Mean Kurtosis Research Articles

The mean kurtosis (MK) is more sensitive diagnostic biomarker than fractional anisotropy (FA) for Parkinson's disease: A diagnostic performance study and meta-analysis.

The mean kurtosis (MK) and fractional anisotropy (FA) in patients of Parkinson's disease (PD) are usually measured by diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI), separately. In this study we perform a meta-analysis to discuss which noninvasive biomarker is more advantageous for PD, MK, or FA. Databases including Medline via PubMed, the Cochrane Central Register of Controlled Trials, Embase via OVID and China National Knowledge Infrastructure. Databases are searched up to December 31st, 2019. Four brain regions are identified for analysis based on data extracted from articles. The articles contain 5 trials with 274 total PD patients and 189 healthy controls (HCs). The results show not only significantly higher MK values of putamen, caudate, globus pallidus in PD compared to that of HCs (weighted mean difference [WMD] = 0.06, 95% CI = 0.02-0.09, P = .002, WMD = 0.03, 95% CI = 0.01-0.067, P = .01, WMD = 0.18, 95% CI = 0.11-0.24, P < .00001), but also a significantly higher FA in caudate of PD compared to HCs (WMD = 0.02, 95% CI = 0.00-0.03, P = .006). This indicates that the sharp difference detected between PD patients and HCs can be detected by DKI and DTI. By further discussing results, we found that MK could be more sensitive diagnostic biomarker than FA toward PD diagnosis.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status.

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas. Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (Dint and Dp) and fractions of strictly limited, intermediate, and perfusion-related diffusion (Fvery-slow, Fint, and Fp) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed. High-grade gliomas (HGGs) had significantly higher Fint, Fvery-slow, and Dp values but significantly lower Fp and Dint values than low-grade gliomas (LGGs), and Fint and Fp differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). Fint achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, Fint, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, Fint remained the only differentiator. Additionally, Fint and Fp showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively). The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses. • The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • Fint was useful in evaluating the grade and genotype of gliomas.

Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models

Currently, there is no noninvasive method to effectively judge the genotype of diffuse gliomas. We explored the association between mean apparent propagator-MRI (MAP-MRI) and WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion genotypes in adult-type diffuse gliomas and compared it with the diagnostic efficiency of diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI). We prospectively recruited 67 participantshistopathologically diagnosed with adult-type diffuse gliomas. Routine MRI, DKI, and DSI were performed before surgery. The extreme and average partial diffusion indexes of solid tumors were measured. A comprehensive assessment of statistically significant diffusion parameters was performed after Bonferroni correction, including ROC curves, correct classification percentage (CCP), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and k-fold cross validation. For differentiating WHO grade 2/3, q-space inverse variance (QIV), mean kurtosis (MK), non-Gaussianity (NG), and return to the origin probability (RTOP) were different (p' < .05), with the mean QIV exhibiting the best diagnostic efficacy and stability (AUC=0.973, CCP=0.906). We observed significant differences in mean diffusivity (MD), mean square displacement, QIV, MK, and RTOP between the IDH wild-type and IDH mutant groups (p' < .001) (AUC, 0.806-0.978) and MAP-MRI showed a higher IDI than DTI and DKI (0.094-0.435, NRI > 0, respectively). For the chromosome 1p/19q combined deletion, the minimum QIV was different between the overall (p' < .05) and no significant differences in MD and MK was observed. MAP-MRI effectively predicts the WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion in adult-type diffuse gliomas, and it may perform better than DTI and DKT.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis.

