scholarly journals Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging

2021 ◽  
Vol 10 (11) ◽  
pp. 2325
Author(s):  
Johann-Martin Hempel ◽  
Cornelia Brendle ◽  
Sasan Darius Adib ◽  
Felix Behling ◽  
Ghazaleh Tabatabai ◽  
...  
Keyword(s):  
Mean Diffusivity ◽  
World Health ◽  
Diffusion Kurtosis Imaging ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Normal Brain ◽  
Diffuse Glioma ◽  
Whole Brain ◽  
Specific Combination ◽  
Diffusion Kurtosis

Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.

Whole brain atlas-based diffusion kurtosis imaging parameters for evaluation of minimal hepatic encephalopathy

2021 ◽  
pp. 197140092110269
Author(s):  
Prateek Gupta ◽  
Sameer Vyas ◽  
Teddy Salan ◽  
Chirag Jain ◽  
Sunil Taneja ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Clinical Symptoms ◽  
Early Stage ◽  
Imaging Techniques ◽  
Clinical Stage ◽  
Minimal Hepatic Encephalopathy ◽  
Brain Atlas ◽  
Diffusion Kurtosis Imaging ◽  
Whole Brain ◽  
Diffusion Kurtosis

Background and purposes Minimal hepatic encephalopathy (MHE) has no recognizable clinical symptoms, but patients have cognitive and psychomotor deficits. Hyperammonemia along with neuroinflammation lead to microstructural changes in cerebral parenchyma. Changes at conventional imaging are detected usually at the overt clinical stage, but microstructural alterations by advanced magnetic resonance imaging techniques can be detected at an early stage. Materials and methods Whole brain diffusion kurtosis imaging (DKI) data acquired at 3T was analyzed to investigate microstructural parenchymal changes in 15 patients with MHE and compared with 15 age- and sex-matched controls. DKI parametric maps, namely kurtosis fractional anisotropy (kFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK), were evaluated at 64 white matter (WM) and gray matter (GM) regions of interest (ROIs) in the whole brain and correlated with the psychometric hepatic encephalopathy score (PHES). Results The MHE group showed a decrease in kFA and AK across the whole brain, whereas MK and RK decreased in WM ROIs but increased in several cortical and deep GM ROIs. These alterations were consistent with brain regions involved in cognitive function. Significant moderate to strong correlations (–0.52 to –0.66; 0.56) between RK, MK and kFA kurtosis metrics and PHES were observed. Conclusion DKI parameters show extensive microstructural brain abnormalities in MHE with minor correlation between the severity of tissue damage and psychometric scores.

scholarly journals Magnetic resonance diffusion kurtosis imaging in differential diagnosis of benign and malignant renal tumors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianxiong Fu ◽  
Jing Ye ◽  
Wenrong Zhu ◽  
Jingtao Wu ◽  
Wenxin Chen ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Mean Diffusivity ◽  
Threshold Value ◽  
Renal Tumors ◽  
Diffusion Kurtosis Imaging ◽  
Renal Oncocytoma ◽  
Renal Angiomyolipoma ◽  
Planar Imaging ◽  
Cell Renal Cell Carcinoma ◽  
Diffusion Kurtosis

