Purpose Early identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In non-contrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyper-acute phase (<8 hours from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry-based analysis of texture features of ischemic lesions in non-contrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase. Methods One hundred thirty-nine (139) ncCT scans of hyperacute ischemic stroke with follow-up magnetic resonance diffusion-weighted (MR-DW) images were collected. The regions of stroke were identified in the MR-DW images, which were spatially aligned to corresponding ncCT images. A state-of-the-art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR-DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co-occurrence and run length texture-based image features in the regions of ischemic stroke and their contralateral regions. Results The classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions. Conclusions The results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in non-contrast CT. This work is an initial step towards development of an automated decision support system for detection of hyperacute ischemic stroke lesions on non-contrast CT of the brain.
Brain atrophy is a key imaging hallmark of Alzheimer disease (AD). In this study, we carried out an integrative evaluation of AD-related atrophy. Twelve patients with AD and 13 healthy controls were enrolled. We conducted a cross-sectional analysis of total brain tissue volumes with SIENAX. Localized gray matter atrophy was identified with optimized voxel-wise morphometry (FSL-VBM), and subcortical atrophy was evaluated by active shape model implemented in FMRIB's Integrated Registration Segmentation Toolkit. SIENAX analysis demonstrated total brain atrophy in AD patients; voxel-based morphometry analysis showed atrophy in the bilateral mediotemporal regions and in the posterior brain regions. In addition, regarding the diminished volumes of thalami and hippocampi in AD patients, subsequent vertex analysis of the segmented structures indicated shrinkage of the bilateral anterior thalami and the left medial hippocampus. Interestingly, the volume of the thalami and hippocampi were highly correlated with the volume of the thalami and amygdalae on both sides in AD patients, but not in healthy controls. This complex structural information proved useful in the detailed interpretation of AD-related neurodegenerative process, as the multilevel approach showed both global and local atrophy on cortical and subcortical levels. Most importantly, our results raise the possibility that subcortical structure atrophy is not independent in AD patients.
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