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. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of image sensitivity between conventional tensor‐based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke

Diffusion kurtosis imaging (DKI) can offer a useful complementary tool to routine diffusion MRI for improved stratification of heterogeneous tissue damage in acute ischemic stroke. However, its relatively long imaging time has hampered its clinical application in the emergency setting. A recently proposed fast DKI approach substantially shortens the imaging time, which may help to overcome the scan time limitation. However, to date, the sensitivity of the fast DKI protocol for the imaging of acute stroke has not been fully described. In this study, we performed routine and fast DKI scans in a rodent model of acute stroke, and compared the sensitivity of diffusivity and kurtosis indices (i.e. axial, radial and mean) in depicting acute ischemic lesions. In addition, we analyzed the contrast‐to‐noise ratio (CNR) between the ipsilateral ischemic and contralateral normal regions using both conventional and fast DKI methods. We found that the mean kurtosis shows a relative change of 47.1 ± 7.3% between the ischemic and contralateral normal regions, being the most sensitive parameter in revealing acute ischemic injury. The two DKI methods yielded highly correlated diffusivity and kurtosis measures and lesion volumes (R² ? 0.90, p < 0.01). Importantly, the fast DKI method exhibited significantly higher CNR of mean kurtosis (1.6 ± 0.2) compared with the routine tensor protocol (1.3 ± 0.2, p < 0.05), with its CNR per unit time (CNR efficiency) approximately doubled when the scan time was taken into account. In conclusion, the fast DKI method provides excellent sensitivity and efficiency to image acute ischemic tissue damage, which is essential for image‐guided and individualized stroke treatment. Copyright © 2016 John Wiley & Sons, Ltd.     

Measuring healthy female nulliparous pubovisceral muscle from diffusion kurtosis imaging

This study explores the feasibility of using diffusion kurtosis imaging (DKI) in the pelvic floor region and assesses the water diffusivity of the pubovisceral muscle. Twenty-seven healthy young nulliparous females underwent DKI at 3.0 T that included 15 gradient directions and three b values (0, 750, and 1500 s/mm²). The diffusion tensor and diffusion kurtosis metrics values of the pubovisceral muscle were measured after image processing. Two independent sample t-tests, a paired-samples t-test, and a nonparametric hypothesis test were performed as appropriate to compare the differences among different metrics. Twenty-six subjects (mean ± standard deviation age, 25 ± 2 years) were successfully analyzed by measuring the diffusion tensor and diffusion kurtosis metrics of the bilateral pubovisceral muscles. The metrics included mean kurtosis, axial kurtosis, radial kurtosis, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. We found no statistically significant differences for these measurement values between the left and right pubovisceral muscles (p = 0.271–0.931). However, radial kurtosis was greater than axial kurtosis in both pubovisceral muscles (p < 0.001) and axial diffusivity was lower than radial diffusivity in both pubovisceral muscles (p < 0.001). We deem the application of DKI technology to the pelvic floor region to be feasible.     

White matter organization in relation to upper limb motor control in healthy subjects: exploring the added value of diffusion kurtosis imaging

Diffusion tensor imaging (DTI) characterizes white matter (WM) microstructure. In many brain regions, however, the assumption that the diffusion probability distribution is Gaussian may be invalid, even at low b values. Recently, diffusion kurtosis imaging (DKI) was suggested to more accurately estimate this distribution. We explored the added value of DKI in studying the relation between WM microstructure and upper limb coordination in healthy controls (N = 24). Performance on a complex bimanual tracking task was studied with respect to the conventional DTI measures (DKI or DTI derived) and kurtosis metrics of WM tracts/regions carrying efferent (motor) output from the brain, corpus callosum (CC) substructures and whole brain WM. For both estimation models, motor performance was associated with fractional anisotropy (FA) of the CC-genu, CC-body, the anterior limb of the internal capsule, and whole brain WM (r _s range 0.42–0.63). Although DKI revealed higher mean, radial and axial diffusivity and lower FA than DTI (p < 0.001), the correlation coefficients were comparable. Finally, better motor performance was associated with increased mean and radial kurtosis and kurtosis anisotropy (r _s range 0.43–0.55). In conclusion, DKI provided additional information, but did not show increased sensitivity to detect relations between WM microstructure and bimanual performance in healthy controls.     

