Diffusion Kurtosis Imaging Data Research Articles

Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI.

We evaluated functional connectivity (FC) in patients with adult autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI). We acquired rs-fMRI data from 33 individuals with ASD and 33 healthy controls (HC) and DKI data from 18 individuals with ASD and 17 HC. ASD showed attenuated FC between the right frontal pole (FP) and the bilateral temporal fusiform cortex (TFusC) and enhanced FC between the right thalamus and the bilateral inferior division of lateral occipital cortex, and between the cerebellar vermis and the right occipital fusiform gyrus (OFusG) and the right lingual gyrus, compared with HC. ASD demonstrated increased axial kurtosis (AK) and mean kurtosis (MK) in white matter (WM) tracts, including the right anterior corona radiata (ACR), forceps minor (FM), and right superior longitudinal fasciculus (SLF). In ASD, there was also a significant negative correlation between MK and FC between the cerebellar vermis and the right OFusG in the corpus callosum, FM, right SLF and right ACR. Increased DKI metrics might represent neuroinflammation, increased complexity, or disrupted WM tissue integrity that alters long-distance connectivity. Nonetheless, protective or compensating adaptations of inflammation might lead to more abundant glial cells and cytokine activation effectively alleviating the degeneration of neurons, resulting in increased complexity. FC abnormality in ASD observed in rs-fMRI may be attributed to microstructural alterations of the commissural and long-range association tracts in WM as indicated by DKI.

The Role of Diffusion Kurtosis MR Imaging in the Evaluation of Invasive Breast Cancer Classification and its Correlation with Prognostic Factors: A Prospective Study of Primarily Advanced Stage Cancers

Background: The present exploration is aimed to determine whether diffusion kurtosis imaging (DKI)-derived mean diffusivity (MD) and mean kurtosis (MK) are possible parameters for the invasive breast cancer grading and whether MD/MK is related to breast cancer clinical-pathologic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and Ki-67. Methods: Tumors from 108 invasive breast carcinoma patients (45.6 ± 11.2 years old; range, 20–84 years), diagnosed by pathological examination between January 2016 and August 2017, were included. DKI data (with b values of 0, 1000, 2000 sec/mm2) and MD/MK were measured. The expression of ER, PR, HER-2, and Ki-67 was evaluated by immunohistochemistry. Results: It was found that MD was significantly lower in grade III breast cancer than in grade II breast cancer (0.82 ± 0.21 vs 1.17 ± 0.24, p < 0.0001), while MK was significantly higher in grade III breast cancer than that in grade II breast cancer (1.00 ± 0.31 vs 0.85 ± 0.21, respectively; p < 0.05). In addition, MD was negatively associated with Ki-67 level (r = –0.39, p < 0.05), while MK was positively associated with Ki-67 level (r = 0.56, p < 0.05). Furthermore, MD was significantly increased in ER and PR positive expression group compared with ER and PR negative expression group, while there is no significant difference of MK in ER and PR positive expression group compared with ER and PR negative expression group. Additionally, there is no significant difference of MD and MK in HER-2 positive expression group compared with HER-2 negative expression group. Conclusions: These results demonstrate that DKI has value in the evaluation on the classification of invasive breast carcinoma. MD values were associated with breast cancer clinical-pathologic factors. DKI can provide useful information in the assessment of tumor proliferation activity.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

Background and objectiveChoosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics’ variability with respect to the magnetic field on the adopted denoising methodology. MethodsFor comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method. ResultsAccounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin. ConclusionsThis study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion

Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.

Application of TBSS-based machine learning models in the diagnosis of pediatric autism.