This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer. A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer. Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed. Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Research on the Sparse Optimization Method of Periodic Weights and its Application in Bearing Fault Diagnosis

The vibration state monitoring signal of rolling bearings usually shows periodic transient impulse characteristics at the time of the fault, but in the fault sprouting stage and actual monitoring, its impulse components are easily disturbed by random pulses, harmonics, background noise, etc., which increases the difficulty of extracting the periodic shock characteristics of the fault. This paper proposed a rolling bearing fault feature extraction method based on sparse coefficient weighting theory and periodic enhancement strategy to effectively extract weak periodic shock features with a low signal-to-noise ratio. In the proposed period weighted sparse wavelet representation (PWSWR) method, firstly, based on the sparse wavelet model, the mean kurtosis (MK) index was introduced as a weight to distinguish the contribution of the wavelet coefficients of each signal component. Secondly, a sparse signal period enhancement strategy was proposed using the improved envelope harmonic product spectrum period estimation method. The strategy is embedded into the sparse representation model to achieve the periodic enhancement of filtered signals. Finally, the envelope detection of the sparse signal was carried out to identify the fault. The results of bearing fault simulation signals and two engineering test signals showed that the proposed method could effectively extract weak fault features of bearings and had certain advantages compared with other sparse methods.

White matter structural and network topological changes in moyamoya disease with limb paresthesia: A study based on diffusion kurtosis imaging

To investigate the structural and network topological changes in the white matter (WM) in MMD patients with limb paresthesia by performing diffusion kurtosis imaging (DKI). A total of 151 MMD patients, including 46 with left-limb paresthesia (MLP), 52 with right-limb paresthesia (MRP), and 53 without paresthesia (MWP), and 28 healthy controls (HCs) underwent whole-brain DKI, while the surgical patients were reexamined 3-4 months after revascularization. The data were preprocessed to calculate the fractional anisotropy (FA) and mean kurtosis (MK) values. Voxel-wise statistics for FA and MK images were obtained by using tract-based spatial statistics (TBSS). Next, the whole-brain network was constructed, and global and local network parameters were analyzed using graph theory. All parameters were compared among the HC, MWP, MLP, and MRP groups, and changes in the MMD patients before and after revascularization were also compared. The TBSS analysis revealed significant reductions in FA and MK in extensive WM regions in the three patient groups. In comparison with the MWP group, the MLP group showed reductions in FA and MK in both right and left WM, mainly in the right WM, while the MRP group mainly showed a reduction in FA in the left WM region and demonstrated no significant change in MK. The graph theoretical analysis showed decreased global network efficiency, increased characteristic path length, and increased sigma in the MWP, MRP, and MLP groups in comparison with the HC group. Among local network parameters, the nodal efficiency decreased in the bilateral MFG and IFGtriang, while the degree decreased in the MFG.L and bilateral IFGtriang. Patients with right-limb paresthesia showed the lowest nodal efficiency and degree in MFG.L and IFGtriang.L, while those with left-limb paresthesia showed the lowest nodal efficiency in MFG.R and IFGtriang.R and the lowest degree in IFGtriang.R. A DKI-based whole-brain structural and network analysis can be used to detect changes in WM damage and network topological changes in MMD patients with limb paresthesia. FA is more sensitive than MK in detecting WM injury, while MFG and IFGtriang are the key nodes related to the development of acroparesthesia.

Preliminary Feasibility Study on Diffusion Kurtosis Imaging to Monitor the Early Functional Alterations of Kidneys in Streptozocin-Induced Diabetic Rats

The aim of this study was to investigate the potential of diffusion kurtosis imaging (DKI) to assess the early renal functional undulation of diabetic mellitus (DM). Fifty-seven Sprague-Dawley (SD) rats were randomly divided into two groups and eventually 48 rats were included in this study: the normal control (CON) group and diabetic mellitus (DM) group. Weeks 0, 4, 8, and 12 after the diabetes model was successfully established, all the rats were scanned on the 3.0T MRI. The DKI derived parameters of renal parenchyma, including fractional anisotropy (FAco, FAme), mean diffusivity (MDco, MDme), and mean kurtosis (MKco, MKme) were measured. Their alteration over time was analyzed and then correlated with urine volume (UV), blood urea nitrogen (BUN), and serum creatinine (Scr) using Pearson correlation analysis. Finally, hematoxylin and eosin (H&E) staining was performed on the kidneys of the two groups. There was a decreasing trend in FA, MK, and MD values over time in diabetic rats. Also, the gradually worsening histological damage of kidneys was noted over time in diabetic rats. The cortical FA and MK values and medullary FA, MK and MD values of diabetic rats were significantly lower than those of controls at most time points after DM induction. In addition, negative correlations were revealed between the BUN and FAco (r=-0.43, p = 0.03) or FAme value (r=-0.49, p = 0.01). The cortical MK value was moderately correlated with UV (r=-0.46, p = 0.03) and BUN (r=-0.55, p = 0.01). The preliminary findings suggest that DKI might be an effective and sensitive tool to assess the early changes of renal function impairment in diabetic rats. The FA values of the cortex and medulla and the MK value of the cortex are sensitive markers in detecting renal injury in diabetic rats.