Abstract Background Benign and malignant renal tumors share similar some imaging findings. Methods Sixty-six patients with clear cell renal cell carcinoma (CCRCC), 13 patients with renal angiomyolipoma with minimal fat (RAMF) and 7 patients with renal oncocytoma (RO) were examined. For diffusion kurtosis imaging (DKI), respiratory triggered echo-planar imaging sequences were acquired in axial plane (3 b-values: 0, 500, 1000s/mm2). Mean Diffusivity (MD), fractional Anisotropy (FA), mean kurtosis (MK), kurtosis anisotropy (KA) and radial kurtosis (RK) were performed. Results For MD, a significant higher value was shown in CCRCC (3.08 ± 0.23) than the rest renal tumors (2.93 ± 0.30 for RO, 1.52 ± 0.24 for AML, P < 0.05). The MD values were higher for RO than for AML (2.93 ± 0.30 vs.1.52 ± 0.24, P < 0.05), while comparable MD values were found between CCRCC and RO (3.08 ± 0.23 vs. 2.93 ± 0.30, P > 0.05). For MK, KA and RK, a significant higher value was shown in AML (1.32 ± 0.16, 1.42 ± 0.23, 1.41 ± 0.29) than CCRCC (0.43 ± 0.08, 0.57 ± 0.16, 0.37 ± 0.11) and RO (0.81 ± 0.08, 0.86 ± 0.16, 0.69 ± 0.08) (P < 0.05). The MK, KA and RK values were higher for RO than for CCRCC (0.81 ± 0.08 vs. 0.43 ± 0.08, 0.86 ± 0.16 vs. 0.57 ± 0.16, 0.69 ± 0.08 vs. 0.37 ± 0.11, P < 0.05). Using MD values of 2.86 as the threshold value for differentiating CCRCC from RO and AML, the best result obtained had a sensitivity of 76.1%, specificity of 72.6%. Using MK, KA and RK values of 1.19,1.13 and 1.11 as the threshold value for differentiating AML from CCRCC and RO, the best result obtained had a sensitivity of 91.2, 86.7, 82.1%, and specificity of 86.7, 83.2, 72.8%. Conclusion DKI can be used as another noninvasive biomarker for benign and malignant renal tumors’ differential diagnosis.

scholarly journals Diffusion Kurtosis Imaging as a Prognostic Marker in Osteosarcoma Patients with Preoperative Chemotherapy

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chenglei Liu ◽  
Yue Xing ◽  
Dongmin Wei ◽  
Qiong Jiao ◽  
Qingcheng Yang ◽  
...  
Keyword(s):  
Preoperative Chemotherapy ◽  
Tumor Resection ◽  
Mean Diffusivity ◽  
Cox Proportional Hazards ◽  
Diffusion Kurtosis Imaging ◽  
Rank Test ◽  
Poor Responders ◽  
Risk Of Death ◽  
Increased Risk ◽  
Diffusion Kurtosis

Background. The accurate prediction of prognosis is key to prompt therapy adjustment. The purpose of our study was to investigate the efficacy of diffusion kurtosis imaging (DKI) in predicting progression-free survival (PFS) and overall survival (OS) in osteosarcoma patients with preoperative chemotherapy. Methods. Thirty patients who underwent DKI before and after chemotherapy, followed by tumor resection, were retrospectively enrolled. The patients were grouped into good responders (GRs) and poor responders (PRs). The Kaplan-Meier and log-rank test were used for survival analysis. The association between the DKI parameters and OS and PFS was performed by univariate and multivariate Cox proportional hazards models. Results. Significantly worse OS and PFS were associated with a lower mean diffusivity (MD) after chemotherapy (HR, 5.8; 95% CI, 1.5-23.1; P=0.012 and HR, 3.5; 95% CI, 1.2-10.1: P=0.028, respectively) and a higher mean kurtosis (MK) after chemotherapy (HR, 0.3; 95% CI, 0.1-0.9; P=0.041 and HR, 0.3; 95% CI, 0.1-0.8; P=0.049, respectively). Likewise, shorter OS and PFS were also significantly associated with a change rate in MD (CR MD) of less than 13.53% (HR, 8.6; 95% CI, 1.8-41.8; P=0.007 and HR, 2.9; 95% CI, 1.0-8.2; P=0.045, respectively). Compared to GRs, PRs had an approximately 9- and 4-fold increased risk of death (HR, 9.4; 95% CI, 1.2-75; P=0.034) and progression (HR, 4.2; 95% CI, 1.2-15; P=0.026), respectively. Conclusions. DKI has a potential to be a prognostic tool in osteosarcoma. Low MK and high MD after chemotherapy or high CR MD indicates favorite outcome, while prospective studies with large sample sizes are warranted.