Validation of fast diffusion kurtosis MRI for imaging acute ischemia in a rodent model of stroke

Diffusion‐weighted imaging (DWI) captures ischemic tissue that is likely to infarct, and has become one of the most widely used acute stroke imaging techniques. Diffusion kurtosis imaging (DKI) has lately been postulated as a complementary MRI method to stratify the heterogeneously damaged DWI lesion. However, the conventional DKI acquisition time is relatively long, limiting its use in the acute stroke setting. Recently, a fast kurtosis mapping method has been demonstrated in fixed brains and control subjects. The fast DKI approach provides mean diffusion and kurtosis measurements under substantially reduced scan time, making it amenable to acute stroke imaging. Because it is not practical to obtain and compare different means of DKI to test whether the fast DKI method can reliably detect diffusion and kurtosis lesions in acute stroke patients, our study investigated its diagnostic value using an animal model of acute stroke, a critical step before fast DKI acquisition can be routinely applied in the acute stroke setting. We found significant correlation, per voxel, between the diffusion and kurtosis coefficients measured using the fast and conventional DKI protocols. In acute stroke rats, the two DKI methods yielded diffusion and kurtosis lesions that were in good agreement. Importantly, substantial kurtosis–diffusion lesion mismatch was observed using the conventional (26 ± 13%, P < 0.01) and fast DKI methods (23 ± 8%, P < 0.01). In addition, regression analysis showed that the kurtosis–diffusion lesion mismatches obtained using conventional and fast DKI methods were substantially correlated (R² = 0.57, P = 0.02). Our results confirmed that the recently proposed fast DKI method is capable of capturing heterogeneous diffusion and kurtosis lesions in acute ischemic stroke, and thus is suitable for translational applications in the acute stroke clinical setting. Copyright © 2014 John Wiley & Sons, Ltd.     

Tensor estimation for double‐pulsed diffusional kurtosis imaging

Double‐pulsed diffusional kurtosis imaging (DP‐DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six‐dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP‐DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP‐DKI is replacing the three‐dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP‐DKI. In this way, the 6D diffusion and kurtosis tensors for DP‐DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well‐defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP‐DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor‐derived rotational invariants are presented.     

Diffusion kurtosis imaging and log‐normal distribution function imaging enhance the visualisation of lesions in animal stroke models

In this work, we report a case study of a stroke model in animals using two methods of quantification of the deviations from Gaussian behaviour: diffusion kurtosis imaging (DKI) and log‐normal distribution function imaging (LNDFI). The affected regions were predominantly in grey rather than in white matter. The parameter maps were constructed for metrics quantifying the apparent diffusivity (evaluated from conventional diffusion tensor imaging, DKI and LNDFI) and for those quantifying the degree of deviations (mean kurtosis and a parameter σ characterising the width of the distribution). We showed that both DKI and LNDFI were able to dramatically enhance the visualisation of ischaemic lesions in comparison with conventional methods. The largest relative change in the affected versus healthy regions was observed in the mean kurtosis values. The average changes in the mean kurtosis and σ values in the lesions were a factor of two to three larger than the relative changes observed in the mean diffusivity. In conclusion, the applied methods promise valuable perspectives in the assessment of stroke. Copyright © 2012 John Wiley & Sons, Ltd.     

Diffusional kurtosis MRI of the lower leg: changes caused by passive muscle elongation and shortening