Application of diffusion kurtosis imaging and 18F-FDG PET in evaluating the subtype, stage and proliferation status of non-small cell lung cancer.

BackgroundLung cancer has become one of the deadliest tumors in the world. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for approximately 80%-85% of all lung cancer cases. This study aimed to investigate the value of diffusion kurtosis imaging (DKI), diffusion-weighted imaging (DWI) and 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) in differentiating squamous cell carcinoma (SCC) and adenocarcinoma (AC) and to evaluate the correlation of each parameter with stage and proliferative status Ki-67.MethodsSeventy-seven patients with lung lesions were prospectively scanned by hybrid 3.0-T chest 18F-FDG PET/MR. Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), maximum standard uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The independent samples t test or Mann–Whitney U test was used to compare and analyze the differences in each parameter of SCC and AC. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis and compared with the DeLong test. A logistic regression analysis was used for the evaluation of independent predictors. Bootstrapping (1000 samples) was performed to establish a control model, and calibration curves and ROC curves were used to validate its performance. Pearson’s correlation coefficient and Spearman’s correlation coefficient were calculated for correlation analysis.ResultsThe MK and ADC values of the AC group were significantly higher than those of the SCC group (all P< 0.05), and the SUVmax, MTV, and TLG values of the SCC group were significantly higher than those of the AC group (all P<0.05). There was no significant difference in the MD value between the two groups. Moreover, MK, SUVmax, TLG and MTV were independent predictors of the NSCLC subtype, and the combination of these parameters had an optimal diagnostic efficacy (AUC, 0.876; sensitivity, 86.27%; specificity, 80.77%), which was significantly better than that of MK (AUC = 0.758, z = 2.554, P = 0.011), ADC (AUC = 0.679, z = 2.322, P = 0.020), SUVmax (AUC = 0.740, z = 2.584, P = 0.010), MTV (AUC = 0.715, z = 2.530, P = 0.011) or TLG (AUC = 0.716, z = 2.799, P = 0.005). The ROC curve showed that the validation model had high accuracy in identifying AC and SCC (AUC, 0.844; 95% CI, 0.785-0.885);. The SUVmax value was weakly positively correlated with the Ki-67 index (r = 0.340, P< 0.05), the ADC and MD values were weakly negatively correlated with the Ki-67 index (r = -0.256, -0.282, P< 0.05), and the MTV and TLG values were weakly positively correlated with NSCLC stage (r = 0.342, 0.337, P< 0.05).ConclusionDKI, DWI and 18F-FDG PET are all effective methods for assessing the NSCLC subtype, and some parameters are correlated with stage and proliferation status.

Developing a Predictive Grading Model for Children with Gliomas Based on Diffusion Kurtosis Imaging Metrics: Accuracy and Clinical Correlations with Patient Survival.