scholarly journals The Value of Diffusion Kurtosis Imaging In Detecting Delayed Brain Development of Premature Infants

2021 ◽  
Author(s):  
Xin Zhao ◽  
Chunxiang Zhang ◽  
Bohao Zhang ◽  
Jiayue Yan ◽  
Kaiyu Wang ◽  
...  
Keyword(s):  
White Matter ◽  
Caudate Nucleus ◽  
Premature Infants ◽  
Developmental Disorders ◽  
Neurodevelopmental Outcome ◽  
Mean Diffusivity ◽  
Internal Capsule ◽  
Diffusion Kurtosis Imaging ◽  
Control Group ◽  
Diffusion Kurtosis

Abstract Objective Preterm infants are at high risk of adverse neurodevelopmental outcome. Our aim is to explore the value of diffusion kurtosis imaging (DKI) in diagnosing brain developmental disorders in premature infants.Materials and Methods A total of 52 subjects were included in this study, including 26 premature infants as the preterm group, and 26 full-term infants as the control group. Routine magnetic resonance imaging and DKI examination were performed. Mean kurtosis (MK), radial kurtosis (RK), fractional anisotropy (FA), mean diffusivity (MD) values were measured in the brain regions including posterior limbs of the internal capsule (PLIC); anterior limb of internal capsule (ALIC); parietal white matter (PWM); frontal white matter (FWM); thalamus (TH); caudate nucleus (CN); genu of the corpus callosum (GCC). The X2, t test, Spearman’s correlation analysis and receiver operating characteristic curve (ROC)were used for data analyses.Results In the premature infant group, the MK and RK values of PLIA, ALIC, and PWM were lower than those in the control group (P<0.05). The FA values of PWM, FWM and TH were also lower than those of the control group (P<0.05). The AUCs of MK in PLIC and ALIC, MD in PWM, and FA in FWM were 0.813, 0.802, 0.842 and 0.867 (P<0.05). In thalamus and caudate nucleus, the correlations between MK, RK values and PMA were higher than those between FA, MD values and PMA.Conclusions DKI can be used as an effective tool in detecting brain developmental disorders in premature infants.

scholarly journals Diffusion Kurtosis Imaging Detects Microstructural Changes in the Brain after Acute Alcohol Intoxication in Rats

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Xi-ran Chen ◽  
Jie-ying Zeng ◽  
Zhi-Wei Shen ◽  
Ling-mei Kong ◽  
Wen-bin Zheng
Keyword(s):  
Frontal Lobe ◽  
Diffusion Tensor ◽  
Alcohol Intoxication ◽  
Mean Diffusivity ◽  
Diffusion Kurtosis Imaging ◽  
Acute Alcohol Intoxication ◽  
Additional Information ◽  
Diffusion Kurtosis ◽  
The Brain ◽  
Acute Intake

The aim of this study was to test the technical feasibility of diffusion kurtosis imaging (DKI) in the brain after acute alcohol intoxication. Diffusion tensor imaging (DTI) and DKI during 7.0 T MRI were performed in the frontal lobe and thalamus before and 30 min, 2 h, and 6 h after ethyl alcohol administration. Compared with controls, mean kurtosis values of the frontal lobe and thalamus first decreased by 44% and 38% within 30 min (p<0.01 all) and then increased by 14% and 46% at 2 h (frontal lobe, p>0.05; thalamus, p<0.01) and by 29% and 68% at 6 h (frontal lobe, p<0.05; thalamus, p<0.01) after acute intake. Mean diffusivity decreased significantly in both the frontal lobe and the thalamus at various stages. However, fractional anisotropy decreased only in the frontal lobe, with no detectable change in the thalamus. This demonstrates that DKI possesses sufficient sensitivity for tracking pathophysiological changes at various stages associated with acute alcohol intoxication and may provide additional information that may be missed by conventional DTI parameters.