Diffusional kurtosis MRI (DKI) quantifies the deviation of water diffusion from a Gaussian distribution. We investigated the influence of passive elongation and shortening of the lower leg muscles on the DKI parameters D (diffusion coefficient) and K (kurtosis). After approval by the local ethics committee, eight healthy volunteers (age, 29.1 ± 2.9 years) underwent MRI of the lower leg at 3 T. Diffusion‐weighted images were acquired with 10 different b values at three ankle positions (passive dorsiflexion 10°, neutral position 0°, passive plantar flexion 40°). Parametrical maps of D and K were obtained by voxel‐wise fitting of the signal intensities using a non‐linear Levenberg–Marquardt algorithm. D and K were measured in the tibialis anterior, medial and lateral gastrocnemius, and soleus muscles. In the neutral position, D and K values were in the range between 1.66–1.79 × 10^–3 mm²/s and 0.21–0.39, respectively. D and K increased with passive shortening, and decreased with passive elongation, which could also be illustrated on the parametrical maps. In dorsiflexion, D (p < 0.01) and K (p = 0.036) were higher in the tibialis anterior than in the medial gastrocnemius. In plantar flexion, the opposite was found for K (p = 0.035). DKI parameters in the lower leg muscles are significantly influenced by the ankle joint position, indicating that the diffusion of water molecules in skeletal muscle deviates from a Gaussian distribution depending on muscle tonus. Copyright © 2016 John Wiley & Sons, Ltd.     

Fast diffusion kurtosis imaging of fibrotic mouse kidneys

Diffusion kurtosis imaging (DKI) is sensitive to tissue microstructure and may therefore be useful in the diagnosis and monitoring of disease in brain and body organs. Generally, diffusion magnetic resonance imaging (dMRI) in the body is challenging because of the heterogeneous body composition, which can cause image artefacts as a result of chemical shifts and susceptibility differences. In addition, the abdomen possesses physiological factors (e.g. breathing, heartbeat, blood flow) which may severely reduce image quality, especially when echo planar imaging is employed, as is typical in dMRI. Collectively, these challenging measurement conditions impede the use and exploration of DKI in the body. This impediment is further exacerbated by the traditionally large amount of data required for DKI and the low signal‐to‐noise ratio at the b‐values needed to effectively probe the kurtosis regime. Recently introduced fast DKI techniques reduce the challenge of DKI in the body by decreasing the data requirement substantially, so that, for example, triggering and breath‐hold techniques may be applied for the entire DKI acquisition without causing unfeasible scan times. One common pathological condition for which body DKI may be of immediate clinical value is kidney fibrosis, which causes progressive changes in organ microstructure. With its sensitivity to microstructure, DKI is an obvious candidate for a non‐invasive evaluation method. We present preclinical evidence indicating that the rapidly obtainable tensor‐derived mean kurtosis ( ) distinguishes moderately fibrotic kidneys from healthy controls. The presence and degree of fibrosis are confirmed by histology, which also indicates fibrosis as the main driver behind the DKI differences observed between groups. We therefore conclude that fast kurtosis is a likely candidate for an MRI‐based method for the detection and monitoring of renal fibrosis. We provide protocol recommendations for fast renal DKI in humans based on a b‐value optimisation performed using data acquired at 3 T in normal human kidney.     

Diffusion abnormalities in temporal lobes of children with temporal lobe epilepsy: a preliminary diffusional kurtosis imaging study and comparison with diffusion tensor imaging

In this preliminary study, we aimed to investigate the abnormalities of water diffusion in children with temporal lobe epilepsy (TLE). Eight children with unilateral TLE (according to electroencephalography, EEG) and eight age‐ and sex‐matched controls were recruited. Diffusion tensor imaging (DTI)/diffusional kurtosis imaging (DKI) acquisitions were performed. Radial diffusivity (λ_⊥), axial diffusivity (λ_∥), mean diffusivity (MD) and fractional anisotropy (FA) maps were calculated for both DTI and DKI, and radial kurtosis (K_⊥), axial kurtosis (K_∥) and mean kurtosis (MK) maps were calculated for DKI only. Mann–Whitney test showed that, for white matter in the temporal lobe, DKI‐derived λ_∥, MD and K_∥ were significantly different in bilateral temporal lobes and EEG‐abnormal and EEG‐normal sides of the temporal lobe between patients and controls, whereas DTI showed no abnormalities. For gray matter, DKI detected significantly higher MD and MK in the same three comparisons, whereas DTI detected abnormalities only in the comparison between bilateral temporal lobes and between EEG‐normal sides in cases and left–right matched sides in controls. No significant difference was observed between EEG‐abnormal and EEG‐normal sides in cases. These preliminary results indicate that DKI is more sensitive than DTI for the detection of diffusion abnormalities in the temporal lobes of children with TLE, even when EEG signals are normal. These findings pave the way for the application of DKI for in‐depth studies on TLE in children. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Characterisation of brain microstructural alterations in children with obstructive sleep apnea syndrome using diffusion kurtosis imaging

