MRI quantification of non-Gaussian water diffusion by kurtosis analysis

Initial Application of Diffusional Kurtosis Imaging in Evaluating Brain Development of Healthy Preterm Infants

Objective

To explore the parametric characteristics of diffusional kurtosis imaging (DKI) in the brain development of healthy preterm infants.

Materials and Methods

Conventional magnetic resonance imaging (MRI) and DKI were performed in 35 preterm (29 to 36 weeks gestational age [GA]; scanned at 33 to 44 weeks postmenstrual age [PMA]) and 10 term infants (37.4 to 40.7 weeks GA; scanned at 38.3 to 42.9 weeks PMA). Fractional anisotropy (FA), mean diffusivity (MD) and mean kurtosis (MK) values from 8 regions of interest, including both white matter (WM) and gray matter (GM), were obtained.

Results

MK and FA values were positively correlated with PMA in most selected WM regions, such as the posterior limbs of the internal capsule (PLIC) and the splenium of the corpus callosum (SCC). The positive correlation between MK value and PMA in the deep GM region was higher than that between FA and PMA. The MK value gradually decreased from the PLIC to the cerebral lobe. In addition, DKI parameters exhibited subtle differences in the parietal WM between the preterm and term control groups.

Conclusions

MK may serve as a more reliable imaging marker of the normal myelination process and provide a more robust characterization of deep GM maturation.

Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study

Our aim was to characterize the nature and extent of pathological changes in the normal-appearing white matter (NAWM) of patients with multiple sclerosis (MS) using novel diffusion kurtosis imaging-derived white matter tract integrity (WMTI) metrics and to investigate the association between these WMTI metrics and clinical parameters. Thirty-two patients with relapsing-remitting MS and 19 age- and gender-matched healthy controls underwent MRI and neurological examination. Maps of mean diffusivity, fractional anisotropy and WMTI metrics (intra-axonal diffusivity, axonal water fraction, tortuosity and axial and radial extra-axonal diffusivity) were created. Tract-based spatial statistics analysis was performed to assess for differences in the NAWM between patients and controls. A region of interest analysis of the corpus callosum was also performed to assess for group differences and to evaluate correlations between WMTI metrics and measures of disease severity. Mean diffusivity and radial extra-axonal diffusivity were significantly increased while fractional anisotropy, axonal water fraction, intra-axonal diffusivity and tortuosity were decreased in MS patients compared with controls (p values ranging from <0.001 to <0.05). Axonal water fraction in the corpus callosum was significantly associated with the expanded disability status scale score (ρ = -0.39, p = 0.035). With the exception of the axial extra-axonal diffusivity, all metrics were correlated with the symbol digits modality test score (p values ranging from 0.001 to <0.05). WMTI metrics are thus sensitive to changes in the NAWM of MS patients and might provide a more pathologically specific, clinically meaningful and practical complement to standard diffusion tensor imaging-derived metrics.

Differentiating T2 hyperintensity in neonatal white matter by two-compartment model of diffusional kurtosis imaging

In conventional neonatal MRI, the T2 hyperintensity (T2h) in cerebral white matter (WM) at term-equivalent age due to immaturity or impairment is still difficult to identify. To clarify such issue, this study used the metrics derived from a two-compartment WM model of diffusional kurtosis imaging (WM-DKI), including intra-axonal, extra-axonal axial and radial diffusivities (D_a, D_e,// and D_e,⊥), to compare WM differences between the simple T2h and normal control for both preterm and full-term neonates, and between simple T2h and complex T2h with hypoxic-ischemic encephalopathy (HIE). Results indicated that compared with control, the simple T2h showed significantly increased D_e,// and D_e,⊥, but no significant change in D_a in multiple premyelination regions, indicative of expanding extra-axonal diffusion microenvironment; while myelinated regions showed no changes. However, compared with simple T2h, the complex T2h with HIE had decreased D_a, increased D_e,⊥ in both premyelination and myelinated regions, indicative of both intra- and extra-axonal diffusion alterations. While diffusion tensor imaging (DTI) failed to distinguish simple T2h from complex T2h with HIE. In conclusion, superior to DTI-metrics, WM-DKI metrics showed more specificity for WM microstructural changes to distinguish simple T2h from complex T2h with HIE.

Kurtosis fractional anisotropy, its contrast and estimation by proxy

The diffusion kurtosis observed with diffusion magnetic resonance imaging (dMRI) may vary with direction. This directional variation is summarized in the scalar kurtosis fractional anisotropy (KFA). Recent studies suggest that kurtosis anisotropy offers microstructural contrast not contained in other commonly used dMRI markers. We compare KFA to other dMRI contrasts in fixed rat brain and in human brain. We then investigate the observed contrast differences using data obtained in a physical phantom and simulations based on data from the phantom, rat spinal cord, and human brain. Lastly, we assess a strategy for rapid estimation of a computationally modest KFA proxy by evaluating its correlation to true KFA for varying number of sampling directions and signal-to-noise ratio (SNR) levels. We also map this proxy’s b-value dependency. We find that KFA supplements the contrast of other dMRI metrics – particularly fractional anisotropy (FA) which vanishes in near orthogonal fiber arrangements where KFA does not. Simulations and phantom data support this interpretation. KFA therefore supplements FA and could be useful for evaluation of complex tissue arrangements. The KFA proxy is strongly correlated to true KFA when sampling is performed along at least nine directions and SNR is high.