To explore the microstructural changes of white matter in children with pediatric autism by using diffusion kurtosis imaging (DKI), and evaluate whether the combination of tract-based spatial statistics (TBSS) and back-propagation neural network (BPNN)/support vector machine (SVM)/logistic regression (LR) was feasible for the classification of pediatric autism. DKI data were retrospectively collected from 32 children with autism and 27 healthy controls (HCs). Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), radial kurtosis (RK), fractional anisotropy (FA), axial diffusivity (DA), mean diffusivity (MD) and Radial diffusivity (DR) were generated by iQuant workstation. TBSS was used to detect the regions of parameters values abnormalities and for the comparison between these two groups. In addition, we also introduced the lateralization indices (LI) to study brain lateralization in children with pediatric autism, using TBSS for additional analysis. The parameters values of the differentiated regions from TBSS were then calculated for each participant and used as the features in SVM/BPNN/LR. All models were trained and tested with leave-one-out cross validation (LOOCV). Compared to the HCs group, the FAK, DA, and KA values of multi-fibers [such as the bilateral superior longitudinal fasciculus (SLF), corticospinal tract (CST) and anterior thalamic radiation (ATR)] were lower in pediatric autism group (p < 0.05, TFCE corrected). And we also found DA lateralization abnormality in Superior longitudinal fasciculus (SLF) (the LI in HCs group was higher than that in pediatric autism group). However, there were no significant differences in FA, MD, MK, DR, and KR values between HCs and pediatric autism group (P > 0.05, TFCE corrected). After performing LOOCV to train and test three model (SVM/BPNN/LR), we found the accuracy of BPNN (accuracy = 86.44%) was higher than that of LR (accuracy = 76.27%), but no different from SVM (RBF, accuracy = 81.36%; linear, accuracy = 84.75%). Our proposed method combining TBSS findings with machine learning (LR/SVM/BPNN), was applicable in the classification of pediatric autism with high accuracy. Furthermore, the FAK, DA, and KA values and Lateralization index (LI) value could be used as neuroimaging biomarkers to discriminate the children with pediatric autism or not.

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions.

To investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs). Patients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs. Most of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs. DKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.

Brain microstructural alterations in the left precuneus mediate the association between KIBRA polymorphism and working memory in healthy adults: a diffusion kurtosis imaging study

Kidney and brain expressed protein (KIBRA) rs17070145 is associated with working memory function and cognitive processes. However, the neural mechanisms underlying these associations are not fully understood. This study aimed to explore the effect of KIBRA polymorphism on brain microstructure and blood oxygenation level dependent (BOLD) fluctuations using diffusion kurtosis imaging (DKI) and resting-state functional magnetic resonance imaging (fMRI) in 163 young adults. We also investigated that whether the imaging alterations mediated the association between KIBRA gene and working memory performance. Voxel-based analysis of DKI data showed that KIBRA C-allele carriers exhibited increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) as well as decreased fractional anisotropy (FA), mean kurtosis (MK) and radial kurtosis (RK) compared with KIBRA TT homozygotes, primarily involving the prefrontal lobe, left precuneus and the left superior parietal white matter. Meanwhile, KIBRA C-allele carriers exhibited decreased amplitude of low-frequency fluctuation (ALFF) in the left precuneus compared to KIBRA TT homozygotes. Mediation analysis revealed that the DKI metrics (MK and RK) of the left precuneus mediated the effect of the KIBRA polymorphism on working memory performance. Moreover, the MK and RK in the left precuneus were positively correlated with ALFF in the same brain region. These findings suggest that abnormal DKI parameters may provide a gene-brain-behavior pathway in which KIBRA rs17070145 affects working memory by modulating brain microstructure in the left precuneus. This illustrates that DKI may provide additional biological information and reveal new insights into the neural mechanisms of the KIBRA polymorphism.

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging.