Obstructive sleep apnea (OSA) is a common chronic sleep-related breathing disorder in children. Previous studies showed widespread alterations in white matter (WM) in children with OSA mainly by using diffusion tensor imaging (DTI), while diffusional kurtosis imaging (DKI) extended DTI and exhibited improved sensitivity in detecting developmental and pathological changes in neural tissues. Therefore, we conducted whole-brain DTI and DKI analyses and compared the differences in kurtosis and diffusion parameters within the skeleton between 41 children with OSA and 32 healthy children. Between-group differences were evaluated by tract-based spatial statistics (TBSS) analysis (p < 0.05, TFCE corrected), and partial correlations between DKI metrics and sleep parameters were assessed considering age and gender as covariates. Compared with the controls, children with OSA showed significantly decreased kurtosis fractional anisotropy (KFA) mainly in white matter regions with a complex fibre arrangement including the posterior corona radiate (PCR), superior longitudinal fasciculus (SLF), and inferior fronto-occipital fasciculus (IFOF), while decreased FA in white matter regions with a coherent fibre arrangement including the posterior limb of internal capsule (PLIC), anterior thalamic radiation (ATR), and corpus callosum (CC). Notably, the receiver operating characteristic (ROC) curve analysis demonstrated the KFA value in complex tissue regions significantly (p < 0.001) differentiated children with OSA from the controls. In addition, the KFA value in the left PCR, SLF, and IFOF showed significant partial correlations to the sleep parameters for children with OSA. Combining DKI derived kurtosis and diffusion parameters can provide complementary neuroimaging biomarkers for assessing white matter alterations, and reveal pathological changes and monitor disease progression in paediatric OSA.     

Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T

Artificial neural networks (ANNs) were used for voxel‐wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least‐squares regression (LSR) and state‐of‐the‐art multi‐step fitting (LSR‐MS) in Monte‐Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR‐MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo‐diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR‐MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR‐MS, 19%; LSR, 8%), D* (ANN, 21%; LSR‐MS, 25%; LSR, 23%) and K (ANN, 0%; LSR‐MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR‐MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state‐of‐the‐art method LSR‐MS with several advantages in the estimation of IVIM–kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times.     

磁共振扩散峰度成像研究进展及新应用

扩散峰度成像(DKI)是一种新兴的扩散磁共振技术,它在传统扩散张量成像的基础上引入了四阶峰度,并以此量化组织中水分子扩散位移概率分布偏离高斯分布的程度,其附加的峰度信息对大脑组织的微观结构更敏感。从扩散峰度成像模型、数据采集参数、模型拟合以及由DKI发展而来的微观结构模型等方面,介绍DKI模型的研究进展和临床应用。最后简要讨论DKI模型存在的问题,并展望其在神经放射学各个方面所具有的广泛深远影响。     

Fast diffusion kurtosis imaging (DKI) with Inherent COrrelation‐based Normalization (ICON) enhances automatic segmentation of heterogeneous diffusion MRI lesion in acute stroke

Diffusion kurtosis imaging (DKI) has been shown to augment diffusion‐weighted imaging (DWI) for the definition of irreversible ischemic injury. However, the complexity of cerebral structure/composition makes the kurtosis map heterogeneous, limiting the specificity of kurtosis hyperintensity to acute ischemia. We propose an Inherent COrrelation‐based Normalization (ICON) analysis to suppress the intrinsic kurtosis heterogeneity for improved characterization of heterogeneous ischemic tissue injury. Fast DKI and relaxation measurements were performed on normal (n = 10) and stroke rats following middle cerebral artery occlusion (MCAO) (n = 20). We evaluated the correlations between mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) derived from the fast DKI sequence and relaxation rates R₁ and R₂, and found a highly significant correlation between MK and R₁ (p < 0.001). We showed that ICON analysis suppressed the intrinsic kurtosis heterogeneity in normal cerebral tissue, enabling automated tissue segmentation in an animal stroke model. We found significantly different kurtosis and diffusivity lesion volumes: 147 ± 59 and 180 ± 66 mm³, respectively (p = 0.003, paired t‐test). The ratio of kurtosis to diffusivity lesion volume was 84% ± 19% (p < 0.001, one‐sample t‐test). We found that relaxation‐normalized MK (RNMK), but not MD, values were significantly different between kurtosis and diffusivity lesions (p < 0.001, analysis of variance). Our study showed that fast DKI with ICON analysis provides a promising means of demarcation of heterogeneous DWI stroke lesions.     

