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.     

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.     

Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging

Conventional diffusion tensor imaging (DTI) measures water diffusion parameters based on the assumption that the spin displacement distribution is a Gaussian function. However, water movement in biological tissue is often non-Gaussian and this non-Gaussian behavior may contain useful information related to tissue structure and pathophysiology. Here we propose an approach to directly measure the non-Gaussian property of water diffusion, characterized by a four-dimensional matrix referred to as the diffusion kurtosis tensor. This approach does not require the complete measurement of the displacement distribution function and, therefore, is more time efficient compared with the q-space imaging technique. A theoretical framework of the DK calculation is established, and experimental results are presented for humans obtained within a clinically feasible time of about 10 min. The resulting kurtosis maps are shown to be robust and reproducible. Directionally-averaged apparent kurtosis coefficients (AKC, a unitless parameter) are 0.74 +/- 0.03, 1.09 +/- 0.01 and 0.84 +/- 0.02 for gray matter, white matter and thalamus, respectively. The three-dimensional kurtosis angular plots show tissue-specific geometry for different brain regions and demonstrate the potential of identifying multiple fiber structures in a single voxel. Diffusion kurtosis imaging is a useful method to study non-Gaussian diffusion behavior and can provide complementary information to that of DTI.     

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.     

核磁共振弥散峰度成像在脑中风诊断中的应用

目的 研究弥散峰度成像(DKI)对脑组织灰质和白质的描述以及在脑中风诊断中的应用.方法 采用2例正常人以及15例脑中风患者(包含9例脑梗死,急性、亚急性和陈旧性各占3例)的脑部核磁共振图像作为试验数据.针对每例数据各个像素位置,分别对9个不同弥散梯度场下各个信号强度进行非线性拟合得到弥散系数(D值)和弥散峰度(K值),进而做出ADC图和DKI图.观察比较分析各例在ADC图和DKI图的表现.结果 DKI成像具有更多的纹理细节,包含更丰富的组织特征.急性、亚急性脑梗死病灶在DKI图呈现高信号,陈旧性脑梗死病灶在DKI图呈现低信号,平均峰度相对值(rMK)分别为2.193±0.166,1.789±0.162,0.564±0.069.结论 DKI对脑组织尤其是灰质以及一些脑中风病灶的描述相对于ADC图包含更多的组织信息.脑梗死病灶的rMK值具有特征性演变规律.     

MRI of neonatal necrotizing enterocolitis in a rodent model

Neonatal necrotizing enterocolitis (NEC) is a poorly understood life‐threatening illness afflicting premature infants. Research is hampered by the absence of a suitable method to monitor disease progression noninvasively. The primary goal of this research was to test in vivo MRI methods for the noninvasive early detection and staging of inflammation in the ileum of an infant rat model of NEC. Neonatal rats were delivered by cesarean section at embryonic stage of day 20 after the beginning of pregnancy and stressed with formula feeding, hypoxia and bacterial colonization to induce NEC. Naturally born and dam‐fed neonatal rats were used as healthy controls. In vivo MRI studies were performed using a Bruker 9.4‐T scanner to obtain high‐resolution anatomical MR images using both gradient echo and spin echo sequences, pixel‐by‐pixel T₂ maps using a multi‐slice–multi‐echo sequence, and maps of the apparent diffusion coefficient (ADC) of water using a spin echo sequence, to assess the degree of ileal damage. Pups were sacrificed at the end of the MRI experiment on day 2 or 4 for histology. T₂ measured by MRI was increased significantly in the ileal regions of pups with NEC by histology (106.3 ± 6.1 ms) compared with experimentally stressed pups without NEC (85.2 ± 6.8 ms) and nonstressed, control rat pups (64.9 ± 2.3 ms). ADC values measured by diffusion‐weighted MRI were also increased in the ileal regions of pups with NEC by histology [(1.98 ± 0.15) × 10^–3 mm²/s] compared with experimentally stressed pups without NEC [(1.43 ± 0.16) × 10^–3 mm²/s] and nonstressed control pups [(1.10 ± 0.06) × 10^–3 mm²/s]. Both T₂ and ADC values between these groups were found to be significantly different (p < 0.03). The correlation of MRI results with histologic images of the excised ileal tissue samples strongly suggests that MRI can noninvasively identify NEC and assess intestinal injury prior to clinical symptoms in a physiologic rat pup model of NEC. © 2013 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.     