More than half of the bipolar depression (BD) subjects are misdiagnosed as unipolar depression (UD) due to lack of objective diagnostic criteria. We aimed to identify microstructural neuronal changes differentiating BD from UD groups using diffusion kurtosis imaging (DKI). The objective of the study is to identify an objective neuro-imaging marker to differentiate BD from UD. A prospective, cross-sectional study included total of 62 subjects with diagnosis of bipolar depression (n = 21), unipolar depression (n = 21), and healthy controls (n = 20). All subjects underwent diffusion-weighted imaging (b = 0,1000,2000) of the whole brain on 3-Tesla MR scanner. DKI data was analyzed using 189 region whole-brain atlas. Eight diffusion and kurtosis metrics including mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), and kurtosis fractional anisotropy (FKA) were measured against these 189 regions. Principle component analysis (PCA) was utilized to identify the most significant regions of the brain. ANOVA with post hoc tests was used for analyzing these regions. BD group showed increased MD, RD, decreased AK at the left amygdala and decreased MK and RK at the right hemi-cerebellum. UD group showed increased MK and RK at the right external capsule; and increased AK, MK, and RK at the right amygdala. The right and left amygdala, right external capsule, and right hemi-cerebellum showed microstructural abnormalities capable of differentiating BD and UD groups. Diffusion imaging especially DKI can aid in management of depression patients.

Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine.

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.

Test-retest reliability of diffusion kurtosis imaging metrics in the healthy adult brain

PurposeTo evaluate the reliability of magnetic resonance imaging (MRI) diffusion kurtosis imaging (DKI) derived metrics from the human brain as well as the effect of spatial smoothing preprocess on measurement reliability through test-retest measurements. Materials and methodsA total of 23 healthy volunteers who underwent MRI DKI scanning during two sessions performed at an interval of two weeks were enrolled in the study. For the eight DKI-derived metrics, intraclass correlation coefficient (ICC) and coefficient of variation (CV) were used to assess regional and global reliability and reproducibility of based gray matter (GM) atlas in the automated anatomical labeling (AAL) and white matter (WM) atlas in JHU-White Matter-Labels. Additional comparisons were made between the reliabilities of images with and without smoothing preprocessing. ResultsThe average ICCs of the eight DKI metrics varied from 0.45 to 0.91, CVs ranged from 1.48% to 4.60% in global GM and WM regions, and the reliability degree ranged from moderate to almost perfect, with regional variations. Kurtosis-related metrics presented inferior reliability to the tensor-related metrics. Among the eight DKI metrics, RD had the best reliability, while KFA ranked the last. The reliability of FA in WM was superior to that in GM; however, FA did not result in a good index for GM reliability assessment. Although the DKI reliability of GM regions varied, the default mode and visual areas consistently demonstrated the highest ICC reliability for a series of kurtosis-related metrics. Additionally, smoothing preprocessing increased reliability for a few metrics only in WM compared to preprocessing without smoothing (P < 0.05, FDR corrected). ConclusionThe DKI metrics can be reliably used as biomarkers for diffusion property measurements. The kurtosis-related metrics presented lower reliability compared to the tensor-related metrics from DKI data. Default mode-related areas exhibited higher reliability than other brain cortices in terms of implicated microstructural DKI property. Additionally, an appropriate smoothing preprocessing could improve the reliability of a few DKI metrics only in WM regions.

Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis

Purpose: The aim of the study is to evaluate the potential of Intravoxel incoherent motion model of diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differentiation of local colorectal cancer recurrence (LCR) from scar/fibrosis tissue in patients that underwent chemo-radiation therapy followed by the total mesorectal excision (TME) for locally advanced rectal cancer (LARC). Methods: Fifty-six patients were retrospectively included for the image analysis. Diffusion and perfusion parameters were extracted by DWI data (apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis). Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analyses, and area under ROC curve (AUC) were used in a univariate statistical analysis. Backward stepwise multivariate logistic regression analysis was also performed. Results: LCR was found in 34 patients and treatment related changes such as scar/fibrosis tissue in 22 patients. At univariate analysis, low performance was reached by the mean value of Kurtosis with and AUC of 0.72 and an accuracy of 75%, respectively. Considering a regression model obtained as weighted sum of the ADC, Kurtosis, fp and Dp mean values, reached an AUC of 0.82 with a sensitivity of 72%, a specificity of 93%, and an accuracy of 81%. Conclusions: DWI derived parameters combined with DKI derived metrics in a multivariate model could allow differentiating of local colorectal recurrence from scar/fibrosis tissue after TME of LARC.

