scholarly journals Diffusion kurtosis imaging with free water elimination: A bayesian estimation approach

2018 ◽  
Vol 80 (2) ◽  
pp. 802-813 ◽  
Author(s):  
Quinten Collier ◽  
Jelle Veraart ◽  
Ben Jeurissen ◽  
Floris Vanhevel ◽  
Pim Pullens ◽  
...  
Keyword(s):  
Bayesian Estimation ◽  
Free Water ◽  
Diffusion Kurtosis Imaging ◽  
Water Elimination ◽  
Diffusion Kurtosis

scholarly journals The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michaela Bartoňová ◽  
Marek Bartoň ◽  
Pavel Říha ◽  
Lubomír Vojtíšek ◽  
Milan Brázdil ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Diffusion Kurtosis Imaging ◽  
Epileptogenic Zone ◽  
Diffusion Weighted Mri ◽  
Specific Detection ◽  
Presurgical Evaluation ◽  
Brain Areas ◽  
Specific Restriction ◽  
Diffusion Kurtosis ◽  
Selection Of

AbstractThe effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.

scholarly journals Diffusion kurtosis imaging detects subclinical white matter abnormalities in Phenylketonuria

2021 ◽  
Vol 29 ◽  
pp. 102555
Author(s):  
Sarah C. Hellewell ◽  
Thomas Welton ◽  
Kate Eisenhuth ◽  
Michel C. Tchan ◽  
Stuart M. Grieve
Keyword(s):  
White Matter ◽  
Diffusion Kurtosis Imaging ◽  
White Matter Abnormalities ◽  
Diffusion Kurtosis

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 IMG-14. DEVELOPING A PREDICTIVE GRADING MODEL FOR CHILDREN WITH GLIOMAS BASED ON DIFFUSION KURTOSIS IMAGING METRICS: ACCURACY AND CLINICAL CORRELATIONS WITH SURVIVAL

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii357-iii358
Author(s):  
Ioan Paul Voicu ◽  
Antonio Napolitano ◽  
Alessia Carboni ◽  
Lorenzo Lattavo ◽  
Andrea Carai ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
High Grade Glioma ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Free Survival ◽  
Kaplan Meier ◽  
Model Predictions ◽  
Probability Prediction ◽  
Grading Model ◽  
Diffusion Kurtosis

Abstract 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 model via correlations with overall survival and progression-free survival. MATERIALS AND METHODS We retrospectively studied 59 children (33M, 26F, median age 7.2 years) affected by gliomas on a 3T magnet. Patients with tumor locations other than infratentorial midline were included. Conventional and DKI sequences were obtained. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps were obtained. Whole tumor volumes (VOIs) were segmented semiautomatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model with penalized logistic regression (glmnet package, R). Elasticnet regularization was used to avoid model overfitting. Fitted model coefficients from each metric were used to develop a probability prediction of a high-grade glioma (HGG). Grading accuracy of the resulting probabilities was tested with ROC analysis. Finally, model predictions were correlated to progression-free survival (PFS) with a Kaplan-Meier analysis. RESULTS The cohort included 46 patients with low-grade gliomas (LGG) and 13 patients with HGG. The developed model predictions yielded an AUC of 0.946 (95%CI: 0.890–1). Model predictions were significantly correlated with PFS (23.1 months for HGG vs 34.7 months for LGG, p<0.004). CONCLUSION In our cohort, a DKI-based predictive model was highly accurate for pediatric glioma grading. DKI-based model predictions were significantly correlated with progression-free survival.

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.

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