Stroke assessment with intravoxel incoherent motion diffusion-weighted MRI

Intravoxel incoherent motion (IVIM) diffusion-weighted MRI can simultaneously measure diffusion and perfusion characteristics in a non-invasive way. This study aimed to determine the potential utility of IVIM in characterizing brain diffusion and perfusion properties for clinical stroke. The multi-b-value diffusion-weighted images of 101 patients diagnosed with acute/subacute ischemic stroke were retrospectively evaluated. The diffusion coefficient D, representing the water apparent diffusivity, was obtained by fitting the diffusion data with increasing high b-values to a simple mono-exponential model. The IVIM-derived perfusion parameters, pseudodiffusion coefficient D*, vascular volume fraction f and blood flow-related parameter fD*, were calculated with the bi-exponential model. Additionally, the apparent diffusion coefficient (ADC) was fitted according to the mono-exponential model using all b-values. The diffusion parameters for the ischemic lesion and normal contralateral region were measured in each patient. Statistical analysis was performed using the paired Student t-test and Pearson correlation test. Diffusion data in both the ischemic lesion and normal contralateral region followed the IVIM bi-exponential behavior, and the IVIM model showed better goodness of fit than the mono-exponential model with lower Akaike information criterion values. The paired Student t-test revealed significant differences for all diffusion parameters (all P < 0.001) except D* (P = 0.218) between ischemic and normal areas. For all patients in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001) and f (r = 0.541, P < 0.001; r = 0.262, P = 0.008); significant correlation was also found between ADC and fD* in the ischemic region (r = 0.254, P = 0.010). For all pixels within the region of interest from a representative subject in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001), f (r = 0.823, P < 0.001; r = 0.652, P < 0.001) and fD* (r = 0.294, P < 0.001; r = 0.340, P < 0.001). These findings may have clinical implications for the use of IVIM imaging in the assessment and management of acute/subacute stroke patients. Copyright © 2016 John Wiley & Sons, Ltd.

Assessment of severity of leukoaraiosis: a diffusional kurtosis imaging study

OBJECTIVE

The objective was to investigate the capabilities of diffusional kurtosis imaging (DKI) in detection of age-related white matter (WM) changes in elderly patients with leukoaraiosis.

MATERIAL AND METHODS

Fractional anisotropy (FA), kurtosis, and diffusion parameters in the frontal lobe and parietal lobe were compared between 14 patients at Fazekas scale 0 and 1, and 15 patients at Fazekas scale 2 and 3.

RESULTS

FA and DKI parameters were significantly altered in the ischemic lesions vs normal regions of WM in the severe patients.

CONCLUSION

DKI can provide sensitive imaging biomarkers for assessing the severity of leukoaraiosis in reference to Fazekas score.

Simultaneous assessment of cerebral blood volume and diffusion heterogeneity using hybrid IVIM and DK MR imaging: initial experience with brain tumors

Objectives

To investigate the feasibility of simultaneously assessing cerebral blood volume and diffusion heterogeneity using hybrid diffusion-kurtosis (DK) and intravoxel-incoherent-motion (IVIM) MR imaging.

Methods

Fifteen healthy volunteers and 30 patients with histologically proven brain tumours (25 WHO grade II–IV gliomas and five metastases) were recruited. On a 3-T system, diffusion-weighted imaging was performed with six b-values ranging from 0 to 1,700 s/mm². Nonlinear least-squares fitting was employed to extract diffusion coefficient (D), diffusion kurtosis coefficient (K, a measure of the degree of non-Gaussian and heterogeneous diffusion) and intravascular volume fraction (f, a measure proportional to cerebral blood volume). Repeated-measures multivariate analysis of variance and receiver operating characteristic analysis were performed to assess the ability of D/K/f in differentiating contrast-enhanced tumour from peritumoral oedema and normal-appearing white matter.

Results

Based on our imaging setting (baseline signal-to-noise ratio = 32–128), coefficient of variation was 14–20 % for K, ~6 % for D and 26–44 % for f. The indexes were able to differentiate contrast-enhanced tumour (Wilks’ λ = 0.026, p < 10^-3), and performance was greatest with K, followed by f and D.

Conclusions

Hybrid DK IVIM imaging is capable of simultaneously measuring cerebral perfusion and diffusion indexes that together may improve brain tumour diagnosis.

Key Points

• Hybrid DK-IVIM imaging allows simultaneous measurement of K, D and f.

• Combined K/D/f better demarcates contrast-enhanced tumour than they do separately.

• f correlates better with contrast-leakage-corrected CBV_DSCthan with uncorrected CBV_DSC.

Diffusional Kurtosis Imaging in Idiopathic Normal Pressure Hydrocephalus: Correlation with Severity of Cognitive Impairment

PURPOSE

Diffusional kurtosis imaging (DKI) is an emerging technique that describes diffusion of water molecules in terms of deviation from Gaussian distribution. This study investigated correlations between DKI metrics and cognitive function in patients with idiopathic normal pressure hydrocephalus (iNPH).

MATERIALS AND METHODS

DKI was performed in 29 iNPH patients and 14 age-matched controls. Mini-mental state examination (MMSE), frontal assessment battery (FAB), and trail making test A (TMT-A) were used as cognitive measures. Tract-based spatial statistics (TBSS) analyses were performed to investigate the between-group differences and correlations with the cognitive measures of the diffusion metrics, including mean kurtosis (MK), fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD).

RESULTS

In iNPH patients, FA and MK identified positive correlations with cognitive function in similar regions, predominantly in the frontal lobes (P < 0.05, corrected for multiple comparisons). The frontoparietal subcortical white matter showed significant correlations with FAB and TMT-A across more extensive areas in MK analyses than in FA. ADC, AD, and RD analyses showed no significant correlations with MMSE and FAB, while negative correlation with TMT-A was observed in the limited portion of the frontal deep white matter.

CONCLUSION

Both FA and MK correlated well with cognitive impairment in iNPH. The observed differences between FA and MK results suggest DKI may play a complementary role to conventional FA and ADC analyses, especially for evaluation of the subcortical white matter.