Spatiotemporal microstructural white matter changes in diffusion tensor imaging after transient focal ischemic stroke in rats

Structural reorganization in white matter (WM) after stroke is a potential contributor to substitute or to newly establish the functional field on the injured brain in nature. Diffusion tensor imaging (DTI) is an imaging modality that can be used to evaluate damage and recovery within the brain. This method of imaging allows for in vivo assessment of the restricted movements of water molecules in WM and provides a detailed look at structural connectivity in the brain. For longitudinal DTI studies after a stroke, the conventional region of interest method and voxel‐based analysis are highly dependent on the user‐hypothesis and parameter settings for implementation. In contrast, tract‐based spatial statistics (TBSS) allows for reliable voxel‐wise analysis via the projection of diffusion‐derived parameters onto an alignment‐invariant WM skeleton. In this study, spatiotemporal WM changes were examined with DTI‐derived parameters (fractional anisotropy, FA; mean diffusivity, MD; axial diffusivity, DA; radial diffusivity, RD) using TBSS 2 h to 6 weeks after experimental focal ischemic stroke in rats (N = 6). FA values remained unchanged 2–4 h after the stroke, followed by a continuous decrease in the ipsilesional hemisphere from 24 h to 2 weeks post‐stroke and gradual recovery from the ipsilesional corpus callosum to the external capsule until 6 weeks post‐stroke. In particular, the fibers in these areas were extended toward the striatum of the ischemic boundary region at 6 weeks on tractography. The alterations of the other parameters in the ipsilesional hemisphere showed patterns of a decrease at the early stage, a subsequent pseudo‐normalization of MD and DA, a rapid reduction of RD, and a progressive increase in MD, DA and RD with a decreased extent in the injured area at later stages. The findings of this study may reflect the ongoing processes on tissue damage and spontaneous recovery after stroke.     

Acute renal impairment characterization using diffusion magnetic resonance imaging: Validation by histology

Diffusion magnetic resonance imaging has been demonstrated to be a simple, noninvasive and accurate method for the detection of renal microstructure and microcirculation, which are closely linked to renal function. Moreover, serum endothelin‐1 (ET‐1) was also reported as a good indicator of early renal injury. The aim of this study was to evaluate the feasibility and capability of diffusion MRI and ET‐1 to detect acute kidney injury by an operation simulating high‐pressure renal pelvic perfusion, which is commonly used during ureteroscopic lithotripsy. Histological findings were used as a reference. Fourteen New Zealand rabbits in an experimental group and 14 in a control group were used in this study. Diffusion tensor imaging and intravoxel incoherent motion diffusion‐weighted imaging were acquired by a 3.0 T MRI scanner. Significant corticomedullary differences were found in the values of the apparent diffusion coefficient (ADC), pure tissue diffusion, volume fraction of pseudo‐diffusion (fp) and fractional anisotropy (FA) (P < 0.05 for all) in both preoperation and postoperation experimental groups. Compared with the control group, the values of cortical fp_mean, medullary ADC_mean and FA_mean decreased significantly (P < 0.05) after the operation in the experimental group. Also, the change rate of medullary ADC_mean in the experimental group was more pronounced than that in the control group (P = 0.018). No significant change was found in serum ET‐1 concentration after surgery in either the experimental (P = 0.80) or control (P = 0.17) groups. In the experimental group, histological changes were observed in the medulla, while no visible change was found in the cortex. This study demonstrated the feasibility of diffusion MRI to detect the changes of renal microstructure and microcirculation in acute kidney injury, with the potential to evaluate renal function. Moreover, the sensitivity of diffusion MRI to acute kidney injury appears to be superior to that of serum ET‐1.     

