Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Consistency of muscle fibers directionality in human thigh derived from diffusion-weighted MRI.

Objective.Diffusion-weighted MR imaging (DW-MRI) is known to quantify muscle fiber directionality and thus may be useful for radiotherapy target definition in sarcomas. Here, we investigate the variability of tissue anisotropy derived from diffusion tensor (DT) in the human thigh to establish the baseline parameters and protocols for DW-MRI acquisition for future studies in sarcoma patients.Approach.We recruited ten healthy volunteers to acquire diffusion-weighted MR images of the left and right thigh. DW-MRI data were used to reconstruct DT eigenvectors within each individual thigh muscle. Deviations of the principal eigenvector from its mean were calculated for different experimental conditions.Main results.Within the majority of muscles in most subjects, the mode of the histogram of the angular deviation of the principal eigenvector of the water DT from its muscle-averaged value did not exceed 20°. On average for all subjects, the mode ranged from 15° to 24°. Deviations much larger than 20° were observed in muscles far from the RF coil, including cases with significant amounts of subcutaneous fat and muscle deformation under its own weight.Significance.Our study is a robust characterization of angular deviations of muscle fiber directionality in the thigh as determined by DW-MRI. We show that an appropriate choice of experimental conditions reduces the variability of the observed directionality. Precise determination of tissue directionality will enable reproducible models of microscopic tumor spread, with future application in defining the clinical target volume for soft tissue sarcoma.

Correlations between DKI and DWI with Ki-67 in gastric adenocarcinoma.

BACKGROUND: Diffusion kurtosis imaging (DKI) has been applied for gastric adenocarcinoma. Correlations between its parameters and Ki-67 are unclear. PURPOSE: To investigate the correlation between DKI and diffusion-weighted imaging (DWI) parameters with the Ki-67 index in gastric adenocarcinoma. MATERIAL AND METHODS: A total of 54 patients with gastric adenocarcinoma were enrolled in the study and underwent DWI and DKI at 3.0-T MRI before surgery. Based on the settings of the regions of interest, the DWI and DKI parameters (including apparent diffusion coefficient [ADC], diffusion kurtosis [K], and diffusion coefficient [DK]) of each patient's gastric adenocarcinoma were measured and calculated. The participants were divided into two groups (low Ki-67 group and high Ki-67 groups). The intraclass correlation coefficient (ICC) and independent-sample t-test were used to compare differences in each parameter between two groups. Spearman's correlation coefficient was calculated to determine the correlation between Ki-67 and the parameters. Each parameter was compared using the area under the receiver operating characteristic curve. All parameters were included in the multivariate logistic regression analysis to explore the relationship between each parameter and high Ki-67 index. RESULTS: ADC and DK were negatively relevant with Ki-67 and K was positively relevant with Ki-67 in gastric adenocarcinoma. ADC, DK, and K had diagnostic efficiency in differentiating the low Ki-67 group from the high Ki-67 group. A higher K value independently predicted a high Ki-67 status. CONCLUSION: DWI and DKI reflected the proliferative characteristics of gastric adenocarcinoma. K was the strongest independent factor for predicting high Ki-67 status.

MP-PCA denoising for diffusion MRS data: promises and pitfalls.

Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.

Diffusion-Weighted Magnetic Resonance Imaging Improves the Accuracy of Differentiation of Benign from Malignant Peripheral Nerve Sheath Tumors.

OBJECTIVE: In patients with neurofibromatosis type 1 (NF1), it is important to accurately determine when plexiform neurofibroma (pNF) transforms to a malignant peripheral nerve sheath tumor (MPNST). The purpose of this study is to investigate the usefulness of diffusion-weighted imaging (DWI) in differentiating pNF and MPNST in NF1 patients. METHODS: Among the NF1 patients who were referred to our hospital between 1985 and 2015, 10 cases of MPNST and 19 cases of pNF were included. We evaluated features of standard magnetic resonance imaging according to the differentiation criteria of malignancy from benignancy as previously reported, apparent diffusion coefficient (ADC) value based on the DWI and the correlation between ADC value and benignancy/malignancy. ROC analysis was performed to determine the appropriate cutoff value of ADC. RESULTS: There were significant differences between MPNST and pNF in the size of the tumor (P = 0.009), peripheral enhancement pattern (P = 0.002), perilesional edema-like zone (P = 0.0008), and intratumoral cystic change (P = 0.02). The mean and minimum values of ADC were significantly lower in MPNST than those in pNF (P = 0.03 and P = 0.003, respectively). When we set a cutoff value of mean ADC as 1.85 × 10-3 mm2/s, the sensitivity and specificity were 80% and 74%, respectively. The area under the curve value improved by adding the Wasa score to the mean ADC evaluation. CONCLUSIONS: ADC values determined by DWI are useful in differentiating MPNST from pNF and adding ADC evaluation to standard MRI evaluation improved the diagnostic accuracy.