Considerations for measuring the fractional anisotropy of metabolites with diffusion tensor spectroscopy

Diffusion tensor spectroscopy of metabolites in brain is challenging because of their lower diffusivity (i.e. less signal attenuation for a given b value) and much poorer signal‐to‐noise ratio relative to water. Although diffusion tensor acquisition protocols have been studied in detail for water, they have not been evaluated systematically for the measurement of the fractional anisotropy of metabolites such as N‐acetylaspartate, creatine and choline in the white and gray matter of human brain. Diffusion tensor spectroscopy was performed in vivo with variable maximal b values (1815 or 5018 s/mm²). Experiments were also performed on simulated spectra and isotropic alcohol phantoms of various diffusivities, ranging from approximately 0.54 × 10^?3 to 0.13 × 10^?3 mm²/s, to assess the sensitivity of diffusion tensor spectroscopic parameters to low diffusivity, noise and b value. The low maximum b value of 1815 s/mm² yielded elevated fractional anisotropy (0.53–0.60) of N‐acetylaspartate in cortical gray matter relative to the more isotropic value (0.25–0.30) obtained with a higher b value of 5018 s/mm²; in contrast, the fractional anisotropy of white matter was consistently anisotropic with the different maximal b values (i.e. 0.43–0.54 for b = 1815 s/mm² and 0.47–0.51 for b = 5018 s/mm²). Simulations, phantoms and in vivo data indicate that greater signal attenuation, to a degree, is desirable for the accurate quantification of diffusion‐weighted spectra for slowly diffusing metabolites. Copyright © 2010 John Wiley & Sons, Ltd.     

MRI quantification of non‐Gaussian water diffusion in normal human kidney: a diffusional kurtosis imaging study

Our aim was to prospectively evaluate the feasibility of diffusional kurtosis imaging (DKI) in normal human kidney and to report preliminary DKI measurements. Institutional review board approval and informed consent were obtained. Forty‐two healthy volunteers underwent diffusion‐weighted imaging (DWI) scans with a 3‐T MR scanner. b values of 0, 500 and 1000 s/mm² were adopted. Maps of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D_⊥), axial diffusivity (D_||), mean kurtosis (MK), radial kurtosis (K_⊥) and axial kurtosis (K_||) were produced. Three representative axial slices in the upper pole, mid‐zone and lower pole were selected in the left and right kidney. On each selected slice, three regions of interest were drawn on the renal cortex and another three on the medulla. Statistical comparison was performed with t‐test and analysis of variance. Thirty‐seven volunteers successfully completed the scans. No statistically significant differences were observed between the left and right kidney for all metrics (p values in the cortex: FA, 0.114; MD, 0.531; D_⊥, 0.576; D_||, 0.691; MK, 0.934; K_⊥, 0.722; K_||, 0.891; p values in the medulla: FA, 0.348; MD, 0.732; D_⊥, 0.470; D_||, 0.289; MK, 0.959; K_⊥, 0.780; K_||, 0.287). Kurtosis metrics (MK, K_||, K_⊥) obtained in the renal medulla were significantly (p <0.001) higher than those in the cortex (0.552 ± 0.04, 0.637 ± 0.07 and 0.530 ± 0.08 in the medulla and 0.373 ± 0.04, 0.492 ± 0.06 and 0.295 ± 0.06 in the cortex, respectively). For the diffusivity measures, FA of the medulla (0.356 ± 0.03) was higher than that of the cortex (0.179 ± 0.03), whereas MD, D_⊥ and D_|| (mm²/ms) were lower in the medulla than in the cortex (3.88 ± 0.09, 3.50 ± 0.23 and 4.65 ± 0.29 in the cortex and 2.88 ± 0.11, 2.32 ± 0.20 and 3.47 ± 0.31 in the medulla, respectively). Our results indicate that DKI is feasible in the human kidney. We have reported the preliminary DKI measurements of normal human kidney that demonstrate well the non‐Gaussian behavior of water diffusion, especially in the renal medulla. Copyright © 2014 John Wiley & Sons, Ltd.     

