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1.  Wallerian Degeneration in Central Nervous System: Dynamic Associations between Diffusion Indices and Their Underlying Pathology 
PLoS ONE  2012;7(7):e41441.
Background
Although diffusion tensor imaging has been used to monitor Wallerian degeneration, the exact relationship between the evolution of diffusion indices and its underlying pathology, especially in central nervous system, remains largely unknown. Here we aimed to address this question using a cat Wallerian degeneration model of corticospinal tract.
Methodology/Principal Findings
Twenty-five domestic mature Felis catus were included in the present study. The evolution of diffusion indices, including mean diffusivity (MD), fractional anisotropy (FA), primary (λ1) and transverse eigenvalues (λ23) of the degenerated corticospinal tract, were observed at baseline (before modeling) and at 2, 4, 6, 8, 10, 15, 20, 25, 30, 45 and 60 days after modeling in 4 cats. Pathological examinations were performed at eight time points mentioned above. Wallerian degeneration can be detected as early as the 2nd day after modeling by both diffusion tensor imaging and pathology. According to the evolution of diffusion indices, Wallerian degeneration can be classified into 2 stages. During the early stage (within 8 days after modeling), progressive disintegration of axons and myelin sheaths underlies the decreases in FA and λ1 and the increase in λ23. However, during the late stage (after 8 days), the gradual increases in FA, MD and λ1 and the unchanged λ23 seem to be a comprehensive reflection of the pathological processes including microglia activation, myelin clearance, and astrocytosis.
Conclusions/Significance
Our findings help the understanding of the altered diffusion indices in the context of pathology and suggest that diffusion tensor imaging has the potential to monitor the processes of Wallerian degeneration in the central nervous system in vivo after acute damage.
doi:10.1371/journal.pone.0041441
PMCID: PMC3400645  PMID: 22829950
2.  An automated and simple method for brain MR image extraction 
Background
The extraction of brain tissue from magnetic resonance head images, is an important image processing step for the analyses of neuroimage data. The authors have developed an automated and simple brain extraction method using an improved geometric active contour model.
Methods
The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.
Results
The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.
Conclusions
The proposed method can provide automated and accurate brain extraction result with high efficiency.
doi:10.1186/1475-925X-10-81
PMCID: PMC3180437  PMID: 21910906

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