A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi‐shell data, with diffusion sensitisation applied along many directions over multiple b‐value shells. Such schemes are characterised by the number of shells acquired, and the specific b‐value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi‐shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion‐weighted images at b = 400, 1000 and 2600 s/mm² with 64, 88 and 128 directions per shell, respectively.     

Tract‐specific and age‐related variations of the spinal cord microstructure: a multi‐parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT)

Being able to finely characterize the spinal cord (SC) microstructure and its alterations is a key point when investigating neural damage mechanisms encountered in different central nervous system (CNS) pathologies, such as multiple sclerosis, amyotrophic lateral sclerosis or myelopathy. Based on novel methods, including inhomogeneous magnetization transfer (ihMT) and dedicated SC probabilistic atlas post‐processing, the present study focuses on the in vivo characterization of the healthy SC tissue in terms of regional microstructure differences between (i) upper and lower cervical vertebral levels and (ii) sensory and motor tracts, as well as differences attributed to normal aging. Forty‐eight healthy volunteers aged from 20 to 70 years old were included in the study and scanned at 3 T using axial high‐resolution T₂*‐w imaging, diffusion tensor imaging (DTI) and ihMT, at two vertebral levels (C2 and C5). A processing pipeline with minimal user intervention, SC segmentation and spatial normalization into a reference space was implemented in order to assess quantitative morphological and structural parameters (cross‐sectional areas, scalar DTI and MT/ihMT metrics) in specific white and gray matter regions of interest. The multi‐parametric MRI metrics collected allowed upper and lower cervical levels to be distinguished, with higher ihMT ratio (ihMTR), higher axial diffusivity (λ_∥) and lower radial diffusivity (λ_⊥) at C2 compared with C5. Significant differences were also observed between white matter fascicles, with higher ihMTR and lower λ_∥ in motor tracts compared with posterior sensory tracts. Finally, aging was found to be associated with significant metric alterations (decreased ihMTR and λ_∥). The methodology proposed here, which can be easily transferred to the clinic, provides new insights for SC characterization. It bears great potential to study focal and diffuse SC damage in neurodegenerative and demyelinating diseases. Copyright © 2016 John Wiley & Sons, Ltd.     

High‐resolution diffusion tensor imaging of the human kidneys using a free‐breathing,multi‐slice,targeted field of view approach

Fractional anisotropy (FA) obtained by diffusion tensor imaging (DTI) can be used to image the kidneys without any contrast media. FA of the medulla has been shown to correlate with kidney function. It is expected that higher spatial resolution would improve the depiction of small structures within the kidney. However, the achievement of high spatial resolution in renal DTI remains challenging as a result of respiratory motion and susceptibility to diffusion imaging artefacts. In this study, a targeted field of view (TFOV) method was used to obtain high‐resolution FA maps and colour‐coded diffusion tensor orientations, together with measures of the medullary and cortical FA, in 12 healthy subjects. Subjects were scanned with two implementations (dual and single kidney) of a TFOV DTI method. DTI scans were performed during free breathing with a navigator‐triggered sequence. Results showed high consistency in the greyscale FA, colour‐coded FA and diffusion tensors across subjects and between dual‐ and single‐kidney scans, which have in‐plane voxel sizes of 2 × 2 mm² and 1.2 × 1.2 mm², respectively. The ability to acquire multiple contiguous slices allowed the medulla and cortical FA to be quantified over the entire kidney volume. The mean medulla and cortical FA values were 0.38 ± 0.017 and 0.21 ± 0.019, respectively, for the dual‐kidney scan, and 0.35 ± 0.032 and 0.20 ± 0.014, respectively, for the single‐kidney scan. The mean FA between the medulla and cortex was significantly different (p < 0.001) for both dual‐ and single‐kidney implementations. High‐spatial‐resolution DTI shows promise for improving the characterization and non‐invasive assessment of kidney function. © 2014 The Authors. NMR in Biomedicine published by John Wiley & Sons, Ltd.     

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.     

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.     

User‐independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study

Automated analysis of diffusion tensor imaging (DTI) data is an appealing way to process large datasets in an unbiased manner. However, automation can sometimes be linked to a lack of interpretability. Two whole‐brain, automated and voxelwise methods exist: voxel‐based analysis (VBA) and tract‐based spatial statistics (TBSS). In VBA, the amount of smoothing has been shown to influence the results. TBSS is free of this step, but a projection procedure is introduced to correct for residual misalignments. This projection assigns the local highest fractional anisotropy (FA) value to the mean FA skeleton, which represents white matter tract centers. For both methods, the normalization procedure has a major impact. These issues are well documented in humans but, to our knowledge, not in rodents. In this study, we assessed the quality of three different registration algorithms (ANTs SyN, DTI‐TK and FNIRT) using study‐specific templates and their impact on automated analysis methods (VBA and TBSS) in a rat pup model of diffuse white matter injury presenting large unilateral deformations. VBA and TBSS results were stable and anatomically coherent across the three pipelines. For VBA, in regions around the large deformations, interpretability was limited because of the increased partial volume effect. With TBSS, two of the three pipelines found a significant decrease in axial diffusivity (AD) at the known injury site. These results demonstrate that automated voxelwise analyses can be used in an animal model with large deformations.     