Cerebral diffusion kurtosis imaging to assess the pathophysiology of postpartum depression

Postpartum depression (PPD), a main cause of maternal suicide, is an important issue in perinatal mental health. Recently, cerebral diffusion tensor imaging (DTI) studies have shown reduced fractional anisotropy (FA) in major depressive disorder (MDD) patients. There are, however, no reports using diffusion kurtosis imaging (DKI) for evaluation of PPD. This was a Japanese single-institutional prospective study from 2016 to 2019 to examine the pathophysiological changes in the brain of PPD patients using DKI. The DKI data from 3.0 T MRI of patients one month after delivery were analyzed; the patients were examined for PPD by a psychiatrist. The mean kurtosis (MK), FA and mean diffusivity (MD) were calculated from the DKI data and compared between PPD and non-PPD groups using tract-based spatial statistics analysis. Of the 75 patients analyzed, eight patients (10.7%) were diagnosed as having PPD. In the PPD group, FA values in the white matter and thalamus were significantly lower and MD values in the white matter and putamen were significantly higher. The area with significant differences in MD value was more extensive (40.8%) than the area with significant differences in FA value (6.5%). These findings may reflect pathophysiological differences of PPD compared with MDD.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. Sixty-six patients (29 females; median age, 45years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis.

To detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. In total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon-Mann-Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches (linear classifier, support vector machine, k-nearest neighbours, decision tree), with features selection methods and a leave-one-out cross validation approach, were considered. A significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80 (sensitivity of 72%, specificity of 85%, accuracy of 79%) using a cut-off of 203.90 × 10-3, and by the mean value of MD with AUC of 0.80 (sensitivity of 84%, specificity of 73%, accuracy of 77%) using a cut-off of 1694.30 mm2/s × 10-6. Considering all extracted features or the predictors obtained by the features selection method (the mean value of S0, the standard deviation value of MK, FP and of DT), the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation (AUC of 0.73 and 0.69, respectively). Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.

Serial Diffusion Kurtosis Magnetic Resonance Imaging Study during Acute, Subacute, and Recovery Periods after Sport-Related Concussion.

Sport-related concussion (SRC) is common in contact sports, but there remains a lack of reliable, unbiased biomarkers of brain injury and recovery. Although the symptoms of SRC generally resolve over a period of days to weeks, the lack of a biomarker impairs detection and return-to-play decisions. To this date, the pathophysiological recovery profile and relationships between brain changes and symptoms remained unclear. In the current study, diffusion kurtosis imaging (DKI) was used to monitor the effects of SRC on the brain and the trajectory of recovery in concussed American football players (n = 96) at <48 h, and 8, 15, and 45 days post-injury, who were compared with a matched group of uninjured players (n = 82). The concussed group reported significantly higher symptoms within 48 h after injury than controls, which resolved by the 8-day follow-up. The concussed group also demonstrated poorer performance on balance testing at <48 h and 8 days than controls. There were no significant differences between the groups in the Standardized Assessment of Concussion (SAC), a cognitive screening measure. DKI data were acquired with 3 mm isotropic resolution, and analyzed using tract-based spatial statistics (TBSS). Additionally, voxel- and region of interest-based analyses were also conducted. At <48 h, the concussed group showed significantly higher axial kurtosis than the control group. These differences increased in extent and magnitude at 8 days, then receded at 15 days, and returned to the normal levels by 45 days. Kurtosis fractional anisotropy (FA) exhibited a delayed response, with a consistent increase by days 15 and 45. The results indicate that changes detected in the acute period appear to be prolonged compared with clinical recovery, but additional brain changes not observable acutely appear to progress. Although further studies are needed to understand the pathological features of DKI changes after SRC, these findings highlight a potential disparity between clinical symptoms and pathophysiological recovery after SRC.