Histological correlation of diffusional kurtosis and white matter modeling metrics in cuprizone‐induced corpus callosum demyelination

The cuprizone mouse model is well established for studying the processes of both demyelination and remyelination in the corpus callosum, and it has been utilized together with diffusion tensor imaging (DTI) to investigate myelin and axonal pathology. Although some underlying morphological mechanisms contributing to the changes in diffusion tensor (DT) metrics have been identified, the understanding of specific associations between histology and diffusion measures remains limited. Diffusional kurtosis imaging (DKI) is an extension of DTI that provides metrics of diffusional non‐Gaussianity, for which an associated white matter modeling (WMM) method has been developed. The main goal of the present study was to quantitatively assess the relationships between diffusion measures and histological measures in the mouse model of cuprizone‐induced corpus callosum demyelination. The diffusional kurtosis (DK) and WMM metrics were found to provide additional information that enhances the sensitivity to detect the morphological heterogeneity in the chronic phase of the disease process in the rostral segment of the corpus callosum. Specifically, in the rostral segment, axonal water fraction (d = 2.6; p < 0.0001), radial kurtosis (d = 2.0; p = 0.001) and mean kurtosis (d = 1.5; p = 0.005) showed the most sensitivity between groups with respect to yielding statistically significant p values and high Cohen's d values. These results demonstrate the ability of DK and WMM metrics to detect white mater changes and inflammatory processes associated with cuprizone‐induced demyelination. They also validate, in part, the application of these new WMM metrics for studying neurological diseases, as well as helping to elucidate their biophysical meaning. Copyright © 2014 John Wiley & Sons, Ltd.     

Hierarchical non‐negative matrix factorization to characterize brain tumor heterogeneity using multi‐parametric MRI

Tissue characterization in brain tumors and, in particular, in high‐grade gliomas is challenging as a result of the co‐existence of several intra‐tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow‐up of individual patients. Twenty‐four patients with glioma [10 low‐grade gliomas (LGGs), 14 high‐grade gliomas (HGGs)] underwent a multi‐parametric MRI (MP‐MRI) scheme, including conventional MRI (cMRI), perfusion‐weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short‐TE ¹H MRSI. MP‐MRI parameters were derived: T₂, T₁ + contrast, fluid‐attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N‐acetyl‐aspartate (NAA), total choline (Cho), etc. Hierarchical non‐negative matrix factorization (hNMF) was applied to the MP‐MRI parameters, providing tissue characterization on a patient‐by‐patient and voxel‐by‐voxel basis. Tissue‐specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities. Copyright © 2015 John Wiley & Sons, Ltd.     

The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi‐tensor model vs. diffusion orientation distribution function

Diffusion tensor imaging (DTI) has been employed for over 2 decades to noninvasively quantify central nervous system diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of coexisting inflammation, edema and crossing nerve fibers. We employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Furthermore, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q‐sampling imaging (GQI), q‐ball imaging (QBI) and neurite orientation dispersion and density imaging to resolve fiber crossing, in reference to the gold standard angles measured from structural images. DTI‐computed diffusivities and fractional anisotropy were significantly confounded by gel‐mimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionally, DBSI accurately and consistently estimated crossing angles in various conditions of gel‐mimicked edema when compared with the gold standard (r² = 0.92, P = 1.9 × 10^?9, bias = 3.9°). Small crossing angles and edema significantly impact the diffusion orientation distribution function, making DKI, GQI and QBI less accurate in detecting and estimating fiber crossing angles. Lastly, we used diffusion tensor ellipsoids to demonstrate that DBSI resolves the confounds of edema and crossing fibers in the peritumoral edema region from a patient with lung cancer metastasis, while DTI failed. In summary, DBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel‐mimicked edema. DBSI also indicated better angular resolution compared with DKI, QBI and GQI.