Brain White Matter Hyperintensity Lesion Characterization in T2 Fluid-Attenuated Inversion Recovery Magnetic Resonance Images: Shape, Texture, and Potential Growth

Abstract

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T 2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T 2 FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape ( P = 1.06 × 10 −2 ) or the texture ( P < 1 × 10 −20 ) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults.

Elucidating of oxidative distress in COVID-19 and methods of its prevention

“…Because brain is the top receiver of blood per cardiac output, and therefore especially prone to redox imbalance. The already detected MRI changes in COVID-19 patients include FLAIR intensity disturbances, typically found in brain tumors, neurodegenerative disorders, and neuroinflammation [[ 46 , 47 ]]. The most common were cortical FLAIR signal abnormalities, as well as white matter hyperintense lesions on FLAIR and diffusion with variable enhancement, associated with hemorrhagic lesions, and extensive and isolated white matter microhemorrhages [[ 48 , 49 ]].…”

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Structural retinal changes in cerebral small vessel disease

“…Inaccuracies may have occurred due to the use of 2D instead of 3D data sets for the LST-LPA 46 . Nevertheless, the LST-LPA has already been demonstrated to be valid and reliable in 2D data sets 47 .…”

Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy

“…This shows that the clustering method can effectively scale the original image from 16 to 3 bits, while still retaining the global content. In addition, the k -means clustering algorithm has been successfully and widely used in various medical image segmentation applications such as WML segmentation 64 , 65 , brain lesion segmentation 66 , brain segmentation 67 , corpus callosum segmentation 68 , and brain tumour segmentation 69 , 70 . In our implementation, k -means was used to accurately cluster the intensity data of WML texture structure, so that the feature data in the next feature extraction process was more accurate in the GLCM calculation (see Fig.…”