Diffusion Kurtosis Imaging for Detection of Early Brain Changes in Parkinson's Disease.

We aimed to evaluate microscale changes in the bilateral red nucleus and substantia nigra of patients with Parkinson's disease (PD) using diffusion kurtosis imaging (DKI). Twenty-six patients with PD [mean age, 62.5 ± 8.7 years; Hoehn-Yahr stage, 0–4.0; Unified Parkinson's Disease Rating Scale (UPDRS) scores, 8–43] and 15 healthy controls (mean age, 59.5 ± 9.4 years) underwent DKI of the substantia nigra and red nucleus. Imaging was performed using a General Electric (GE) Signa 3.0-T MRI system. Patients with PD were divided into two groups consisting of 12 patients with UPDRS scores ≥ 30 and 14 patients with UPDRS scores < 30. All DKI data processing operations were performed with commercial workstations (GE, ADW 4.6) using Functool software to generate color-coded and parametric maps of mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD). MK values in the bilateral substantia nigra were significantly lower in patients with early- and advanced-stage PD than in controls. Moreover, MK values in the left substantia nigra were significantly lower in patients with advanced-stage PD than in those with early-stage PD. Patients with advanced-stage PD also exhibited significant decreases in MK values in the bilateral red nucleus relative to controls. No significant differences in FA or MD values were observed between the PD and control groups. There were no significant correlations between MK, FA, or MD values and UPDRS scores. Our findings suggest that decreased MK values in the substantia nigra may aid in determining the severity of PD and help provide early diagnoses.

Diffusional kurtosis imaging of kidneys in patients with hyperuricemia: initial study.

At present, there remains a lack of a reliable indicator for monitoring renal function in patients with hyperuricemia. This study aimed to evaluate the feasibility of diffusion kurtosis imaging in the assessment of renal function in patients with hyperuricemia. A total of 75 male participants, including 25 with asymptomatic hyperuricemia, 25 with gouty arthritis, and 25 age-matched male healthy controls, were enrolled in this study. Diffusion kurtosis imaging data were acquired to derive axial (Ka), radial (Kr), and mean kurtosis (MK), fractional anisotropy, axial (Da), radial (Dr), and mean diffusivity (MD) for comparisons among the three groups. They were also correlated with estimated glomerular filtration rate (eGFR). The MK values of the renal cortex and medulla and Kr value of the renal medulla in patients with asymptomatic hyperuricemia and gouty arthritis significantly increased compared with those in the controls (P < 0.05). Patients with gouty arthritis showed significant higher cortical and medullary Ka values compared with the other two groups (P < 0.05). The cortical Kr values of the asymptomatic hyperuricemia and gouty arthritis patients were significantly higher than that of the controls (P < 0.05). The medullary fractional anisotropy value showed a significant difference between the control and gouty arthritis groups (P < 0.05). No correlation was found between any diffusion kurtosis imaging parameters and eGFR value. Diffusion kurtosis imaging is feasible in the assessment of the early changes of renal cortex and medulla in patients with hyperuricemia.

Evaluation of cerebral microstructural changes in adult patients with obstructive sleep apnea by MR diffusion kurtosis imaging using a whole-brain atlas

Purpose:The association between obstructive sleep apnea (OSA) and cognitive impairment is well-recognized, but little is known about neural derangements that underlie this phenomenon. The purpose of this study was to evaluate the utility of diffusion kurtosis imaging (DKI) using a whole-brain atlas to comprehensively assess microstructural tissue changes in the brain of patients with OSA.Methods:This prospective study was conducted in 20 patients with moderate-to-severe OSA and 20 age- and gender-matched controls. MRI data acquisition was performed with 3 Tesla and data was analyzed using a whole-brain atlas. DKI data were processed and transformed into a brain template space to obtain various kurtosis parameters including axial kurtosis (AK), radial kurtosis (RK), mean kurtosis (MK), and kurtosis fractional anisotropy (KFA) using a 189-region brain atlas in the same template space. These kurtosis measurements were further analyzed using a student t-test in order to determine kurtosis measurements that present significant differences between the OSA patient set and the control set.Results:Significant differences (P < 0.05) were found in AK (54 regions), RK (10 regions), MK (6 regions) and KFA (41 regions) values in patients with OSA as compared to controls. DKI indices, using an atlas-based whole-brain analysis approach used in our study, showed widespread involvement of the anatomical regions in patients with OSA.Conclusion:The kurtosis parameters are more sensitive in demonstrating abnormalities in brain tissue structural organization at the microstructural level before any detectable changes appear in conventional MRI or other imaging modalities.