A multi‐Gaussian model for apparent diffusion coefficient histogram analysis of Wilms’ tumour subtype and response to chemotherapy

Wilms’ tumours (WTs) are large heterogeneous tumours, which typically consist of a mixture of histological cell types, together with regions of chemotherapy‐induced regressive change and necrosis. The predominant cell type in a WT is assessed histologically following nephrectomy, and used to assess the tumour subtype and potential risk. The purpose of this study was to develop a mathematical model to identify subregions within WTs with distinct cellular environments in vivo, determined using apparent diffusion coefficient (ADC) values from diffusion‐weighted imaging (DWI). We recorded the WT subtype from the histopathology of 32 tumours resected in patients who received DWI prior to surgery after pre‐operative chemotherapy had been administered. In 23 of these tumours, DWI data were also available prior to chemotherapy. Histograms of ADC values were analysed using a multi‐Gaussian model fitting procedure, which identified ‘subpopulations’ with distinct cellular environments within the tumour volume. The mean and lower quartile ADC values of the predominant viable tissue subpopulation (ADC_1MEAN, ADC_1LQ), together with the same parameters from the entire tumour volume (ADC_0MEAN, ADC_0LQ), were tested as predictors of WT subtype. ADC_1LQ from the multi‐Gaussian model was the most effective parameter for the stratification of WT subtype, with significantly lower values observed in high‐risk blastemal‐type WTs compared with intermediate‐risk stromal, regressive and mixed‐type WTs (p < 0.05). No significant difference in ADC_1LQ was found between blastemal‐type and intermediate‐risk epithelial‐type WTs. The predominant viable tissue subpopulation in every stromal‐type WT underwent a positive shift in ADC_1MEAN after chemotherapy. Our results suggest that our multi‐Gaussian model is a useful tool for differentiating distinct cellular regions within WTs, which helps to identify the predominant histological cell type in the tumour in vivo. This shows potential for improving the risk‐based stratification of patients at an early stage, and for guiding biopsies to target the most malignant part of the tumour. Copyright © 2015 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.     

Fast and accurate initialization of the free‐water imaging model parameters from multi‐shell diffusion MRI

Cerebrospinal fluid partial volume effect is a known bias in the estimation of Diffusion Tensor Imaging (DTI) parameters from diffusion MRI data. The Free‐Water Imaging model for diffusion MRI data adds a second compartment to the DTI model, which explicitly accounts for the signal contribution of extracellular free‐water, such as cerebrospinal fluid. As a result the DTI parameters obtained through the free‐water model are corrected for partial volume effects, and thus better represent tissue microstructure. In addition, the model estimates the fractional volume of free‐water, and can be used to monitor changes in the extracellular space. Under certain assumptions, the model can be estimated from single‐shell diffusion MRI data. However, by using data from multi‐shell diffusion acquisitions, these assumptions can be relaxed, and the fit becomes more robust. Nevertheless, fitting the model to multi‐shell data requires high computational cost, with a non‐linear iterative minimization, which has to be initialized close enough to the global minimum to avoid local minima and to robustly estimate the model parameters. Here we investigate the properties of the main initialization approaches that are currently being used, and suggest new fast approaches to improve the initial estimates of the model parameters. We show that our proposed approaches provide a fast and accurate initial approximation of the model parameters, which is very close to the final solution. We demonstrate that the proposed initializations improve the final outcome of non‐linear model fitting.     

Diffusion‐weighted MRI measurements on stroke patients reveal water‐exchange mechanisms in sub‐acute ischaemic lesions

The aim of this study was to investigate the diffusion time dependence of signal‐versus‐b curves obtained from diffusion‐weighted magnetic resonance imaging (DW‐MRI) of sub‐acute ischaemic lesions in stroke patients. In this case series study, 16 patients with sub‐acute ischaemic stroke were examined with DW‐MRI using two different diffusion times (60 and 260 ms). Nine of these patients showed sufficiently large lesions without artefacts to merit further analysis. The signal‐versus‐b curves from the lesions were plotted and analysed using a two‐compartment model including compartmental exchange. To validate the model and to aid the interpretation of the estimated model parameters, Monte Carlo simulations were performed. In eight cases, the plotted signal‐versus‐b curves, obtained from the lesions, showed a signal–curve split‐up when data for the two diffusion times were compared, revealing effects of compartmental water exchange. For one of the patients, parametric maps were generated based on the extracted model parameters. These novel observations suggest that water exchange between different water pools is measurable and thus potentially useful for clinical assessment. The information can improve the understanding of the relationship between the DW‐MRI signal intensity and the microstructural properties of the lesions. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

