years) underwent MR imaging of the cervical spinal cord on a 3T scanner (T2 TSE, T1 magnetization-prepared rapid acquisition of gradient echo, diffusional kurtosis imaging, T2 fast low-angle shot). Fractional anisotropy, mean diffusivity, and mean kurtosis were measured on the whole cord and in normal-appearing gray matter and WM.

RESULTS

Spinal cord T2-hyperintense lesions were identified in 18 patients. Whole spinal cord fractional anisotropy and mean kurtosis (P = .0009, P = .003), WM fractional anisotropy (P = .01), and gray matter mean kurtosis (P = .006) were significantly decreased, and whole spinal cord mean diffusivity (P = .009) was increased in patients compared with controls. Mean spinal cord area was significantly lower in patients (P = .04).

CONCLUSIONS

Diffusional kurtosis imaging of the spinal cord can provide a more comprehensive characterization of lesions and normal-appearing WM and gray matter damage in patients with MS. Diffusional kurtosis imaging can provide additional and complementary information to DTI on spinal cord pathology.

Attention-deficit/hyperactivity disorder without comorbidity is associated with distinct atypical patterns of cerebral microstructural development

Differential core symptoms and treatment responses are associated with the pure versus comorbid forms of attention-deficit/hyperactivity disorder (ADHD). However, comorbidity has largely been unaccounted for in neuroimaging studies of ADHD. We used diffusional kurtosis imaging to investigate gray matter (GM) and white matter (WM) microstructure of children and adolescents with ADHD (n = 22) compared to typically developing controls (TDC, n = 27) and examined whether differing developmental patterns are related to comorbidity. The ADHD group (ADHD-mixed) consisted of subgroups with and without comorbidity (ADHD-comorbid, n = 11; ADHD-pure, n = 11, respectively). Age-related changes and group differences in cerebral microstructure of the ADHD-mixed group and each ADHD subgroup were compared to TDC. Whole-brain voxel-based analyses with mean kurtosis (MK) and mean diffusivity (MD) metrics were conducted to probe GM and WM. Tract-based spatial statistics analyses of WM were performed with MK, MD, fractional anisotropy, and directional (axial, radial) kurtosis and diffusivity metrics. ADHD-pure patients lacked significant age-related changes in GM and WM microstructure that were observed globally in TDC and had significantly greater WM microstructural complexity than TDC in bilateral frontal and parietal lobes, insula, corpus callosum, and right external and internal capsules. Including ADHD patients with diverse comorbidities in analyses masked these findings. A distinct atypical age-related trajectory and aberrant regional differences in brain microstructure were detected in ADHD without comorbidity. Our results suggest that different phenotypic manifestations of ADHD, defined by the presence or absence of comorbidity, differ in cerebral microstructural markers.