Simple SummaryThe most frequent brain tumors in children are solid tumors. A significant fraction of pediatric brain tumors is represented by gliomas, which are heterogeneous. Diffusion kurtosis imaging metrics (MK, AK, RK, FA, and ADC) have shown promising results for glioma grading in adult patients; however, it is unclear whether this technique is accurate for diagnosing high grade pediatric gliomas and if it is correlated with patient survival. In our study, we performed a retrospective whole-tumor analysis on 59 children affected by gliomas and tested (1) if DKI metrics are accurate for grading pediatric gliomas and (2) if DKI metrics are correlated with patient overall survival and progression-free survival.Purpose: To develop a predictive grading model based on diffusion kurtosis imaging (DKI) metrics in children affected by gliomas, and to investigate the clinical impact of the predictive model by correlating with overall survival and progression-free survival. Materials and methods: 59 patients with a histological diagnosis of glioma were retrospectively studied (33 M, 26 F, median age 7.2 years). Patients were studied on a 3T scanner with a standardized MR protocol, including conventional and DKI sequences. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) maps were obtained. Whole tumour volumes (VOIs) were segmented semi-automatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model to develop a probability prediction of a high-grade glioma (pHGG). Three models were tested: DTI-based, DKI-based, and combined (DTI and DKI). The grading accuracy of the resulting probabilities was tested with a receiver operating characteristics (ROC) analysis for each model. In order to account for dataset imbalances between pLGG and pHGG, we applied a random synthetic minority oversampling technique (SMOTE) analysis. Lastly, the most accurate model predictions were correlated with progression-free survival (PFS) and overall survival (OS) using the Kaplan–Meier method. Results: The cohort included 46 patients with pLGG and 13 patients with pHGG. The developed model predictions yielded an AUC of 0.859 (95%CI: 0.752–0.966) for the DTI model, of 0.939 (95%CI: 0.879–1) for the DKI model, and of 0.946 (95%CI: 0.890–1) for the combined model, including input from both DTI and DKI metrics, which resulted in the most accurate model. Sample estimation with the random SMOTE analysis yielded an AUC of 0.98 on the testing set. Model predictions from the combined model were significantly correlated with PFS (25.2 months for pHGG vs. 40.0 months for pLGG, p < 0.001) and OS (28.9 months for pHGG vs. 44.9 months for pLGG, p < 0.001). Conclusions: a DKI-based predictive model was highly accurate for pediatric glioma grading. The combined model, derived from both DTI and DKI metrics, proved that DKI-based model predictions of tumour grade were significantly correlated with progression-free survival and overall survival.

The microstructural abnormalities of cingulum was related to patients with mild cognitive impairment: a diffusion kurtosis imaging study

ObjectiveOur study aimed to investigate the correlations between microstructural changes of cingulum and patients with mild cognitive impairment (MCI) by diffusion kurtosis imaging (DKI) technique.MethodA total of 104 patients with cerebral small vessel diseases (cSVD) were retrospectively enrolled in this study. According to Montreal Cognitive Assessment Scale (MoCA) scores, these patients were divided into MCI group (n = 59) and non-MCI group (n = 45). The general clinical data was collected and analyzed. The regions of interests (ROIs) were selected for investigation in cingulum. The values of DKI parameters were measured in each ROI and compared between the two groups, the correlations between DKI parameters and MoCA scores were examined.ResultsCompared to non-MCI group, MCI patients had more severe white matter hyperintensities (WMHs) (P = 0.038) and lower MoCA scores (P < 0.01). MCI patients showed significantly decreased fractional anisotropy (FA), axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK), and kurtosis fractional anisotropy (KFA) in the left cingulum in the cingulated cortex (CgC) region (all P < 0.0125). In the left CgC region, FA, AK, MK, RK, and KFA were positively correlated with MoCA scores (r = 0.348, 0.409, 0.310, 0.441, 0.422, all P < 0.001). Meanwhile, FA, AK, MK, RK, and KFA were also positively correlated with MoCA scores (r = 0.338, 0.352, 0.289, 0.380, 0.370, all P < 0.001) in the right CgC region.ConclusionDKI technique could be used to explore the microstructural changes of cingulum in MCI patients and DKI-derived parameters might be feasible to evaluate MCI patients.