Functional Magnetic Resonance Imaging in Acute Kidney Injury: Present Status

Acute kidney injury (AKI) is a common complication of hospitalization that is characterized by a sudden loss of renal excretory function and associated with the subsequent development of chronic kidney disease, poor prognosis, and increased mortality. Although the pathophysiology of renal functional impairment in the setting of AKI remains poorly understood, previous studies have identified changes in renal hemodynamics, perfusion, and oxygenation as key factors in the development and progression of AKI. The early assessment of these changes remains a challenge. Many established approaches are not applicable to humans because of their invasiveness. Functional renal magnetic resonance (MR) imaging offers an alternative assessment tool that could be used to evaluate renal morphology and function noninvasively and simultaneously. Thus, the purpose of this review is to illustrate the principle, application, and role of the techniques of functional renal MR imaging, including blood oxygen level-dependent imaging, arterial spin labeling, and diffusion-weighted MR imaging, in the management of AKI. The use of gadolinium in MR imaging may exacerbate renal impairment and cause nephrogenic systemic fibrosis. Therefore, dynamic contrast-enhanced MR imaging will not be discussed in this paper.

3T multiparametric MRI of the prostate: Does intravoxel incoherent motion diffusion imaging have a role in the detection and stratification of prostate cancer in the peripheral zone?

PURPOSE

To evaluate the potential added value of the intravoxel incoherent motion model to conventional multiparametric magnetic resonance protocol in order to differentiate between healthy and neoplastic prostate tissue in the peripheral zone.

MATERIAL AND METHODS

Mono-exponential and bi-exponential fits were used to calculate ADC and IVIM parameters in 53 patients with peripheral zone biopsy proved tumor. Inferential statistics analysis was performed on T2, ADC and IVIM parameters (D, D*, f) comparing healthy and neoplastic tissues. Linear discriminant analysis was performed for the conventional parameters (T2 and ADC), the IVIM parameters (molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f) and the combined T2-weighted imaging/DWI and IVIM parameters (T2, ADC, D, D* and f). A correlation with Gleason scores was achieved.

RESULTS

The values of T2, ADC and D were significantly lower in cancerous tissues (2749.82 ± 1324.67 ms, 0.76 ± 0.27 × 10(-3)mm(2)/s and 0.99 ± 0.38 × 10(-3)mm(2)/s respectively) compared to those found in the healthy tissues (3750.70 ± 1735.37 ms, 1.39 ± 0.48 × 10(-3)mm(2)/s and 1.77 ± 0.36 × 10(-3)mm(2)/s respectively); D* parameter was significantly increased in neoplastic compared to healthy tissue (15.56 ± 12.91 × 10(-3)mm(2)/s and 10.25 ± 10.52 × 10(-3)mm(2)/s respectively). The specificity, sensitivity and accuracy of the T2-weighted imaging/DWI and IVIM parameters were 100, 96 and 98%, respectively, compare to 88, 92 and 90% and 96, 92 and 94 for T2-weighted imaging/ADC and IVIM alone.

CONCLUSIONS

IVIM parameters increase the specificity and sensitivity in the evaluation of peripheral zone prostate cancer. A statistical difference between low grade tumors and high grade tumors has been demostrated in that ADC, D and D* dataset; in particular, D has been found to have the highest significativity.

Probing lung microstructure with hyperpolarized noble gas diffusion MRI: theoretical models and experimental results

The introduction of hyperpolarized gases ((3)He and (129)Xe) has opened the door to applications for which gaseous agents are uniquely suited-lung MRI. One of the pulmonary applications, diffusion MRI, relies on measuring Brownian motion of inhaled hyperpolarized gas atoms diffusing in lung airspaces. In this article we provide an overview of the theoretical ideas behind hyperpolarized gas diffusion MRI and the results obtained over the decade-long research. We describe a simple technique based on measuring gas apparent diffusion coefficient (ADC) and an advanced technique, in vivo lung morphometry, that quantifies lung microstructure both in terms of Weibel parameters (acinar airways radii and alveolar depth) and standard metrics (mean linear intercept, surface-to-volume ratio, and alveolar density) that are widely used by lung researchers but were previously available only from invasive lung biopsy. This technique has the ability to provide unique three-dimensional tomographic information on lung microstructure from a less than 15 s MRI scan with results that are in good agreement with direct histological measurements. These safe and sensitive diffusion measurements improve our understanding of lung structure and functioning in health and disease, providing a platform for monitoring the efficacy of therapeutic interventions in clinical trials.

Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord

Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques, it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion and its directionality, both techniques are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption neglects pathological processes that are not Gaussian therefore reducing the amount of potentially clinically relevant information. Additions to the Gaussian distribution measured by the excess kurtosis, or peakedness, of the probabilistic model provide a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can offer additional information about the micromolecular environment of the pediatric spinal cord. This is accomplished by a more thorough characterization of the nature of random water displacement within the cord. A novel DKI imaging sequence based on a tilted 2D spatially selective radio frequency pulse providing reduced field of view (FOV) imaging was developed, implemented, and optimized on a 3 Tesla MRI scanner, and tested on pediatric subjects (healthy subjects: 15; patients with spinal cord injury (SCI):5). Software was developed and validated for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in mean kurtosis (p < 0.01) and radial kurtosis (p < 0.01) between healthy subjects and subjects with SCI. DKI provides incremental and novel information over conventional diffusion acquisitions when coupled with higher order estimation algorithms.

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Diffusion Kurtosis Imaging (DKI) was performed on pediatric subjects using a tilted 2D RF reduced field of view sequence.

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Results show statistically significant differences in FA, MK, Krad, and Drad between healthy subjects and patients with SCI.

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DKI provides additional structural information that when paired with DTI metrics could be used as a novel imaging biomarker.