扩散峰度成像在预测直肠癌KRAS基因突变中的应用价值

目的 探讨扩散峰度成像(DKI)定量参数在预测直肠癌KRAS基因突变中的应用价值。方法 回顾性分析山西省肿瘤医院2016年11月-2018年6月经病理证实的152例直肠腺癌患者的资料,其中男92例,女60例;年龄33～86岁,平均61岁。患者均于术前行常规MRI和功能DKI序列检查,由2名放射科医师双盲勾画感兴趣区,应用MatLab软件计算两组患者DKI定量参数平均表观扩散系数(MD)、平均峰度(MK)以及表观扩散系数(ADC),并采用组内相关系数进行一致性分析。术后均进行KRAS基因检测,依据检测结果将患者分为野生组和突变组两组,采用独立样本t检验对比两组患者的MD、MK、ADC。绘制DKI定量参数诊断KRAS基因突变的受试者工作特征曲线(ROC) ,根据约登指数确定各定量参数的最佳诊断阈值,以及相应的灵敏度、特异度,并采用DeLong检验比较各定量参数的ROC的曲线下面积(AUC)。结果 152例直肠癌患者中,KRAS基因突变组74例,野生组78例,基因突变率为48.6%(74/152)。突变组患者的ADC、MD、MK值分别为(1.18±0.18)×10^-3 mm²/s、(1.28±0.18)×10^-3 mm²/s、0.97±0.11,野生组分别为(1.33±0.18)×10^-3 mm²/s、(1.42±0.17)×10^-3 mm²/s、0.82±0.09;突变组ADC、MD值均小于野生组患者,而MK值则大于野生组患者,差异均有统计学意义(t=5.424、4.882、-8.809, P值均＜0.01)。ROC曲线显示,ADC、MD、和MK值预测KRAS基因的AUC分别为0.758、0.740、0.845,灵敏度分别为75.7%、82.4%、77.0%,特异度分别为68.0%、57.8%、84.6%。DeLong检验结果显示,MK值的AUC明显高于ADC和MD值(P值均＜0.01),而ADC值和MD值间AUC差异无统计学意义(P＞0.05)。结论 DKI定量参数MD、MK和ADC值,在预测直肠癌的KRAS基因突变方面均有一定的价值,其中MK值有更高的AUC和特异度,有更高的诊断价值。     

Comparison of different MRI sequences in lesion detection and early response evaluation of diffuse large B‐cell lymphoma – a whole‐body MRI and diffusion‐weighted imaging study

To compare different MRI sequences for the detection of lesions and the evaluation of response to chemotherapy in patients with diffuse large B‐cell lymphoma (DLBCL), 18 patients with histology‐confirmed DLBCL underwent 3‐T MRI scanning prior to and 1 week after chemotherapy. The MRI sequences included T₁‐weighted pre‐ and post‐contrast, T₂‐weighted with and without fat suppression, and a single‐shot echo‐planar diffusion‐weighted imaging (DWI) with two b values (0 and 800 s/mm²). Conventional MRI sequence comparisons were performed using the contrast ratio between tumor and normal vertebral body instead of signal intensity. The apparent diffusion coefficient (ADC) of the tumor was measured directly on the parametric ADC map. The tumor volume was used as a reference for the evaluation of chemotherapy response. The mean tumor volume was 374 mL at baseline, and decreased by 65% 1 week after chemotherapy (p < 0.01). The T₂‐weighted image with fat suppression showed a significantly higher contrast ratio compared with images from all other conventional MRI sequences, both before and after treatment (p < 0.01, respectively). The contrast ratio of the T₂‐weighted image with fat suppression decreased significantly (p < 0.01), and that of the T₁‐weighted pre‐contrast image increased significantly (p < 0.01), after treatment. However, there was no correlation between the change in contrast ratio and tumor volume. The mean ADC value was 0.68 × 10^–3 mm²/s at baseline; it increased by 89% after chemotherapy (p < 0.001), and the change in ADC value correlated with the change in tumor volume (r = 0.66, p < 0.01). The baseline ADC value also correlated inversely with the percentage change in ADC after treatment (r = ?0.62, p < 0.01). In conclusion, this study indicates that T₂‐weighted imaging with fat suppression is the best conventional sequence for the detection of lesions and evaluation of the efficacy of chemotherapy in DLBCL. DWI with ADC mapping is an imaging modality with both diagnostic and prognostic value that could complement conventional MRI. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Improvement of accuracy of diffusion MRI using real‐time self‐gated data acquisition