Performance analysis of the augmented complex-valued least mean kurtosis algorithm

The augmented complex-valued least mean kurtosis (ACLMK) algorithm was proposed to enhance the performance of adaptive filters by utilizing the negated kurtosis of the error signal. This paper provides a comprehensive physical insight into its stochastic behaviors and a more thorough performance analysis. By using Isserlis’ theorem, the evolutions of both the covariance and complementary covariance matrices of the weight error vector are established, based on which the expressions for transient mean-square deviation (MSD) and excess mean-square error (EMSE) are derived. Moreover, the higher-order moments of the measurement noise are also taken into account. Different from the existing framework which treats the ACLMK as a variable step-size augmented complex-valued least mean square (ACLMS) algorithm, the steady-state mean-square performance of the ACLMK is analyzed through the approximate uncorrelating transform (AUT) to yield the theoretical steady-state MSD and EMSE. The proposed framework of performance analysis is shown to provide more accurate models for the theoretical MSD and MSE. Simulation results are provided to validate the theoretical findings for second-order noncircular input signals in second-order circular and noncircular noisy environments.

Brain microstructure abnormalities in the 3xTg-AD mouse – A diffusion MRI and morphology correlation study

The widely studied triple transgenic (3xTg-AD) mouse provides a robust model of Alzheimer's disease (AD) with region dependent patterns of progressive amyloid-β (Aß) and tau pathology. Using diffusion MRI (dMRI), we investigated the sensitivity of dMRI measures in capturing AD pathology associated microstructure alterations in older 3xTg-AD mice, and the degree to which dMRI changes correlate with measurements of Aβ and tau pathology. 3xTg-AD and normal control (NC) mice, 15 to 21 months of age, were used in this study. In vivo dMRI data were acquired for the generation of diffusion tensor (DT) and diffusional kurtosis (DK) measures within the hippocampus and fimbria (Fi). For these same brain regions, Aβ and tau pathology were quantified by morphological analysis of Aß1–42 and AT8 immunoreactivity. Two-tailed, two-sample t-tests were performed to assess group differences in each brain region of interest (ROI), with the Benjamini-Hochberg false discovery rate (FDR) method being applied to adjust for multiple comparisons. Spearman correlation coefficients were calculated to investigate associations between diffusion and morphological measures. Our results revealed, depending on the brain region, DT and DK measures were able to detect group differences. In the dorsal hippocampus (HD), fractional anisotropy (FA) was significantly higher in the 3xTg-AD mice compared with NC mice. In the subiculum (SUB), FA, axial diffusivity (D||) and radial kurtosis (K┴) were significantly higher in 3xTg-AD mice compared with NC mice. Morphological quantification of Aß1–42 and AT8 immunoreactivity showed elevated Aß and tau in the Fi, ventral hippocampus (HV) and SUB of 3xTg-AD mice. The presence of Aβ and tau was significantly correlated with several DT and DK measures, particularly in the SUB, where an increase in tau correlated with an increase in mean kurtosis (MK) and K┴. This work demonstrates significant dMRI differences between older 3xTg-AD and NC mice in the hippocampus and Fi. Significant correlations were found between dMRI and morphological measures of Aβ and tau pathology. These results support the potential of dMRI-derived parameters as biomarkers of AD pathology. Since the imaging methods employed here are easily translatable to clinical MRI, our results are also relevant for human AD patients.

Intravoxel incoherent motion imaging combined with diffusion kurtosis imaging to assess the response to radiotherapy in a rabbit VX2 malignant bone tumor model