Cognitive Impairment, Neuroimaging, and Alzheimer Neuropathology in Mouse Models of Down Syndrome

Down syndrome (DS) is the most common non-lethal genetic condition that affects approximately 1 in 700 births in the United States of America. DS is characterized by complete or segmental chromosome 21 trisomy, which leads to variable intellectual disabilities, progressive memory loss, and accelerated neurodegeneration with age. During the last three decades, people with DS have experienced a doubling of life expectancy due to progress in treatment of medical comorbidities, which has allowed this population to reach the age when they develop early onset Alzheimer's disease (AD). Individuals with DS develop cognitive and pathological hallmarks of AD in their fourth or fifth decade, and are currently lacking successful prevention or treatment options for dementia. The profound memory deficits associated with DS-related AD (DS-AD) have been associated with degeneration of several neuronal populations, but mechanisms of neurodegeneration are largely unexplored. The most successful animal model for DS is the Ts65Dn mouse, but several new models have also been developed. In the current review, we discuss recent findings and potential treatment options for the management of memory loss and AD neuropathology in DS mouse models. We also review agerelated neuropathology, and recent findings from neuroimaging studies. The validation of appropriate DS mouse models that mimic neurodegeneration and memory loss in humans with DS can be valuable in the study of novel preventative and treatment interventions, and may be helpful in pinpointing gene-gene interactions as well as specific gene segments involved in neurodegeneration.

Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver

An area of rapid advancement in abdominal MRI is diffusion-weighted imaging (DWI). By measuring diffusion properties of water molecules, DWI is capable of non-invasively probing tissue properties and physiology at cellular and macromolecular level. The integration of DWI as part of abdominal MRI exam allows better lesion characterization and therefore more accurate initial diagnosis and treatment monitoring. One of the most technical challenging, but also most useful abdominal DWI applications is in liver and therefore requires special attention and careful optimization. In this article, the latest technical developments of DWI and its liver applications are reviewed with the explanations of the technical principles, recommendations of the imaging parameters, and examples of clinical applications. More advanced DWI techniques, including Intra-Voxel Incoherent Motion (IVIM) diffusion imaging, anomalous diffusion imaging, and Diffusion Kurtosis Imaging (DKI) are discussed.

Parameter Estimation Error Dependency on the Acquisition Protocol in Diffusion Kurtosis Imaging

Mono-exponential kurtosis model is routinely fitted on diffusion weighted, magnetic resonance imaging data to describe non-Gaussian diffusion. Here, the purpose was to optimize acquisitions for this model to minimize the errors in estimating diffusion coefficient and kurtosis. Similar to a previous study, covariance matrix calculations were used, and coefficients of variation in estimating each parameter of this model were calculated. The acquisition parameter, b values, varied in discrete grids to find the optimum ones that minimize the coefficient of variation in estimating the two non-Gaussian parameters. Also, the effect of variation of the target values on the optimized values was investigated. Additionally, the results were benchmarked with Monte Carlo noise simulations. Simple correlations were found between the optimized b values and target values of diffusion and kurtosis. For small target values of the two parameters, there is higher chance of having significant errors; this is caused by maximum b value limits imposed by the scanner than the mathematical bounds. The results here, cover a wide range of parameters D and K so that they could be used in many directionally averaged diffusion weighted cases such as head and neck, prostate, etc.

Eddy current compensated double diffusion encoded (DDE) MRI

PURPOSE

Eddy currents might lead to image distortions in diffusion-weighted echo planar imaging. A method is proposed to reduce their effects on double diffusion encoding (DDE) MRI experiments and the thereby derived microscopic fractional anisotropy (μFA).

METHODS

The twice-refocused spin echo scheme was adapted for DDE measurements. To assess the effect of individual diffusion encodings on the image distortions, measurements of a grid of plastic rods in water were performed. The effect of eddy current compensation on μFA measurements was evaluated in the brains of six healthy volunteers.

RESULTS

The use of an eddy current compensation reduced the signal variation. As expected, the distortions caused by the second encoding were larger than those of the first encoding, entailing a stronger need to compensate for them. For an optimal result, however, both encodings had to be compensated. The artifact reduction strongly improved the measurement of the μFA in ventricles and gray matter by reducing the overestimation. An effect of the compensation on absolute μFA values in white matter was not observed.

CONCLUSION

It is advisable to compensate both encodings in DDE measurements for eddy currents. Magn Reson Med 77:328-335, 2017. © 2015 Wiley Periodicals, Inc.

Time Course of Diffusion Kurtosis in Cerebral Infarctions of Transient Middle Cerebral Artery Occlusion Rat Model

OBJECTIVE

To evaluate the relationship between fiber bundle direction and changes in diffusion kurtosis, we evaluated the apparent diffusion kurtosis coefficients (AKCs) that were perpendicular to and parallel to the principal diffusion tensor eigenvector.

MATERIALS AND METHODS

Adult male Wistar rats were subjected to 30 or 60 minutes of middle cerebral artery occlusion and imaged with a 7T Magnetic Resonance Imager System (Varian MRI System 7T/210: Agilent Technologies, CA). Diffusion kurtosis images were obtained before middle cerebral artery (MCA) reperfusion and 3, 6, and 24 hours after reperfusion to generate the apparent diffusion coefficient (ADC), fractional anisotropy (FA), mean apparent diffusion kurtosis coefficient (mAKC), AKC axial to the eigenvector (axAKC), and AKC radial to the eigenvector (radAKC) images. The time course of the region/normal ratio was evaluated for the above parameters in the caudoputamen and white matter.

RESULTS

Relative FA and relative ADC values decreased 3 hours after MCA reperfusion and remained decreased until 24 hours. Relative mAKC, axAKC, and radAKC values were increased 3 hours after MCA reperfusion, peaked after 6 hours, and slightly decreased after 24 hours. In the white matter, axAKC showed larger changes than radAKC.

CONCLUSION

The time course of the diffusion kurtosis value showed earlier pseudonormalization than the ADC value of the lesions. For white matter lesions, the increase in axAKC was larger than that in radAKC, suggesting that the tissue changes after infarction mainly produce reduced diffusivity along the fibers and lead to increased inhomogeneity of the diffusion.