Diffusion‐weighted and diffusion tensor MR imaging (DWI, DTI) techniques are generally performed with signal averaging of multiple measurements to improve the signal‐to‐noise ratio (SNR) and the accuracy of the diffusion measurement. Any discrepancy in the images between different averages causes errors which reduce the accuracy of the diffusion MRI measurements. In this report, a motion artifact reduction scheme with a real‐time self‐gated (RTSG) data acquisition for diffusion MRI using two‐dimensional echo planar imaging (2D EPI) is described. A subject's translational and rotational motions during application of the diffusion gradients induce an additional phase term and a shift of the echo‐peak position in the k‐space, respectively. These motions also reduce the magnitude of the echo‐peak. Based on these properties, we present a new scheme which monitors the position and the magnitude of the largest echo‐peak in the k‐space. The position and the magnitude of each average is compared to those of early averaging shot to determine if the differences are within or beyond the given threshold values. Motion corrupted data are reacquired in real time. Our preliminary results using RTSG indicate an improvement of both SNR and the accuracy of diffusion MRI measurements. Copyright © 2009 John Wiley & Sons, Ltd.     

Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder

Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that results from disruption of multiple neural circuits involved in emotional regulation. Although previous studies using diffusion tensor imaging (DTI) found smaller values of fractional anisotropy (FA) in the white matter, predominantly in the frontal lobe, of patients with MDD, studies using diffusion kurtosis imaging (DKI) are scarce. Here, we used DKI whole‐brain analysis with tract‐based spatial statistics (TBSS) to investigate the brain microstructural abnormalities in MDD. Twenty‐six patients with MDD and 42 age‐ and sex‐matched control subjects were enrolled. To investigate the microstructural pathology underlying the observations in DKI, a compartment model analysis was conducted focusing on the corpus callosum. In TBSS, the patients with MDD showed significantly smaller values of FA in the genu and frontal portion of the body of the corpus callosum. The patients also had smaller values of mean kurtosis (MK) and radial kurtosis (RK), but MK and RK abnormalities were distributed more widely compared with FA, predominantly in the frontal lobe but also in the parietal, occipital, and temporal lobes. Within the callosum, the regions with smaller MK and RK were located more posteriorly than the region with smaller FA. Model analysis suggested significantly smaller values of intra‐neurite signal fraction in the body of the callosum and greater fiber dispersion in the genu, which were compatible with the existing literature of white matter pathology in MDD. Our results show that DKI is capable of demonstrating microstructural alterations in the brains of patients with MDD that cannot be fully depicted by conventional DTI. Though the issues of model validation and parameter estimation still remain, it is suggested that diffusion MRI combined with a biophysical model is a promising approach for investigation of the pathophysiology of MDD.     

Longitudinal diffusion tensor imaging in a rat brain glioma model

In order to investigate the properties of water motion within and around brain tumors as a function of tumor growth, longitudinal diffusion tensor imaging (DTI) was carried out in a rat brain glioma (C6) model. As tumors grew in size, significant anisotropy of water diffusion was seen both within and around the tumor. The tissue water surrounding the tumor exhibited high planar anisotropy, as opposed to the linear anisotropy normally seen in white matter, indicating that cells were experiencing stress in a direction normal to the tumor border. When tumors were sufficiently large, significant anisotropy was also seen within the tumor because of longer-range organization of cancer cells within the tumor borders. These findings have important implications for diffusion-weighted MRI experiments examining tumor growth and response to therapy. Copyright (c) 2008 John Wiley & Sons, Ltd.