PurposeTo combine intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) parameters for the evaluation of radiotherapy response in rabbit VX2 malignant bone tumor model.Material and methodsForty-seven rabbits with bone tumor were prospectively enrolled and divided into pre-treatment, considerable effect and slight effect group. Treatment response was evaluated using IVIM-DKI. IVIM-based parameters (tissue diffusion [Dt], pseudo-diffusion [Dp], perfusion fraction [fp]), and DKI-based parameters (mean diffusion coefficient [MD] and mean kurtosis [MK]) were calculated for each animal. Corresponding changes in MRI parameters before and after radiotherapy in each group were studied with one-way ANOVA. Correlations of diffusion parameters of IVIM and DKI model were computed using Pearson’s correlation test. A diagnostic model combining different diffusion parameters was established using binary logistic regression, and its ROC curve was used to evaluate its diagnostic performance for determining considerable and slight effect to malignant bone tumor.ResultsAfter radiotherapy, Dt and MD increased, whereas fp and MK decreased (p < 0.05). The differences in Dt, fp, MD, and MK between considerable effect and slight effect groups were statistically significant (p < 0.05). A combination of Dt, fp, and MK had the best diagnostic performance for differentiating considerable effect from slight effect (AUC = 0.913, p < 0.001).ConclusionsA combination of IVIM- and DKI-based parameters allowed the non-invasive assessment of cellular, vascular, and microstructural changes in malignant bone tumors after radiotherapy, and holds great potential for monitoring the efficacy of tumor radiotherapy.

Oxygen Challenge Imaging Reveals Differences in Metabolic Activity Between Kurtosis Lesion and Diffusion/Kurtosis Lesion Mismatch in a Rodent Model of Acute Stroke.

Accurate identification of potentially salvageable tissues is critical for improving acute stroke treatment. A previous study showed that the kurtosis lesion exhibited insignificant response after prompt reperfusion treatment, while the diffusion/kurtosis lesion mismatch could recover after reperfusion. We hypothesized that these 2 regions are in different metabolic states. Transient oxygen challenge (OC) is a procedure that uses oxygen as a metabolic bio-tracer and has been performed to explore metabolic activity in tissues. We combined OC with multiparameter magnetic resonance imaging (including diffusion kurtosis imaging and T2* mapping sequences) to study metabolic activity in the ischemic brain of Sprague Dawley rats. Oxygen challenge image analysis revealed changes in T2* values, most significantly in the mean diffusivity (MD)/mean kurtosis (MK) lesion mismatch (22.3 ± 1.6%) and least significantly in the MK lesions (6.6 ± 0.6%). The MD images acquired within 138 ± 9 minutes after ischemia showed a larger ischemic lesion (45.5 ± 3.0% of the total area) than the MK images (33.2 ± 4.2% of the total area). The change rate of the MK value (53.0 ± 4.4%) was higher than that of the MD value (37.5 ± 3.2%). The present study shows that MK lesion and MD/MK lesion mismatch exhibited different metabolic activity states. The MK lesion presented metabolic-related values close to the ischemic core, while at least part of the MD/MK mismatch area was comparable with ischemic penumbra metabolic activity. These findings are important to support image-guided individualized stroke therapies.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease.

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.

A comparative study of functional MRI in predicting response of regional nodes to induction chemotherapy in patients with nasopharyngeal carcinoma.

PurposeTo identify and compare the value of functional MRI (fMRI) in predicting the early response of metastatic cervical lymph nodes (LNs) to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) patients.MethodsThis prospective study collected 94 metastatic LNs from 40 consecutive NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion-weighted imaging, diffusion kurtosis imaging, intravoxel incoherent motion, and dynamic contrast-enhanced magnetic resonance imaging were performed before and after IC. The parameter maps apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), mean kurtosis (MK), Dslow, Dfast, perfusion fraction (PF), Ktrans, Ve, and Kep) of the metastatic nodes were calculated by the Functool postprocessing software. All LNs were classified as the responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between the RG and the NRG. The significant parameters are fitted by logistic regression analysis to produce new predictive factor (PRE)–predicted probabilities. Logistic regression analysis and receiver operating characteristic (ROC) curves were performed to further identify and compare the efficacy of the parameters.ResultsAfter IC, the mean values of ADC, MD, and Dslow significantly increased, while MK, Dfast, and Ktrans values decreased dramatically, while no significant difference was detected in Ve and Kep. Compared with NRG, PF-pre and Ktrans-pre values in the RG were higher statistically. The areas under the ROC for the pretreatment PF, Ktrans, and PRE were 0.736, 0.722, and 0.810, respectively, with the optimal cutoff value of 222 × 10-4, 934 × 10-3/min, and 0.6624, respectively.ConclusionsThe pretreatment fMRI parameters PF and Ktrans showed promising potential in predicting the response of the metastatic LNs to IC in NPC patients.Clinical Trial RegistrationThis study was approved by the ethics board of the Chinese PLA General Hospital, and registered on 30 January 2021, in the Chinese Clinical Trial Registry; http://www.chictr.org.cn/showproj.aspx?proj=121198, identifier (ChiCTR2100042863).