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

2019 ◽  
Vol 14 (2) ◽  
pp. 627-638
Author(s):  
Qing Sun ◽  
Wenliang Fan ◽  
Yuan Liu ◽  
Yan Zou ◽  
Natalie Wiseman ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Mean Diffusivity ◽  
Minimal Hepatic Encephalopathy ◽  
Health Concern ◽  
Diffusion Kurtosis Imaging ◽  
Neurocognitive Performance ◽  
Microstructural Alterations ◽  
Additional Information ◽  
Cirrhotic Patients ◽  
Diffusion Kurtosis

Abstract 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.

Differentiation of microinfiltration and simple-edema areas in VX2 bone tumors by diffusion kurtosis imaging in animal experiments: a preliminary study

2021 ◽  
pp. 028418512110175
Author(s):  
Fang Yajun ◽  
Tang Limin ◽  
Zhou Ruizhi ◽  
Xu Qi ◽  
Chen Haisong
Keyword(s):  
Bone Tumors ◽  
Animal Experiments ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
Diffusion Kurtosis Imaging ◽  
Tumor Models ◽  
Roc Curve Analysis ◽  
Malignant Bone Tumors ◽  
Diagnostic Values ◽  
Diffusion Kurtosis

Background Dual-energy computed tomography, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to distinguish microinvasion areas of malignant bone tumors. However, reports of diffusion kurtosis imaging (DKI) to determine the extent of intramedullary infiltration are relatively rare. Purpose To assess the application value of MR-DKI in differentiating areas of microinfiltration and simple edema in rabbit bone VX2 tumor models. Material and Methods Conventional MRI and DKI were performed on 25 successfully constructed rabbit VX2 bone tumor models. We acquired a midline sagittal section of the tumor for hematoxylin and eosin staining. Using pathological findings as the gold standard and combining them with MRI data, strict point-to-point control was performed to delineate regions of interest (ROIs) in the microinfiltration and simple-edema areas of bone tumors for quantitative measurement of mean diffusivity (MD) and mean kurtosis (MK). MD and MK values between microinfiltration and simple-edema areas were compared using an independent sample t-test, and the diagnostic values were evaluated by receiver operating characteristic (ROC) curve analysis. Results In comparison with the simple-edema area, the micro-infiltration area demonstrated significantly smaller MD values and larger MK values ( P < 0.05), and MD showed a better area under the curve (AUC) than MK (AUC = 0.884 vs. AUC = 0.690) for distinguishing the microinfiltration area from the simple-edema area. The optimal cutoff MD value was 1108.5 mm2/s with a sensitivity of 84% and specificity of 84%. Conclusion DKI can distinguish the microinfiltration and simple-edema areas of malignant bone tumors in animal experiments.

scholarly journals Diffusion Kurtosis Imaging in the Assessment of Peritumoral Brain Edema in Glioblastomas and Brain Metastases

2017 ◽  
pp. 97-112 ◽  
Author(s):  
A. M. Turkin ◽  
E. L. Pogosbekyan ◽  
A. C. Tonoyan ◽  
E. I. Shults ◽  
I. I. Maximov ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Contrast Enhancement ◽  
Diffusion Kurtosis Imaging ◽  
Acquisition Time ◽  
Normal Brain ◽  
Peritumoral Edema ◽  
Brain White Matter ◽  
Significant Difference ◽  
Diffusion Kurtosis