Widespread effects of dMRI data quality on diffusion measures in children.

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large-scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5-17 years of age) from six different centers. Six data quality metrics-contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)-and four diffusion measures-fractional anisotropy, mean diffusivity, tract density, and length-were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between-site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group-wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

What's new and what's next in diffusion MRI preprocessing.

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

THE ROLE OF THE APPARENT DIFFUSION COEFFICIENT OF THE BIPARAMETRIC MRI AS AN IMAGING MARKER OF PROSTATE CANCER. / ROL' VYMIRIuVANOGO KOEFITsIIeNTA DYFUZIÏ BIPARAMETRYChNOÏ MRT IaK PROMENEVOGO MARKERA RAKU PEREDMIKhUROVOÏ ZALOZY.

Prostate cancer (PCa) is the most common malignancy in men. The role of the apparent diffusion coefficient (ADC)of biparametric MRI (biMRI) which is a study without the use of dynamic contrast enhancement (DCE), in detectionof PCa is still not comprehensively investigated. OBJECTIVE: The goal of the study was to assess the role of ADC of biMRI as an imaging marker of clinically significant PCaMaterials and methods. The study involved 78 men suspected of having PCa. All patients underwent a comprehensive clinical examination, which included multiparametric MRI of the prostate, a component of which was biMRI. TheMRI data was evaluated according to the PIRADS system version 2.1. RESULTS: The distribution of patients according to the PIRADS system was as follows: 1 point - 9 (11.54 %)patients, 2 points - 12 (15.38 %) patients, 3 points - 25 (32.05 %) patients, 4 points - 19 (24.36 %) patients and5 points - 13 (16.67 %) patients. In a subgroup of patients with 5 points, clinically significant PCa was detected in 100 % of cases. In the subgroup of patients with tumors of 4 points clinically significant PCa was diagnosed in 16of 19 (84.21 %) cases, and in 3 (15.79 %) patients - clinically insignificant tumor. In the subgroup of patients with3 points, clinically significant PCa was diagnosed in 11 of 25 (44.0 %) cases, in 8 (32.0 %) patients - clinicallyinsignificant tumor and in 6 (24.0 %) patients - benign prostatic hyperplasia. PCa with a score of 7 on the Gleasonscale showed significantly lower mean values of ADC of the diffusion weighted MRI images compared to tumors witha score of < 7 on the Gleason scale: (0.86 ± 0.07) x 103 mm2/s vs (1.08 ± 0.04) x 103 mm2/s (р < 0.05). CONCLUSIONS: The obtained results testify to the high informativeness of biMRI in the diagnosis of prostate cancer.The use of ADC allowed to differentiate clinically significant and insignificant variants of the tumor, as well asbenign changes in prostate tissues and can be considered as a potential imaging marker of PCa.

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution.

Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution-a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.

Effectiveness of ADC Difference Value on Pre-neoadjuvant Chemotherapy MRI for Response Evaluation of Breast Cancer.

Background: Neoadjuvant chemotherapy (NAC) is known to be a suitable treatment and first-line defense for locally advanced breast cancer. However, the NAC response may include unexpected outcomes, and it is not easy to predict the NAC response precisely. Especially, early detection of those patients who do not benefit from NAC is needed to reduce unnecessary therapy and side effects. Objective: The purpose of this study was to determine whether the pretreatment apparent diffusion coefficient (ADC) value is effective for predicting the response of breast cancer to NAC. Method: Forty-nine breast cancer cases with pre- and post-NAC breast MRI were enrolled. MRI was performed using a 1.5-T scanner with the basic protocol including diffusion-weighted imaging. ADC difference value (ADC-diff) was calculated in all cases. Results: ADC-diff was high in complete response and partial response cases (p < .05). ADC-diff correlated with the DWI rim sign, with a positive DWI rim sign being associated with a higher ADC-diff (p < .05). Conclusion: High-ADC difference value on the pretreatment MRI can provide information for a better response of NAC on breast cancer.

In vivo assessment of anisotropy of apparent magnetic susceptibility in white matter from a single orientation acquisition.

Multiple studies have reported a significant dependence of the effective transverse relaxation rate constant (R2*) and the phase of gradient-echo based (GRE) signal on the orientation of white matter fibres in the human brain. It has also been hypothesized that magnetic susceptibility, as obtained by single-orientation quantitative susceptibility mapping (QSM), exhibits such a dependence. In this study, we investigated this hypothesized relationship in a cohort of healthy volunteers. We show that R2* follows the predicted orientation dependence consistently across white matter regions, whereas the apparent magnetic susceptibility is related differently to fibre orientation across the brain and often in a complex non-monotonic manner. In addition, we explored the effect of fractional anisotropy measured by diffusion-weighted MRI on the strength of the orientation dependence and observed only a limited influence in many regions. However, with careful consideration of such an impact and the limitations imposed by the ill-posed nature of the dipole inversion process, it is possible to study magnetic susceptibility anisotropy in specific brain regions with a single orientation acquisition.

Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study.

OBJECTIVES: To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD. METHODS: 108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics. RESULTS: Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively). CONCLUSIONS: PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.

High-resolution whole-brain diffusion MRI at 3T using simultaneous multi-slab (SMSlab) acquisition.

High-resolution diffusion MRI (dMRI) is a crucial tool in neuroscience studies to detect fine fiber structure, depict complex fiber architecture and analyze cortical anisotropy. However, high-resolution dMRI is limited by its intrinsically low SNR due to diffusion attenuation. A series of techniques have been proposed to improve the SNR performance, but most of them are at the cost of long scan time, which in turn sacrifice the SNR efficiency, especially for large FOV imaging, such as whole-brain imaging. Recently, a combination of 3D multi-slab acquisition and simultaneous multi-slice (SMS) excitation, namely simultaneous multi-slab (SMSlab), has been demonstrated to have potential for high-resolution diffusion imaging with high SNR and SNR efficiency. In our previous work, we have proposed a 3D Fourier encoding and reconstruction framework for SMSlab acquisition. In this study, we extend this 3D k-space framework to diffusion imaging, by developing a novel navigator acquisition strategy and exploring a k-space-based phase correction method. In vivo brain data are acquired using the proposed SMSlab method and compared with a series of different acquisitions, including the traditional 3D multi-slab, 2D SMS and 2D single-shot EPI (ss-EPI) acquisitions. The results demonstrate that SMSlab has a better SNR performance compared with 3D multi-slab and 2D SMS. The detection capacity of fine fiber structures is improved using SMSlab, compared with the low-resolution diffusion imaging using conventional 2D ss-EPI.

Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile.

PURPOSE: Glioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. METHODS: Pre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics. RESULTS: Grade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG. CONCLUSIONS: ADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example.

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.

The variability of MR axon radii estimates in the human white matter.

The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.

Prognostic Value of Feature-Tracking Circumferential Strain in Dilated Cardiomyopathy Patients with Severely Reduced Ejection Fraction Incremental to Late Gadolinium Enhancement.

Myocardial fiber deformation measurements have been reported to be associated with adverse outcomes in patients with acute heart failure and those with myocardial infarction. However, few studies have addressed the prognostic value of global circumferential strain (GCS) in dilated cardiomyopathy (DCM) patients with severely impaired systolic function. This study aimed to evaluate the prognostic value of cardiac magnetic resonance (CMR)-derived GCS in DCM patients with severely reduced ejection. Consecutive DCM patients with severely reduced ejection fraction (EF <35%) who underwent CMR were included. GCS was calculated from CMR cine images. The clinical endpoint was a composite of all-cause mortality, heart transplantation, implantable cardioverter defibrillator (ICD) implantation and aborted sudden cardiac death (SCD). A total of 129 patients with a mean EF of 15.33% (11.36%-22.27%) were included. During a median follow-up of 518 days, endpoint events occurred in 50 patients. Patients with GCS ≥ the median (-5.17%) had significantly reduced event-free survival as compared with those with GCS < the median (P<0.01). GCS was independently associated with adverse events after adjusting for clinical and imaging risk factors including extent of late gadolinium enhancement (LGE) (P<0.05). Adding GCS into the model including the extent of LGE resulted in significant improvements in the C-statistic (from 0.706 to 0.742; P<0.05) with a continuous net reclassification improvement (NRI) of 29.71%. It was concluded that GCS derived from CMR could be useful for risk stratification in DCM patients with severely reduced EF, which may increase common imaging risk factors including LGE.

Diffusion-weighted MRI of ischemic stroke at 3T: Value of synthetic b-values.

OBJECTIVES: Diffusion-weighted imaging (DWI) plays a crucial role in the diagnosis of ischemic stroke. We assessed the value of computed and acquired high b-value DWI in comparison with conventional b = 1000 s mm-2 DWI for ischemic stroke at 3T. METHODS: We included 36 patients with acute ischemic stroke who presented with diffusion abnormalities on DWI performed within 24 h of symptom onset. B-values of 0, 500, 1000 and 2000 s mm-2 were acquired. Synthetic images with b-values of 1000, 1500, 2000 and 2500 s mm-2 were computed. Two readers compared synthetic (syn) and acquired (acq) b = 2000 s mm-2 images with acquired b = 1000 s mm-2 images in terms of lesion detection rate, image quality, presence of uncertain hyperintensities and lesion conspicuity. Readers also selected their preferred b-value. Contrast ratio (CR) measurements were performed. Non-parametrical statistical tests and weighted Cohens' κ tests were computed. RESULTS: Syn1000 and syn1500 matched acq1000 images in terms of lesion detection rate, image quality and presence of uncertain hyperintensities but presented with significantly improved lesion conspicuity (p < 0.01) and were frequently selected as preferred b-values. Acq2000 images exhibited a similar lesion detection rate and improved lesion conspicuity (p < 0.01) but worse image quality (p < 0.01) than acq1000 images. Syn2000 and syn2500 images performed significantly worse (p < 0.01) than acq1000 images in most or all categories. CR significantly increased with increasing b-values. CONCLUSION: Synthetic images at b = 1000 and 1500 s mm-2 and acquired DWI images at b = 2000 s mm-2 may be of clinical value due to improved lesion conspicuity. ADVANCES IN KNOWLEDGE: Synthetic b-values enable improved lesion conspicuity for DWI of ischemic stroke.