A survey of current trends in diffusion MRI for structural brain connectivity

In this paper, we review the state of the art in diffusion magnetic resonance imaging (dMRI) and we present current trends in modelling the brain's tissue microstructure and the human connectome. dMRI is today the only tool that can probe the brain's axonal architecture in vivo and non-invasively, and has grown in leaps and bounds in the last two decades since its conception. A plethora of models with increasing complexity and better accuracy have been proposed to characterise the integrity of the cerebral tissue, to understand its microstructure and to infer its connectivity. Here, we discuss a wide range of the most popular, important and well-established local microstructure models and biomarkers that have been proposed from these models. Finally, we briefly present the state of the art in tractography techniques that allow us to understand the architecture of the brain's connectivity.

Comparison of calculated and acquired high b value diffusion-weighted imaging in prostate cancer

PURPOSE

To determine whether the performance of calculated high b value diffusion-weighted images (DWI) derived from regular lower b value DWI using exponential diffusion decay models (intravoxel incoherent motion = IVIM and diffusional kurtosis = DK) is comparable to acquired high b value DWI in prostate cancer detection.

MATERIALS AND METHODS

One hundred six patients underwent diagnostic multiparametric prostate MRI at 3T using an endorectal coil. Five b value (b = 0, 188, 375, 563, 750 s/mm(2)) DWI and high b value (b = 0, 1000 and 2000 s/mm(2)) DWI were acquired. Calculated high b value (b = 1000 s/mm(2) and b = 2000 s/mm(2)) DWI were derived from the DWI dataset using DK and IVIM models. Calculated and acquired high b value DWI images were compared for lesion visibility and image quality by two experienced radiologists (1 and 6 years of experience). GEE with Wald test was used to compare the image quality among the four calculated high b value DWI by comparing the proportion of lesions in each model which were comparable to the acquired images. This comparison was done for all lesions and by lesion location (PZ or CG; low apical/anterior or apical/mid/base)

RESULTS

More lesions were visible on acquired b = 2000 s/mm(2) compared to b = 1000 s/mm(2) DWI. Calculated high b value DWI using the IVIM model had approximately the same number of lesions as acquired high b value DWI, whereas the DK model had fewer lesions than acquired images. The image quality of calculated high b value DWI was comparable to that of acquired images, and the highest quality images were obtained with b1000IVIM. The image quality of calculated b1000IVIM was the same as that of acquired DWI in apical/mid/base (98%) locations and comparable in low apical and anterior (95.4%) locations. The image quality of calculated b2000IVIM was inferior in both apical/mid/base (86.2%) locations and comparable in low apical and anterior (83.9%) locations.

CONCLUSION

Calculated high b value DWI obtained using IVIM model has same lesion visibility as that of acquired DWI. The image quality of calculated high b value DWI relative to corresponding acquired DWI decreases with increase in b value.

Gamma convolution models for self-diffusion coefficient distributions in PGSE NMR

We introduce a closed-form signal attenuation model for pulsed-field gradient spin echo (PGSE) NMR based on self-diffusion coefficient distributions that are convolutions of n gamma distributions, n⩾1. Gamma convolutions provide a general class of uni-modal distributions that includes the gamma distribution as a special case for n=1 and the lognormal distribution among others as limit cases when n approaches infinity. We demonstrate the usefulness of the gamma convolution model by simulations and experimental data from samples of poly(vinyl alcohol) and polystyrene, showing that this model provides goodness of fit superior to both the gamma and lognormal distributions and comparable to the common inverse Laplace transform.

Constrained optimization of gradient waveforms for generalized diffusion encoding

Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences.

Experimental considerations for fast kurtosis imaging

PURPOSE

The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor W¯ and mean diffusivity ( D¯) was made possible from 13 distinct diffusion weighted MRI acquisitions (the 1-3-9 protocol) with simple postprocessing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA).

THEORY AND METHODS

1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D¯, FA, W¯ from human brains are compared with traditional estimates from an extensive diffusion kurtosis imaging data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data.

RESULTS

Accuracy and precision in D¯ and W¯ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in nonideal data.

CONCLUSION

The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented. Magn Reson Med 76:1455-1468, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Physics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI)

In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher order diffusion model that is a straightforward extension of the DTI model. Here, we analyze the physics underlying this method, we report our MRI acquisition protocol with the preprocessing pipeline used and the DKI parametric maps obtained on a 1.5 T scanner, and we review the most relevant clinical applications of this technique in various neurological diseases.

Differentiation among parkinsonisms using quantitative diffusion kurtosis imaging

Differential diagnoses among Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy syndrome (PSPS) are often difficult. Hence, we investigated whether diffusion kurtosis imaging (DKI) could detect pathological changes that occur in these disorders and be used to differentiate between such patients. Fourteen patients (five with PD, four MSA, and five PSPS) and six healthy controls were examined using a 1.5-T scanner. Mean kurtosis (MK), fractional anisotropy, and mean diffusivity maps were generated, and these values of the midbrain tegmentum (MBT) and pontine crossing tract (PCT), as well as MBT/PCT ratios, were obtained. We found no significant differences in MBT and PCT values on DKI maps among the groups. In contrast, MBT/PCT ratios from MK maps were significantly increased in the MSA group and decreased in the PSPS group compared with the other groups. MBT/PCT ratios from mean diffusivity maps showed a significant increase in the PSPS group. Therefore, quantitative DKI analyses, particularly the MBT/PCT ratio from MK maps, can differentiate patients with parkinsonisms.

Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice

Technologic advances enable performance of diffusion-weighted imaging (DWI) at ultrahigh b-values, where standard monoexponential model analysis may not apply. Rather, non-Gaussian water diffusion properties emerge, which in cellular tissues are, in part, influenced by the intracellular environment that is not well evaluated by conventional DWI. The novel technique, diffusion kurtosis imaging (DKI), enables characterization of non-Gaussian water diffusion behavior. More advanced mathematical curve fitting of the signal intensity decay curve using the DKI model provides an additional parameter Kapp that presumably reflects heterogeneity and irregularity of cellular microstructure, as well as the amount of interfaces within cellular tissues. Although largely applied for neural applications over the past decade, a small number of studies have recently explored DKI outside the brain. The most investigated organ is the prostate, with preliminary studies suggesting improved tumor detection and grading using DKI. Although still largely in the research phase, DKI is being explored in wider clinical settings. When assessing extracranial applications of DKI, careful attention to details with which body radiologists may currently be unfamiliar is important to ensure reliable results. Accordingly, a robust understanding of DKI is necessary for radiologists to better understand the meaning of DKI-derived metrics in the context of different tumors and how these metrics vary between tumor types and in response to treatment. In this review, we outline DKI principles, propose biostructural basis for observations, provide a comparison with standard monoexponential fitting and the apparent diffusion coefficient, report on extracranial clinical investigations to date, and recommend technical considerations for implementation in body imaging.

Detection of acute nervous system injury with advanced diffusion-weighted MRI: a simulation and sensitivity analysis

Diffusion-weighted imaging (DWI) is a powerful tool to investigate the microscopic structure of the central nervous system (CNS). Diffusion tensor imaging (DTI), a common model of the DWI signal, has a demonstrated sensitivity to detect microscopic changes as a result of injury or disease. However, DTI and other similar models have inherent limitations that reduce their specificity for certain pathological features, particularly in tissues with complex fiber arrangements. Methods such as double pulsed field gradient (dPFG) and q-vector magic angle spinning (qMAS) have been proposed to specifically probe the underlying microscopic anisotropy without interference from the macroscopic tissue organization. This is particularly important for the study of acute injury, where abrupt changes in the microscopic morphology of axons and dendrites manifest as focal enlargements known as beading. The purpose of this work was to assess the relative sensitivity of DWI measures to beading in the context of macroscopic fiber organization and edema. Computational simulations of DWI experiments in normal and beaded axons demonstrated that, although DWI models can be highly specific for the simulated pathologies of beading and volume fraction changes in coherent fiber pathways, their sensitivity to a single idealized pathology is considerably reduced in crossing and dispersed fibers. However, dPFG and qMAS have a high sensitivity for beading, even in complex fiber tracts. Moreover, in tissues with coherent arrangements, such as the spinal cord or nerve fibers in which tract orientation is known a priori, a specific dPFG sequence variant decreases the effects of edema and improves specificity for beading. Collectively, the simulation results demonstrate that advanced DWI methods, particularly those which sample diffusion along multiple directions within a single acquisition, have improved sensitivity to acute axonal injury over conventional DTI metrics and hold promise for more informative clinical diagnostic use in CNS injury evaluation.

Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination

Although MRI is the gold standard for the diagnosis and monitoring of multiple sclerosis (MS), current conventional MRI techniques often fail to detect cortical alterations and provide little information about gliosis, axonal damage and myelin status of lesioned areas. Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) provide sensitive and complementary measures of the neural tissue microstructure. Additionally, specific white matter tract integrity (WMTI) metrics modelling the diffusion in white matter were recently derived. In the current study we used the well-characterized cuprizone mouse model of central nervous system demyelination to assess the temporal evolution of diffusion tensor (DT), diffusion kurtosis tensor (DK) and WMTI-derived metrics following acute inflammatory demyelination and spontaneous remyelination. While DT-derived metrics were unable to detect cuprizone induced cortical alterations, the mean kurtosis (MK) and radial kurtosis (RK) were found decreased under cuprizone administration, as compared to age-matched controls, in both the motor and somatosensory cortices. The MK remained decreased in the motor cortices at the end of the recovery period, reflecting long lasting impairment of myelination. In white matter, DT, DK and WMTI-derived metrics enabled the detection of cuprizone induced changes differentially according to the stage and the severity of the lesion. More specifically, the MK, the RK and the axonal water fraction (AWF) were the most sensitive for the detection of cuprizone induced changes in the genu of the corpus callosum, a region less affected by cuprizone administration. Additionally, microgliosis was associated with an increase of MK and RK during the acute inflammatory demyelination phase. In regions undergoing severe demyelination, namely the body and splenium of the corpus callosum, DT-derived metrics, notably the mean diffusion (MD) and radial diffusion (RD), were among the best discriminators between cuprizone and control groups, hence highlighting their ability to detect both acute and long lasting changes. Interestingly, WMTI-derived metrics showed the aptitude to distinguish between the different stages of the disease. Both the intra-axonal diffusivity (Da) and the AWF were found to be decreased in the cuprizone treated group, Da specifically decreased during the acute inflammatory demyelinating phase whereas the AWF decrease was associated to the spontaneous remyelination and the recovery period. Altogether our results demonstrate that DKI is sensitive to alterations of cortical areas and provides, along with WMTI metrics, information that is complementary to DT-derived metrics for the characterization of demyelination in both white and grey matter and subsequent inflammatory processes associated with a demyelinating event.

Hypomyelinating disorders: An MRI approach

In recent years, the concept of hypomyelinating disorders has been proposed as a group of disorders with varying systemic manifestations that are identified by MR findings of absence or near absence of the T2 hypointensity that develops in white matter as a result of myelination. Initially proposed as a separate group because they were the largest single category of undiagnosed leukodystrophies, their separation as a distinct group that can be recognized by looking for a specific MRI feature has resulted in a marked increase in their diagnosis and a better understanding of the different causes of hypomyelination. This review will discuss the clinical presentations, imaging findings on standard MRI, and new MRI-related techniques that allow a better understanding of these disorders and proposed methods for quantifying the myelination as a potential means of assessing disease course and the effects of proposed treatments. Disorders with hypomyelination of white matter, or hypomyelinating disorders (HMDs), represent the single largest category among undiagnosed genetic leukoencephalopathies (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010). This group of inborn errors of metabolism is characterized by a magnetic resonance imaging (MRI) appearance of reduced or absent myelin development: delay in the development of T2 hypointensity and, often, T1 hyperintensity in the white matter of the brain. The concept of hypomyelination was first conceptualized by (Schiffmann and van der Knaap, 2009; Steenweg et al., 2010; Schiffmann et al., 1994) in a series of papers that showed that these MRI characteristics were easily recognized, were different from the MRI characteristics of dysmyelinating and demyelinating disorders, and that the combination of these imaging findings with specific other clinical and imaging features could be used to make diagnoses with some confidence. In this manuscript, we will discuss the physiologic and genetic bases of hypomyelinating disorders, as well as their classification, clinical manifestations and imaging characteristics.

