3D texture analysis of hippocampus based on MR images in patients with alzheimer disease and mild cognitive impairment

2010 ◽  
Author(s):  
Xin Li ◽  
Hong Xia ◽  
Zhen Zhou ◽  
Longzheng Tong
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Alzheimer Disease ◽  
Texture Analysis ◽  
Mr Images ◽  
3D Texture ◽  
3D Texture Analysis

3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

2014 ◽  
Vol 533 ◽  
pp. 415-420 ◽  
Author(s):  
Wei Fang Liu ◽  
Xu Wang ◽  
Hong Xia
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Corpus Callosum ◽  
Texture Analysis ◽  
Three Dimensional ◽  
Texture Features ◽  
Grey Level ◽  
Run Length ◽  
3D Texture ◽  
3D Texture Analysis

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p<0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p>0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

scholarly journals 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution

2017 ◽  
Vol 31 (2) ◽  
pp. 285-294 ◽  
Author(s):  
Monika Béresová ◽  
Andrés Larroza ◽  
Estanislao Arana ◽  
József Varga ◽  
László Balkay ◽  
...  
Keyword(s):  
Brain Metastases ◽  
Texture Analysis ◽  
Mr Images ◽  
3D Texture ◽  
3D Texture Analysis ◽  
2D And 3D

scholarly journals Psychometric Properties of the Persian Montreal Cognitive Assessment in Mild Cognitive Impairment and Alzheimer Disease

2021 ◽  
pp. 51-57
Author(s):  
Vahid Rashedi ◽  
Mahshid Foroughan ◽  
Negin Chehrehnegar
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Alzheimer Disease ◽  
Psychometric Properties ◽  
Cognitive Assessment ◽  
Screening Test ◽  
Cognitive Status ◽  
Montreal Cognitive Assessment ◽  
Cutoff Score ◽  
Persian Version

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.

scholarly journals Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Pyo Kim ◽  
Jonghoon Kim ◽  
Hyemin Jang ◽  
Jaeho Kim ◽  
Sung Hoon Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apoe Genotype ◽  
Texture Features ◽  
Structural Mri ◽  
Regularized Regression ◽  
Mr Images ◽  
Baseline Model ◽  
Imaging Model ◽  
T1 And T2

AbstractPredicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71–0.74, AUC for validation = 0.68–0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.

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