Diagnostic performance of arterial spin-labeled perfusion imaging and diffusion-weighted imaging in full-term neonatal hypoxic-ischemic encephalopathy.

We evaluated the performance of arterial spin-labeled perfusion imaging and diffusion-weighted imaging in diagnosing full-term neonatal hypoxic-ischemic encephalopathy. Arterial spin-labeled, diffusion-weighted imaging and conventional magnetic resonance imaging (T1-weighted imaging, T2-weighted imaging and T2 fluid-attenuated inversion recovery) were performed in 23 full-term neonates with hypoxic-ischemic encephalopathy group 10 normal neonates (Control group). The cerebral blood flow and the apparent diffusion coefficient were measured in the bilateral basal ganglia, thalamus and frontal white matter. The effect of neonatal age on the CBF and apparent diffusion coefficient values were further investigated after dividing the 23 ischemic encephalopathy cases into three subgroups (1-3 days, 4-7 days, and 8-15 days). The cerebral blood flow values in the thalamus and lenticular nucleus were significantly higher. The apparent diffusion coefficient values in the thalamus, frontal white matter and lenticular nucleus head were significantly lower in the hypoxic-ischemic encephalopathy group than those in the Control group (p < 0.05). There were no significant differences between the ischemic encephalopathy and Control groups in the cerebral blood flow values in the caudate nucleus head and frontal lobe white matter (p > 0.05). The cerebral blood flow and apparent diffusion coefficient values in the thalamus and lenticular nucleus were negatively correlated. Comparison among different age subgroups of hypoxic-ischemic encephalopathyneonates showed that the cerebral blood flow value was higher. In comparison, the apparent diffusion coefficient value was lower in the 1-3 days old neonates than those in the older neonates (p < 0.05). Arterial spin-labeled and diffusion-weighted imaging could reflect the ischemic encephalopathy pathological processes more comprehensively. The cerebral blood flow measurement and apparent diffusion coefficient values in the thalamus and the lenticular nucleus may represent a novel way to diagnose ischemic encephalopathy early.

Technical limitations and pitfalls of diffusion-weighted imaging in intraoperative high-field MRI.

OBJECTIVE: Image quality in high-field intraoperative MRI (iMRI) is often influenced negatively by susceptibility artifacts. While routine sequences are rather robust, advanced imaging such as diffusion-weighted imaging (DWI) is very sensitive to susceptibility resulting in insufficient imaging data. This study aims to analyze intraoperatively acquired DWI to identify the main factors for susceptibility, to compare results with postoperative images and to identify technical aspects for improvement of intraoperative DWI. METHODS: 100 patients with intraaxial lesions operated in a high-field iMRI were analyzed retrospectively for the quality of intraoperative DWI in comparison to the postoperative scan. General quality of the MR scan, individual diffusion restrictions, artifacts, and their causes were analyzed. RESULTS: Inclusion criteria were met in 78 patients, 124 diffusion restrictions were included in the comparative analysis. PPV and NPV for the detection of DWI changes intraoperatively were 0.94 and 0.56, respectively (SEN 0.94; SPE 0.56). Image quality was rated significantly (p < 0.0001) worse intraoperatively compared to the postoperative MRI. The main reasons for reduced image quality intraoperatively were air (64%) and artificial material (e.g., compress) (38%) in the resection cavity, as well as positioning of patient's head outside the MR's isocenter 37%. Analysis of surgical approaches showed that frontal craniotomies have the highest risk of limited image quality (40%), whereat better results (15% limited image quality) were seen for all other approaches (p = 0.059). CONCLUSION: Intraoperative DWI showed reliable results in this analysis. However, image-quality was limited severely in many cases leading to uncertainty in the interpretation. Susceptibility-causing factors might be prevented in many cases, if the surgical team is aware of them. The most important factors are good filling of the resection cavity with irrigation fluid, not placing artificial materials in the resection cavity and adequate positioning of patient's head according to the MR isocenter.