Single Center to Evaluate and Compare Anisometropic Amblyopia in Adults Using Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging and Diffusion Kurtosis Imaging.

BackgroundAnisometropic amblyopia results from the unequal ability to focus between the right and left eyes. Blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) measures the proportion of oxygenated hemoglobin in specific areas. Diffusion kurtosis imaging (DKI) is a method of diffusion tensor imaging that estimates the skewed distribution of water diffusion probability. We aimed to evaluate and compare 11 adult patients with anisometropic amblyopia (AA) with 13 normally sighted healthy controls (HC) using BOLD-fMRI and DKI.Material/MethodsEleven adults with AA (age range 20–49; mean age 29.18±8.089) and 13 HC adults (age range 22–50; mean age 28.00±5.79) were recruited. DKI scanning used a single excitation echo-planar imaging sequence and a region of interest to obtain DKI parameters for optic radiation; the corpus callosum was manually placed, including mean kurtosis (MK), fractional anisotropic (FA), and mean diffusivity (MD) values; and BOLD data used a gradient-echo echo-planar imaging sequence.ResultsThe AA group had lower MK and FA of bilateral optic radiation than the HC group (P=0.008 and P=0.006, respectively) and higher MD than the HC group (P=0.005). The MK of the corpus callosum in the AA group was lower than that of HC group (P=0.012).Compared with the non-dominant eyes of the HC group, the amblyopic eyes in the AA group had less activation range and intensity in Brodmann areas 17, 18, and 19.ConclusionsThe combined use of DKI and BOLD-fMRI detected microstructural changes associated with local visual pathways and identified damage to the visual cortex in patients with amblyopia.

Microstructural Differences of the Cerebellum-Thalamus-Basal Ganglia-Limbic Cortex in Patients with Somatic Symptom Disorders: a Diffusion Kurtosis Imaging Study.

Somatic symp tom disorders (SSDs) are a group of psychiatric disorders characterized by persistent disproportionate concern and obsessive behaviors regarding physical conditions. Currently, SSDs lack effective treatments and their pathophysiology is unclear. In this paper, we aimed to examine microstructural abnormalities in the brains of patients with SSD using diffusion kurtosis imaging (DKI) and to investigate the correlation between these abnormalities and clinical indicators. Diffusion kurtosis images were acquired from 30 patients with SSD and 30 healthy controls (HCs). Whole-brain maps of multiple diffusion measures, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD), mean kurtosis (MK), radial kurtosis (RK), and axial kurtosis (AK), were calculated. To analyze differences between the two groups, nonparametric permutation testing with 10,000 randomized permutations and threshold-free cluster enhancement was used with family-wise error-corrected p values < 0.05 as the threshold for statistical significance. Then, the correlations between significant changes in these diffusion measures and clinical factors were examined. Compared to HCs, patients with SSD had significantly higher FA, MK, and RK and significantly lower MD and RD in the cerebellum, thalamus, basal ganglia, and limbic cortex. The FA in the left caudate and the pontine crossing tract were negatively correlated with disease duration; the MD and the RD in the genu of the corpus callosum were positively correlated with disease duration. Our findings highlight the role of the cerebellum-thalamus-basal ganglia-limbic cortex pathway, especially the cerebellum, in SSDs and enhance our understanding of the pathogenesis of SSDs.

Comparison of brain microstructure alterations on diffusion kurtosis imaging among Alzheimer's disease, mild cognitive impairment, and cognitively normal individuals.