Detection of changes in the periaqueductal gray matter of patients with episodic migraine using quantitative diffusion kurtosis imaging: preliminary findings

INTRODUCTION

The periaqueductal gray matter (PAG) is considered to play an important role in generating migraine, but findings from imaging studies remain unclear. Therefore, we investigated whether diffusion kurtosis imaging (DKI) can detect changes in the PAG of migraine patients.

METHODS

We obtained source images for DKI from 20 patients with episodic migraine and 20 healthy controls using a 3 T magnetic resonance imaging scanner. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) maps were generated, and the values of the PAG and other deep gray and white matter structures were automatically measured using an atlas-based region-of-interest analysis. The metrics of these structures were compared between the patients and controls.

RESULTS

The MK and MD values of the PAG were significantly increased in the migraine patients compared with the controls (p < 0.05). The FA values were not significantly different. There were no significant differences in the metrics of the other structures between the patients and controls. The MK values of the PAG were significantly positively correlated with both age and the untreated period in the patient group under univariate analysis (r = 0.53 and 0.56, respectively; p < 0.05) but not multivariate analysis.

CONCLUSIONS

DKI detected significant increases in the MK and MD values of the PAG in patients with migraine, which suggests that structural changes in the PAG are associated with the pathophysiological mechanisms of migraine.

Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis

Objectives

A new method that can estimate diffusional kurtosis image (DKI), estimated DKI (eDKI), parallel and perpendicular to neuronal fibres from greatly limited image data was designed to enable quick and practical assessment of DKI in clinics. The purpose of this study was to discuss the potential of this method for clinical use.

Methods

Fourteen healthy volunteers were examined with a 3-Tesla MRI. The diffusion-weighting parameters included five different b-values (0, 500, 1,500, 2,000 and 2,500 s/mm²) with 64 different encoding directions for each of the b-values. K values were calculated by both conventional DKI (convDKI) and eDKI from these complete data, and also from the data that the encoding directions were abstracted to 32, 21, 15, 12 and 6. Error-pixel ratio and the root mean square error (RMSE) compared with the standard were compared between the methods (Wilcoxon signed-rank test: P < 0.05 was considered significant).

Results

Error-pixel ratio was smaller in eDKI than in convDKI and the difference was significant. In addition, RMSE was significantly smaller in eDKI than in convDKI, or otherwise the differences were not significant when they were obtained from the same data set.

Conclusion

eDKI might be useful for assessing DKI in clinical settings.

Key Points

• A method to practically estimate axial/radial DKI from limited data was developed.

• The high robustness of the proposed method can greatly improve map images.

• The accuracy of the proposed method was high.

• Axial/radial K maps can be calculated from limited diffusion-encoding directions.

• The proposed method might be useful for assessing DKI in clinical settings.

Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping

The purpose of this work was to investigate the effects of hemispheric location, gender and age on susceptibility value, as well as the association between susceptibility value and diffusional metrics, in deep gray matter. Iron content was estimated in vivo using quantitative susceptibility mapping. Microstructure was probed using diffusional kurtosis imaging. Regional susceptibility and diffusional metrics were measured for the putamen, caudate nucleus, globus pallidus, thalamus, substantia nigra and red nucleus in 42 healthy adults (age range 25-78 years). Susceptibility value was significantly higher in the left than the right side of the caudate nucleus (P = 0.043) and substantia nigra (P < 0.001). Women exhibited lower susceptibility values than men in the thalamus (P < 0.001) and red nucleus (P = 0.032). Significant age-related increases of susceptibility were observed in the putamen (P < 0.001), red nucleus (P < 0.001), substantia nigra (P = 0.004), caudate nucleus (P < 0.001) and globus pallidus (P = 0.017). The putamen exhibited the highest rate of iron accumulation with aging (slope of linear regression = 0.73 × 10(-3) ppm/year), which was nearly twice those in substantia nigra (slope = 0.40 × 10(-3) ppm/year) and caudate nucleus (slope = 0.39 × 10(-3) ppm/year). Significant positive correlations between the susceptibility value and diffusion measurements were observed for fractional anisotropy (P = 0.045) and mean kurtosis (P = 0.048) in the putamen without controlling for age. Neither correlation was significant after controlling for age. Hemisphere, gender and age-related differences in iron measurements were observed in deep gray matter. Notably, the putamen exhibited the highest rate of increase in susceptibility with aging. Correlations between susceptibility value and microstructural measurements were inconclusive. These findings could provide new clues for unveiling mechanisms underlying iron-related neurodegenerative diseases.

Optimization of white matter fiber tractography with diffusional kurtosis imaging

Diffusional kurtosis imaging (DKI) is a clinically feasible diffusion MRI technique for white matter (WM) fiber tractography (FT) with the ability to directly resolve intra-voxel crossing fibers by means of the kurtosis diffusion orientation distribution function (dODF). Here we expand on previous work by exploring properties of the kurtosis dODF and their subsequent effects on WM FT for in vivo human data. For comparison, the results are contrasted with fiber bundle orientation estimates provided by the diffusion tensor, which is the primary quantity obtained from diffusion tensor imaging. We also outline an efficient method for performing DKI-based WM FT that can substantially decrease the computational requirements. The recommended method for implementing the kurtosis ODF is demonstrated to optimize the reproducibility and sensitivity of DKI for detecting crossing fibers while reducing the occurrence of non-physically-meaningful, negative values in the kurtosis dODF approximation. In addition, DKI-based WM FT is illustrated for different protocols differing in image acquisition times from 48 to 5.3 min.

