Susceptibility‐weighted and diffusion kurtosis imaging to evaluate encephalomalacia with epilepsy after traumatic brain injury

Li, Wenbin; Wang, Xuan; Wei, Xiaoer; Wang, Mingliang

doi:10.1002/acn3.552

“…A variety of causes can lead to liquefaction and necrosis of brain tissue and the formation of encephalomalacia [ 9 ]. These causes include trauma, cerebrovascular disease, and intracranial infection [ 10 ]. The pathological manifestations of brain soft focus ranged from early neuronal necrosis to neuronal disappearance and then to glial cell proliferation.…”

Section: Discussionmentioning

confidence: 99%

Screening of Risk Factors for Poor Prognosis in Patients with Refractory Epilepsy Secondary to Encephalomalacia

Zhong

¹

2022

Computational and Mathematical Methods in Medicine

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Objective. The study was aimed at screening the independent prognostic risk factors for refractory epilepsy associated with encephalomalacia (REAE). Methods. Patients with REAE treated in the First People’s Hospital of Linping District from January 2018 to December 2019 were selected. The prognosis was represented by Engel grading. Clinical data of the patients were collected, including age, sex, BMI, lesion sites, number of lesion sites, lesion size, seizure frequency, epilepsy type, and treatment methods. Independent risk factors for poor prognosis were screened by logistic regression analysis. The receiver operating characteristic curve (ROC) was used to evaluate the prognostic efficacy of independent risk factors. Results. A total of 48 patients were included in this study, including 31 patients (64.58%) in the good prognosis group and 17 patients (35.42%) in the poor prognosis group. The mean age of the poor prognosis group was higher than that of the good prognosis group ( P = 0.002 ). The proportion of patients with multisite lesions in the poor prognosis group was higher than that in the good prognosis group ( P = 0.016 ). The proportion of patients with cerebral malacia lesion diameter ≥ 3 cm in the poor prognosis group was higher than that in the good prognosis group ( P = 0.002 ). The proportion of patients with attack frequency ≥ 2 times / month in the poor prognosis group was higher than in the good prognosis group ( P = 0.002 ). The proportion of patients receiving surgical treatment in the poor prognosis group was lower than that in the good prognosis group ( P < 0.001 ). Age, number of lesion sites, size of encephalomalacia, and seizure frequency were independent risk factors for the prognosis of patients with REAE ( OR > 1 , P < 0.05 ). Surgical treatment was an independent protective factor associated with the prognosis of patients with REAE ( OR < 1 , P < 0.05 ). The area under the ROC curve of surgical treatment was 0.83 ( P = 0.004 ). The area under the ROC curve of the size of encephalomalacia was 0.72 ( P = 0.008 ). There was a positive correlation between age and size of encephalomalacia and Engel grade ( r > 0 , P < 0.05 ). Surgical treatment was negatively correlated with Engel grade ( r < 0 , P < 0.05 ). The number of lesion sites and seizure frequency had no significant correlation with Engel ( P > 0.05 ). The proportion of Engel I patients treated with surgery was higher than that treated with drugs ( P = 0.001 ). The ratio of Engel III and IV patients treated with surgery was lower than that treated with medications ( P < 0.05 ). Conclusion. Age, number of lesion sites, size of encephalomalacia, and seizure frequency are independent risk factors for the prognosis of patients with REAE. Surgical treatment is an independent prognostic factor for patients with REAE. Surgical treatment can significantly improve patient outcomes.

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“…Based on the properties of water diffusion among cellular membranes, DKI utilizes a non-Gaussian tensor model to calculate mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) that more accurately assesses the movement of these molecules among tissue [47]. MK, RK, and AK indices have extended the assessment of specific biomarkers within white matter for TLE [48,49], post-traumatic epilepsy [50], and idiopathic generalized epilepsies (IGEs) [51]. While DSI and DKI improve resolution of crossing fibers, another approach known as neurite orientation dispersion and density imaging (NODDI) [52] has recently emerged to resolve both crossing fibers and biological specificity [53,54].…”

Section: Diffusion Magnetic Resonance Imagingmentioning

confidence: 99%

Recent Advances in Neuroimaging of Epilepsy

Goodman

¹

,

Szaflarski

²

2021

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Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

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“…Te parameters obtained from the IVIM biexponential model are perfusion parameters D * and f and difusion parameter D. Te value of D * captures the microcirculatory perfusion-related components of the capillary network; the value of f explains the percentage of difusionrelated components of the microcirculatory perfusion in the total difusion; and the value of D mirrors the water molecules in the real tissue. Tere are many studies on IVIM imaging in glioma grading at home and abroad, and IVIM could be used as a non-invasive predictor of preoperative grading of glioma [14][15][16]. Based on these new techniques, we attempted to assess the occurrence and development of epilepsy using brain difusion imaging.…”

Section: Introductionmentioning

confidence: 99%

Brain Diffusion Weighted Imaging Study of Mongolian Idiopathic Epilepsy

Zhao

¹

,

Ma

²

,

Ban

³

et al. 2022

Journal of Healthcare Engineering

0

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Objective. To evaluate the effectiveness of diffusion-weighted imaging in the assessment of idiopathic epilepsy in Mongolian. Methods. One hundred Mongolian idiopathic epilepsy patients were enrolled as the observation group and 100 healthy Mongolian volunteers as the control group. All the subjects underwent routine MRI, diffusion kurtosis imaging (DKI), and intra-voxel incoherent motion (IVIM) examination on a 3.0 T scanner. Mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA), true water molecular diffusion coefficient (D), mean diffusion coefficient (MD), pseudo-diffusion coefficient ( D ∗ ), and perfusion fraction (f) of each region of interest in the brain were measured. Count data were expressed as rates, and the chi-square test was performed for comparison between groups. Measurement data were first assessed by a normality test, and the t test for independent samples was performed for comparison between groups if they met the normal distribution; for non-normal distribution, the Mann-Whitney U test was performed for comparison between groups. A ROC curve analysis was performed to test the effectiveness of each parameter. Results. MK values of the hippocampus, thalamus, and white matter of the temporal lobe in the observation group were significantly higher than those in the control group, while D and F values were significantly lower (all P < 0.05 ). ROC curve analysis showed that MK, D, and F values of the hippocampus, thalamus, and white matter of the temporal lobe had moderate to good diagnostic efficacy for idiopathic epilepsy (AUC = 0.617–0.749, all P < 0.001 ). Conclusion. DKI and IVIM can more accurately represent the abnormal changes of brain tissue in patients with epilepsy, and it may have important implications for the clinical diagnosis of Mongolian epileptic patients.

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Susceptibility‐weighted and diffusion kurtosis imaging to evaluate encephalomalacia with epilepsy after traumatic brain injury

Cited by 8 publications

References 30 publications

Screening of Risk Factors for Poor Prognosis in Patients with Refractory Epilepsy Secondary to Encephalomalacia

Screening of Risk Factors for Poor Prognosis in Patients with Refractory Epilepsy Secondary to Encephalomalacia

Recent Advances in Neuroimaging of Epilepsy

Brain Diffusion Weighted Imaging Study of Mongolian Idiopathic Epilepsy

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