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.     

High‐resolution diffusion tensor imaging in cervical spondylotic myelopathy: a preliminary follow‐up study

Diffusion imaging is a promising technique as it can provide microstructural tissue information and thus potentially show viable changes in spinal cord. However, the traditional single‐shot imaging method is limited as a result of various image artifacts. In order to improve measurement accuracy, we used a newly developed, multi‐shot, high‐resolution, diffusion tensor imaging (DTI) method to investigate diffusion metric changes and compare them with T₂‐weighted (T2W) images before and after decompressive surgery for cervical spondylotic myelopathy (CSM). T2W imaging, single‐shot DTI and multi‐shot DTI were employed to scan seven patients with CSM before and 3 months after decompressive surgery. High signal intensities were scored using the T2 W images. DTI metrics, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were quantified and compared pre‐ and post‐surgery. In addition, the relationship between imaging metrics and neurological assessments was examined. The reproducibility of multi‐shot DTI was also assessed in 10 healthy volunteers. Post‐surgery, the mean grade of cervical canal stenosis was reduced from grade 3 to normal after 3 months. Compared with single‐shot DTI, multi‐shot DTI provided better images with lower artifact levels, especially following surgery, as a result of reduced artifacts from metal implants. The new method also showed acceptable reproducibility. Both FA and RD values from the new acquisition showed significant differences post‐surgery (FA, p = 0.026; RD, p = 0.048). These changes were consistent with neurological assessments. In contrast, T2W images did not show significant changes before and after surgery. Multi‐shot diffusion imaging showed improved image quality over single‐shot DWI, and presented superior performance in diagnosis and recovery monitoring for patients with CSM compared with T2W imaging. DTI metrics can reflect the pathological conditions of spondylotic spinal cord quantitatively and may serve as a sensitive biomarker for potential CSM management.     

弥散张量磁共振成像的新进展

弥散张量磁共振成像技术是近年来出现的一项新技术,由于其对脑白质纤维具有高度敏感性以致在临床上的应用日益广泛,并为人体中枢神经系统的深入研究提供了有效的工具。     

Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods

Diffusion tensor imaging (DTI) has been proposed for the prognosis of cervical myelopathy (CM), but the manual analysis of DTI features is complicated and time consuming. This study evaluated the potential of artificial intelligence (AI) methods in the analysis of DTI for the prognosis of CM. Seventy‐five patients who underwent surgical treatment for CM were recruited for DTI imaging and were divided into two groups based on their one‐year follow‐up recovery. The DTI features of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted from DTI maps of all cervical levels. Conventional AI models using logistic regression (LR), k‐nearest neighbors (KNN), and a radial basis function kernel support vector machine (RBF‐SVM) were built using these DTI features. In addition, a deep learning model was applied to the DTI maps. Their performances were compared using 50 repeated 10‐fold cross‐validations. The accuracy of the classifications reached 74.2% ± 1.6% for LR, 85.6% ± 1.4% for KNN, 89.7% ± 1.6% for RBF‐SVM, and 59.2% ± 3.8% for the deep leaning model. The RBF‐SVM algorithm achieved the best accuracy, with sensitivity and specificity of 85.0% ± 3.4% and 92.4% ± 1.9% respectively. This finding indicates that AI methods are feasible and effective for DTI analysis for the prognosis of CM.     

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.     

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.     

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.     

Multi‐centre reproducibility of diffusion MRI parameters for clinical sequences in the brain

The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra‐voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice–water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub‐regions. A mixed effect model was used to measure the intra‐ and inter‐scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra‐ and inter‐scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra‐scanner CV of 8.4% and inter‐scanner CV of 24.8%. No major difference in the inter‐scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra‐scanner reproducibility, with the inter‐scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter‐scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi‐centre clinical studies and trials. © 2015 The Authors NMR in Biomedicine Published by John Wiley & Sons Ltd.     

Comparing diagnostic accuracy of luminal water imaging with diffusion‐weighted and dynamic contrast‐enhanced MRI in prostate cancer: A quantitative MRI study

Luminal water imaging (LWI) is a new MRI T₂ mapping technique that has been developed with the aim of diagnosis of prostate carcinoma (PCa). This technique measures the fractional amount of luminal water in prostate tissue, and has shown promising preliminary results in detection of PCa. To include LWI in clinical settings, further investigation on the accuracy of this technique is required. In this study, we compare the diagnostic accuracy of LWI with those of diffusion‐weighted imaging (DWI) and dynamic contrast‐enhanced (DCE) MRI in detection and grading of PCa. Fifteen patients with biopsy‐proven PCa consented to participate in this ethics‐board‐approved prospective study. Patients were examined with LWI, DWI, and DCE sequences at 3 T prior to radical prostatectomy. Maps of MRI parameters were generated and registered to whole‐mount histology. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic accuracy of individual and combined MR parameters. Correlation with Gleason score (GS) was evaluated using Spearman's rank correlation test. The results show that area under the ROC curve (AUC) obtained from LWI was equal to or higher than the AUC obtained from DWI, DCE, or their combination, in peripheral zone (0.98 versus 0.90, 0.89, and 0.91 respectively), transition zone (0.99 versus 0.98, n/a, and 0.98), and the entire prostate (0.85 versus 0.81, 0.75, and 0.84). The strongest correlation with GS was achieved from LWI (ρ = ?0.81 ± 0.09, P < 0.001). Results of this pilot study show that LWI performs equally well as, or better than, DWI and DCE in detection of PCa. LWI provides significantly higher correlation with GS than DWI and DCE. This technique can potentially be included in clinical MRI protocols to improve characterization of tumors. However, considering the small size of the patient population in this study, a further study with a larger cohort of patients and broader range of GS is required to confirm the findings and draw a firm conclusion on the applicability of LWI in clinical settings.     