ObjectiveOur study aimed to explore the differences in brain microstructure in patients with Alzheimer’s disease (AD) and with mild cognitive impairment (MCI) and in individuals with normal cognition using diffusion kurtosis imaging (DKI) to identify a potential non-invasive biomarker of AD.Materials and methodsA total of 61 subjects were included in our study, including 20 subjects diagnosed with AD, 21 patients diagnosed with amnestic MCI, and 20 cognitively normal individuals. We acquired magnetic resonance imaging (MRI) scans, and DKI images were processed. Twelve regions of interest were drawn, and various parameters were measured and analyzed using SPSS version 11.0 software.ResultsComparative analysis showed that differences in brain regions in terms of mean diffusion (MD) and mean kurtosis (MK) between groups were the most marked. Precuneus MD, temporal MK, precuneus MK, and hippocampal MK were significantly correlated with neuropsychological test scores. Hippocampal MK showed the strongest correlation with the medial temporal lobe atrophy score (r = −0.510), and precuneus MD had the strongest correlation with the Koedam score (r = 0.463). The receiver operating curve analysis revealed that hippocampal MK exhibited better diagnostic efficacy than precuneus MD for comparisons between any group pair.ConclusionDKI is capable of detecting differences in brain microstructure between patients with AD, patients with MCI, and cognitively normal individuals. Moreover, it compensates for the deficiencies of conventional MRI in detecting pathological changes in microstructure before the appearance of macroscopic atrophy. Hippocampus MK was the most sensitive single parameter map for differentiating patients with AD, patients with MCI, and cognitively normal individuals.

The value of diffusion kurtosis imaging, diffusion weighted imaging and 18F-FDG PET for differentiating benign and malignant solitary pulmonary lesions and predicting pathological grading.

ObjectiveTo explore the value of PET/MRI, including diffusion kurtosis imaging (DKI), diffusion weighted imaging (DWI) and positron emission tomography (PET), for distinguishing between benign and malignant solitary pulmonary lesions (SPLs) and predicting the histopathological grading of malignant SPLs.Material and methodsChest PET, DKI and DWI scans of 73 patients with SPL were performed by PET/MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), maximum standard uptake value (SUVmax), metabolic total volume (MTV) and total lesion glycolysis (TLG) were calculated. Student’s t test or the Mann–Whitney U test was used to analyze the differences in parameters between groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy. Logistic regression analysis was used to evaluate independent predictors.ResultsThe MK and SUVmax were significantly higher, and the MD and ADC were significantly lower in the malignant group (0.59 ± 0.13, 10.25 ± 4.20, 2.27 ± 0.51[×10-3 mm2/s] and 1.35 ± 0.33 [×10-3 mm2/s]) compared to the benign group (0.47 ± 0.08, 5.49 ± 4.05, 2.85 ± 0.60 [×10-3 mm2/s] and 1.67 ± 0.33 [×10-3 mm2/s]). The MD and ADC were significantly lower, and the MTV and TLG were significantly higher in the high-grade malignant SPLs group (2.11 ± 0.51 [×10-3 mm2/s], 1.35 ± 0.33 [×10-3 mm2/s], 35.87 ± 42.24 and 119.58 ± 163.65) than in the non-high-grade malignant SPLs group (2.46 ± 0.46 [×10-3 mm2/s], 1.67 ± 0.33[×10-3 mm2/s], 20.17 ± 32.34 and 114.20 ± 178.68). In the identification of benign and malignant SPLs, the SUVmax and MK were independent predictors, the AUCs of the combination of SUVmax and MK, SUVmax, MK, MD, and ADC were 0.875, 0.787, 0.848, 0.769, and 0.822, respectively. In the identification of high-grade and non-high-grade malignant SPLs, the AUCs of MD, ADC, MTV, and TLG were 0.729, 0.680, 0.693, and 0.711, respectively.ConclusionDWI, DKI, and PET in PET/MRI are all effective methods to distinguish benign from malignant SPLs, and are also helpful in evaluating the pathological grading of malignant SPLs.