scholarly journals Cerebral White Matter Hyperintensities in T2 Weighted Magnetic Resonance Images of Elderly Patients- Correlation with their Mental Status

2021 ◽  
Vol 4 (1) ◽  
pp. 26-31
Author(s):  
Niraj Regmi ◽  
Abu Saleh Mohiuddin ◽  
Abu Taher ◽  
Mahfuz Ara Ferdousi
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Elderly Patients ◽  
Mental Status ◽  
Negative Correlation ◽  
White Matter Hyperintensities ◽  
Mmse Score ◽  
Significant Negative Correlation ◽  
Magnetic Resonance Images ◽  
Elderly Subjects

Background: White matter hyperintensities (WMH), focal and/or diffuse areas of hyperintense signals on T2 weighted magnetic resonance imaging (MRI), are the most common incidental finding in elderly patients. However, their clinical significance is usually overlooked. We aimed to find out the correlation between the degree of cerebral WMH in MRI with the mental status of elderly patients, assessed by Mini-Mental Status Examination (MMSE) score. Methods: This cross-sectional study was conducted for two years on eighty eligible elderly patients (> 60 years) referred to the Department of Radiology and Imaging for MRI of the brain. Demographic variables like age and sex, MMSE score, and MRI variables like location and number of WMHs were studied. The Pearson’s correlation coefficient was used to calculate the correlation between the extent of periventricular WMHs and MMSE score. Results: A significant negative correlation (r = -0.78; p < 0.001) was found between decreased MMSE and the extent of periventricular WMH. A significant negative correlation was also found when periventricular hyperintensities were evaluated individually for frontal caps (r = -0.68; p < 0.0001), band opacities (r = -0.55; p<0.0001) and occipital cap (r = -0.59; p < 0.0001). However, subcortical WMH was not significantly corelated with MMSE score (r = +0.018, p = 0.0897). Conclusion: A significant negative correlation exists between the extent of periventricular WMH seen at brain MRI with cognitive decline in elderly subjects. However, no such correlation exists between subcortical WMH and mental status.

scholarly journals Limited One-time Sampling Irregularity Map (LOTS-IM): Automatic Unsupervised Quantitative Assessment of White Matter Hyperintensities in Structural Brain Magnetic Resonance Images

2018 ◽  
Author(s):  
Muhammad Febrian Rachmadi ◽  
Maria del C. Valdés-Hernández ◽  
Hongwei Li ◽  
Ricardo Guerrero ◽  
Rozanna Meijboom ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
White Matter ◽  
Magnetic Resonance ◽  
Quantitative Assessment ◽  
Deep Neural Networks ◽  
White Matter Hyperintensities ◽  
Magnetic Resonance Images ◽  
Supervised Machine Learning ◽  
Time Sampling

AbstractWe present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRI’s texture information. We tested and compared the usage of IM for WMH segmentation on T2-FLAIR MRI with various methods, including the well established unsupervised WMH segmentation Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), conventional supervised machine learning schemes andstate-of-the-artsupervised deep neural networks. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep neural networks algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). The high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

scholarly journals A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

2017 ◽  
Vol 9 (11) ◽  
pp. 1174 ◽  
Author(s):  
Hsian-Min Chen ◽  
Hsin Wang ◽  
Jyh-Wen Chai ◽  
Chi-Chang Chen ◽  
Bai Xue ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Hyperspectral Imaging ◽  
White Matter Hyperintensities ◽  
Magnetic Resonance Images ◽  
Imaging Approach

[P4-172]: WHITE MATTER HYPERINTENSITIES AND COGNITIVE FUNCTIONS IN ELDERLY PATIENTS WITH DEPRESSION

2017 ◽  
Vol 13 (7S_Part_27) ◽  
pp. P1328-P1329
Author(s):  
Jeong Lan Kim ◽  
Gahye Noh ◽  
Hyunkyung Kim ◽  
Miji Lee
Keyword(s):  
White Matter ◽  
Elderly Patients ◽  
Cognitive Functions ◽  
White Matter Hyperintensities

scholarly journals Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review

2015 ◽  
Vol 13 (3) ◽  
pp. 261-276 ◽  
Author(s):  
Maria Eugenia Caligiuri ◽  
Paolo Perrotta ◽  
Antonio Augimeri ◽  
Federico Rocca ◽  
Aldo Quattrone ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
Healthy Aging ◽  
White Matter Hyperintensities ◽  
Automatic Detection ◽  
Resonance Imaging
Sign in / Sign up

Export Citation Format

Share Document