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.     

Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle

Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time‐consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi‐automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post‐marathon and follow‐up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi‐automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out‐of‐phase Dixon images at baseline. These segmentations were longitudinally propagated for the post‐marathon and follow‐up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi‐automatic segmentations. Bland‐Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ₃). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland‐Altman analysis showed that the 95% limits of agreement for MD, FA and λ₃ ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ₃ (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi‐automatic segmentation framework successfully quantified DTI indices in the upper‐leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.     

The quest for high spatial resolution diffusion‐weighted imaging of the human brain in vivo

Diffusion‐weighted imaging, a contrast unique to MRI, is used for assessment of tissue microstructure in vivo. However, this exquisite sensitivity to finer scales far above imaging resolution comes at the cost of vulnerability to errors caused by sources of motion other than diffusion motion. Addressing the issue of motion has traditionally limited diffusion‐weighted imaging to a few acquisition techniques and, as a consequence, to poorer spatial resolution than other MRI applications. Advances in MRI imaging methodology have allowed diffusion‐weighted MRI to push to ever higher spatial resolution. In this review we focus on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion‐weighted MRI.     

Diffusion tensor imaging and T2 relaxometry of bilateral lumbar nerve roots: feasibility of in‐plane imaging

Lower back pain is a common problem frequently encountered without specific biomarkers that correlate well with an individual patient's pain generators. MRI quantification of diffusion and T₂ relaxation properties may provide novel insight into the mechanical and inflammatory changes that occur in the lumbosacral nerve roots in patients with lower back pain. Accurate imaging of the spinal nerve roots is difficult because of their small caliber and oblique course in all three planes. Two‐dimensional in‐plane imaging of the lumbosacral nerve roots requires oblique coronal imaging with large field of view (FOV) in both dimensions, resulting in severe geometric distortions using single‐shot echo planar imaging (EPI) techniques. The present work describes initial success using a reduced‐FOV single‐shot spin‐echo EPI acquisition to obtain in‐plane diffusion tensor imaging (DTI) and T₂ mapping of the bilateral lumbar nerve roots at the L4 level of healthy subjects, minimizing partial volume effects, breathing artifacts and geometric distortions. A significant variation in DTI and T₂ mapping metrics is also reported along the course of the normal nerve root. The fractional anisotropy is statistically significantly lower in the dorsal root ganglia (0.287 ± 0.068) than in more distal regions in the spinal nerve (0.402 ± 0.040) (p < 10^–5). The T₂ relaxation value is statistically significantly higher in the dorsal root ganglia (78.0 ± 11.9 ms) than in more distal regions in the spinal nerve (59.5 ± 7.4 ms) (p < 10^–5). The quantification of nerve root DTI and T₂ properties using the proposed methodology may identify the specific site of any degenerative and inflammatory changes along the nerve roots of patients with lower back pain. Copyright © 2012 John Wiley & Sons, Ltd.     

Intravoxel water diffusion heterogeneity imaging of human high‐grade gliomas

This study aimed to determine the potential value of intravoxel water diffusion heterogeneity imaging for brain tumor characterization and evaluation of high‐grade gliomas, by comparing an established heterogeneity index (α value) measured in human high‐grade gliomas to those of normal appearing white and grey matter landmarks. Twenty patients with high‐grade gliomas prospectively underwent diffusion‐weighted magnetic resonance imaging using multiple b‐values. The stretched‐exponential model was used to generate α and distributed diffusion coefficient (DDC) maps. The α values and DDCs of the tumor and contralateral anatomic landmarks were measured in each patient. Differences between α values of tumors and landmark tissues were assessed using paired t‐tests. Correlation between tumor α and tumor DDC was assessed using Pearson's correlation coefficient. Mean α of tumors was significantly lower than that of contralateral frontal white matter (p = 0.0249), basal ganglia (p < 0.0001), cortical grey matter (p < 0.0001), and centrum semiovale (p = 0.0497). Correlation between tumor α and tumor DDC was strongly negative (Pearson correlation coefficient, ?0.8493; p < 0.0001). The heterogeneity index α of human high‐grade gliomas is significantly different from those of normal brain structures, which potentially offers a new method for evaluating brain tumors. The observed negative correlation between tumor α and tumor DDC requires further investigation. Copyright © 2009 John Wiley & Sons, Ltd.