Characterization of brain microstructural abnormalities in cirrhotic patients without overt hepatic encephalopathy using diffusion kurtosis imaging

Cirrhosis is a major public health concern. However, little is known about the neurobiological mechanisms underlying brain microstructure alterations in cirrhotic patients. The purpose of this prospective study was to investigate brain microstructural alterations in cirrhosis with or without minimal hepatic encephalopathy (MHE) and their relationship with patients’ neurocognitive performance and disease duration using voxel-based analysis of diffusion kurtosis imaging (DKI). DKI data were acquired from 30 cirrhotic patients with MHE, 31 patients without MHE (NMHE) and 59 healthy controls. All DKI-derived parametric maps were compared across the three groups to investigate their group differences. Correlation analyses were further performed to assess relationships between altered imaging parameters and clinical data. Voxel-based analysis of DKI data results showed that MHE/NMHE patients had increased radial diffusivity, axial diffusivity (AD) and mean diffusivity in addition to decreased axial kurtosis (AK) and fractional anisotropy of kurtosis in several regions. Compared to controls, these regions were primarily the cingulum, temporal and frontal cortices. The DKI metrics (i.e., AK and AD) were correlated with clinical variables in the two patient groups. In conclusion, DKI is useful for detecting brain microstructural abnormalities in MHE and NMHE patients. Abnormal DKI parameters suggest alterations in brain microstructural complexity in cirrhotic patients, which may contribute to the neurobiological basis of neurocognitive impairment. These results may provide additional information on the pathophysiology of cirrhosis.

Comparative analysis of the value of diffusion kurtosis imaging and diffusion-weighted imaging in evaluating the histological features of endometrial cancer

PurposeThis study evaluated and compared the performances of diffusion kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) for diagnosing and histologically grading endometrial cancer.Materials and methodsIn this retrospective study, DKI and DWI data for 61 patients with endometrial cancer and 30 patients with a normal endometrium were analyzed, and the mean kurtosis (MK), mean diffusion coefficient (MD) and apparent diffusion coefficient (ADC) values for the endometrial cancer tissue and normal endometrial tissue were acquired. The parameters for the normal endometrium group (G0) and the endometrial cancer groups (G1, G2 and G3) were compared and analyzed. The receiver operating characteristic (ROC) curve was used to evaluate each parameter’s diagnostic accuracy and threshold. Spearman correlation analysis was used to analyze the correlations between all parameters and histological grades.ResultsThe MK values for the G0, G1, G2 and G3 groups increased gradually, while the MD and ADC values decreased gradually. Except for the differences in the ADC values between G0 and G1, the differences among the groups were statistically significant (P < 0.05). The MK values had the highest diagnostic accuracy in differentiating G0 and (G1 + G2 + G3), G0 and G1, G1 and G2, and G2 and G3 (AUC = 0.93, 0.76, 0.91, 0.91, P < 0.05). MK was maximally correlated with histological grade, followed by MD and ADC (MK > MD > ADC; r = − 0.85, + 0.82, + 0.76, P < 0.01).ConclusionBoth DKI and DWI can be used to evaluate the diagnosis and histological grading of endometrial cancer. Compared with DWI, the DKI model is a more complete mathematical model with more sensitive parameters, which can more effectively evaluate the pathological and physiological characteristics of endometrial cancer.