Aim: to explore the opportunities of application of diffusionkurtosis imaging (DKI) for assessment and estimation of diffusion scalar metrics in different locations of peritumoral edema for extra- and intracerebral tumors and in contralateral normal tissue.Materials and methods. 38 patients with supratentorial brain tumors were investigated: 24 (63%) patients with primarily revealed glioblastomas (GB) and 14 (37%) patients with solitary cancer brain metastasis (MTS). MRI was performed on 3.0 T MR-scanner (Signa HDxt, General Electric, USA) with the standard protocols for brain tumor and additional protocol for DKI. The standard protocol for brain tumor included: T1-, T2-weighted images, T2-FLAIR, DWI,  T1 with contrast enhancement. Diffusion kurtosis MRI based on SE  EPI with TR = 10000 ms, TE = 102 ms, FOV = 240 mm, isotropic voxel size 3 × 3 × 3 mm3, 60 noncoplanar diffusion directions. We  used three b-values: 0, 1000 and 2500 s/mm2. Аcquisition time was 22 min. Total acquisition time was near 40 min. This study was approved by Ethical committee of Burdenko National Scientific  and Practical Center for Neurosurgery. Parametric maps were  constructed for the following diffusion coefficients: mean (MK),  transverse / radial (RK), longitudinal / axial (AK) kurtozis; medium  (MD), transverse / radial (RD) and longitudinal / axial (AD) diffusion; fractional anisotropy (FA) and a bi-exponential diffusion model  coefficients: axonal water fractions (AWF), axial (AxEAD) and radial  (RadEAD) extra-axonal water diffusion and the water molecules  trajectory tortuosity index (TORT). Normative quantitative indicators  were obtained for the six regions of the peritumoral zone as they  moved away from the tumor (region 2) to the edema periphery  (regions 4–5), as well as in the normal brain on the contralateral  hemisphere (C/L) (zone 7). A comparative analysis of these  indicators was conducted for cases with GB and MTS. DKI scalar metrics were estimated using Explore DTI (http://www.exploredti.com/).Results. Anatomic MRI (T1 without/with contrast enhancement) for  all cases with GB and MTS visualized a contrast enhancement tumor.  The peritumoral edema, spreading mainly over the brain white  matter, was well visualized on T2-FLAIR. Diffusion kurtosis  coefficients decreased in the near peritumoral edema (regions 2–3)  and a gradually increased to the edema periphery (regions 5–6). In Region 2, MK in both GB and MTS groups were MKGB(2) = 0.637 ±  0.140 and MKMTS(2) = 0.550 ± 0.046; RK in this region were  RKGB(2) = 0.690 ± 0.154 and RKMTS (2) = 0.584 ± 0.051.  Differences both MK and RK coefficients in patients with GB and MTS of region 2 were significant (p < 0.001). There were no differences in AK values for GB and MTS in region 2 (p > 0.05), but in regions 3  and 4 differences were observed (p < 0.01). The minimum value of  AK in the central edema (regions 3–4) was AKMTS(3–4) = 0.433 ± 0.063 in patients with MTS. The values of MK and RK on the  contralateral side in patients with MTS were significantly higher than  in the GB group (p < 0.02); MKC/LMTC = 0.954 ± 0.140, RKC/LMTC  = 1.257 ± 0.308 and MKC/LGB = 0.829 ± 0.146, RKc/LGB = 0.989  ± 0.282. There was no significant difference for contralateral AK between the groups.Conclusions. We found that DKI scalar metrics are the sensitive  tumor biomarkers. It allows us to perform a robust differentiation  between the infiltrating GB tumor and purely vasogenic edema of  МТS. The obtained results will allow further differential diagnosis of  extra- and intracerebral tumors and can be used to plan surgical /  radiosurgical treatment for brain tumors.

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

2020 ◽  
Vol 61 (9) ◽  
pp. 1228-1239
Author(s):  
Xiaodan Chen ◽  
Lin Lin ◽  
Jie Wu ◽  
Guang Yang ◽  
Tianjin Zhong ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Mean Diffusivity ◽  
Labeling Index ◽  
Histogram Analysis ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
High Grade ◽  
Ki 67 ◽  
Diffusion Kurtosis ◽  
Median Maximum

Background Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients. Purpose To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas. Material and Methods A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann–Whitney U test, Kruskal–Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index. Results Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index ( P < 0.05). Conclusion The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.

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