Fiber ball imaging

By modeling axons as thin cylinders, it is shown that the inverse Funk transform of the diffusion MRI (dMRI) signal intensity obtained on a spherical shell in q-space gives an estimate for a fiber orientation density function (fODF), where the accuracy improves with increasing b-value provided the signal-to-noise ratio is sufficient. The method is similar to q-ball imaging, except that the Funk transform of q-ball imaging is replaced by its inverse. We call this new approach fiber ball imaging. The fiber ball method is demonstrated for healthy human brain, and fODF estimates are compared to diffusion orientation distribution function (dODF) approximations obtained with q-ball imaging. The fODFs are seen to have sharper features than the dODFs, reflecting an enhancement of the higher degree angular frequencies. The inverse Funk transform of the dMRI signal intensity data provides a simple and direct method of estimating a fODF. In addition, fiber ball imaging leads to an estimate for the ratio of the fraction of MRI visible water confined to the intra-axonal space divided by the square root of the intra-axonal diffusivity. This technique may be useful for white matter fiber tractography, as well as other types of microstructural modeling of brain tissue.

Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

BACKGROUND

We assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas.

METHODS

In this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients.

RESULTS

Statistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%.

CONCLUSIONS

Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Evaluation of diffusion kurtosis imaging in ex vivo hypomyelinated mouse brains

Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and DKI-derived white matter tract integrity metrics (WMTI) were experimentally evaluated ex vivo through comparisons to histological measurements and established magnetic resonance imaging (MRI) measures of myelin in two knockout mouse models with varying degrees of hypomyelination. DKI metrics of mean and radial kurtosis were found to be better indicators of myelin content than conventional DTI metrics. The biophysical WMTI model based on the DKI framework reported on axon water fraction with good accuracy in cases with near normal axon density, but did not provide additional specificity to myelination. Overall, DKI provided additional information regarding white matter microstructure compared with DTI, making it an attractive method for future assessments of white matter development and pathology.

Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects

It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.

Apparent diffusion coefficient-dependent voxelwise computed diffusion-weighted imaging: An approach for improving SNR and reducing T2 shine-through effects

PURPOSE

To introduce and evaluate a method for signal-to-noise ratio (SNR) improvement and T2 shine-through effect reduction in diffusion-weighted magnetic resonance imaging (DWI).

MATERIALS AND METHODS

The proposed method uses quantitative information given by the voxel apparent diffusion coefficient (ADC) to derive voxelwise-computed DWI (vcDWI). Behavior of signal intensity variations was simulated and correlated with measurements using a dedicated phantom for DWI allowing for independent adjustment of T2 -relaxivity and diffusivity. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were measured and compared to the method of computed DWI (cDWI). Image signal was correlated with ADCs to appreciate the extent of T2 shine-through effects. Additionally, the proposed method was retrospectively applied to whole-body DWI data of 20 patients with metastatic malignancies. vcDWI was compared to cDWI and measured DWI with respect to image quality, lesion detectability, and lesion diffusivity assessment.

RESULTS

Theoretically predicted signal intensity variations showed a high correlation with measured phantom data (r > 0.96). The proposed method yielded lower background signal intensity variation and higher contrast (+144%) and CNR (+358%) for diffusion-restricted phantom compartments than cDWI. Signal intensities of vcDWI showed an increased inverse correlation with phantom ADC values compared to cDWI (r = -0.86 vs. r = -0.73). Application to patient data showed higher image quality (P < 0.001) and lesion detectability (P = 0.011) using vcDWI compared to cDWI, and higher confidence for the correct identification of diffusion-restricted lesions compared to measured DWI (80/80 vs. 60/81; P = 0.013).

CONCLUSION

vcDWI is a promising approach for the reduction of T2 shine-through effects and improvement of SNR and CNR in DWI. The clinical significance of these improvements, especially regarding lesion detection, needs to be evaluated in larger prospective clinical studies.

N-acetyl-aspartate levels correlate with intra-axonal compartment parameters from diffusion MRI

Diffusion MRI combined with biophysical modeling allows for the description of a white matter (WM) fiber bundle in terms of compartment specific white matter tract integrity (WMTI) metrics, which include intra-axonal diffusivity (Daxon), extra-axonal axial diffusivity (De||), extra-axonal radial diffusivity (De┴), axonal water fraction (AWF), and tortuosity (α) of extra-axonal space. Here we derive these parameters from diffusion kurtosis imaging to examine their relationship to concentrations of global WM N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho) and myo-Inositol (mI), as measured with proton MR spectroscopy ((1)H-MRS), in a cohort of 25 patients with mild traumatic brain injury (MTBI). We found statistically significant (p<0.05) positive correlations between NAA and Daxon, AWF, α, and fractional anisotropy; negative correlations between NAA and De,┴ and the overall radial diffusivity (D┴). These correlations were supported by similar findings in regional analysis of the genu and splenium of the corpus callosum. Furthermore, a positive correlation in global WM was noted between Daxon and Cr, as well as a positive correlation between De|| and Cho, and a positive trend between De|| and mI. The specific correlations between NAA, an endogenous probe of the neuronal intracellular space, and WMTI metrics related to the intra-axonal space, combined with the specific correlations of De|| with mI and Cho, both predominantly present extra-axonally, corroborate the overarching assumption of many advanced modeling approaches that diffusion imaging can disentangle between the intra- and extra-axonal compartments in WM fiber bundles. Our findings are also generally consistent with what is known about the pathophysiology of MTBI, which appears to involve both intra-axonal injury (as reflected by a positive trend between NAA and Daxon) as well as axonal shrinkage, demyelination, degeneration, and/or loss (as reflected by correlations between NAA and De┴, AWF, and α).