Measurement of Fractional Anisotropy in Normal Cerebral White Matter and Brain Tumors with Diffusion Tensor Imaging

2002 ◽  
Vol 47 (2) ◽  
pp. 147 ◽  
Author(s):  
Seung Koo Lee ◽  
Dong Ik Kim ◽  
Si Yeon Kim ◽  
Yon Kwon Ihn ◽  
Sang Heum Kim
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Brain Tumors ◽  
Fractional Anisotropy ◽  
Diffusion Tensor ◽  
Cerebral White Matter

scholarly journals Diffusion Tensor Imaging Evaluation of Neural Network Development in Patients Undergoing Therapeutic Repetitive Transcranial Magnetic Stimulation following Stroke

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Naoki Yamada ◽  
Ryo Ueda ◽  
Wataru Kakuda ◽  
Ryo Momosaki ◽  
Takahiro Kondo ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Transcranial Magnetic Stimulation ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Motor Function ◽  
Magnetic Stimulation ◽  
Diffusion Tensor ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Cerebral White Matter ◽  
Imaging Evaluation

We aimed to investigate plastic changes in cerebral white matter structures using diffusion tensor imaging following a 15-day stroke rehabilitation program. We compared the detection of cerebral plasticity between generalized fractional anisotropy (GFA), a novel tool for investigating white matter structures, and fractional anisotropy (FA). Low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) of 2400 pulses applied to the nonlesional hemisphere and 240 min intensive occupation therapy (OT) daily over 15 days. Motor function was evaluated using the Fugl-Meyer assessment (FMA) and Wolf Motor Function Test (WMFT). Patients underwent diffusion tensor magnetic resonance imaging (MRI) on admission and discharge, from which bilateral FA and GFA values in Brodmann area (BA) 4 and BA6 were calculated. Motor function improved following treatment (p<0.001). Treatment increased GFA values for both the lesioned and nonlesioned BA4 (p<0.05, p<0.001, resp.). Changes in GFA value for BA4 of the lesioned hemisphere were significantly inversely correlated with changes in WMFT scores (R2=0.363, p<0.05). Our findings indicate that the GFA may have a potentially more useful ability than FA to detect changes in white matter structures in areas of fiber intersection for any such future investigations.

scholarly journals Quantitative diffusion tensor imaging analysis does not distinguish pediatric canines with mucopolysaccharidosis I from control canines

2017 ◽  
Vol 30 (5) ◽  
pp. 454-460
Author(s):  
Dana M Middleton ◽  
Jonathan Y Li ◽  
Steven D Chen ◽  
Leonard E White ◽  
Patricia I Dickson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Imaging System ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Magnetic Resonance Imaging System ◽  
Radial Diffusivity ◽  
Mucopolysaccharidosis I ◽  
Diffusivity Measurements

Purpose We compared fractional anisotropy and radial diffusivity measurements between pediatric canines affected with mucopolysaccharidosis I and pediatric control canines. We hypothesized that lower fractional anisotropy and higher radial diffusivity values, consistent with dysmyelination, would be present in the mucopolysaccharidosis I cohort. Methods Six canine brains, three affected with mucopolysaccharidosis I and three unaffected, were euthanized at 7 weeks and imaged using a 7T small-animal magnetic resonance imaging system. Average fractional anisotropy and radial diffusivity values were calculated for four white-matter regions based on 100 regions of interest per region per specimen. A 95% confidence interval was calculated for each mean value. Results No difference was seen in fractional anisotropy or radial diffusivity values between mucopolysaccharidosis affected and unaffected brains in any region. In particular, the 95% confidence intervals for mucopolysaccharidosis affected and unaffected canines frequently overlapped for both fractional anisotropy and radial diffusivity measurements. In addition, in some brain regions a large range of fractional anisotropy and radial diffusivity values were seen within the same cohort. Conclusion The fractional anisotropy and radial diffusivity values of white matter did not differ between pediatric mucopolysaccharidosis affected canines and pediatric control canines. Possible explanations include: (a) a lack of white matter tissue differences between mucopolysaccharidosis affected and unaffected brains at early disease stages; (b) diffusion tensor imaging does not detect any existing differences; (c) inflammatory processes such as astrogliosis produce changes that offset the decreased fractional anisotropy values and increased radial diffusivity values that are expected in dysmyelination; and (d) our sample size was insufficient to detect differences. Further studies correlating diffusion tensor imaging findings to histology are warranted.

Diffusion Tensor Imaging and Fiber Tractography

2009 ◽  
pp. 229-246
Author(s):  
Evanthia E. Tripoliti ◽  
Dimitrios I. Fotiadis ◽  
Konstantia Veliou
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Cerebral White Matter ◽  
Tissue Microstructure ◽  
Diffusion Weighted Images ◽  
Fiber Tracts ◽  
Diffusion Weighted ◽  
Magnetic Resonance Imaging Mri

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain structures and neural connectivity. DTI measures are thought to be representative of brain tissue microstructure and are particularly useful for examining organized brain regions, such as white matter tract areas. DTI measures the water diffusion tensor using diffusion weighted pulse sequences which are sensitive to microscopic random water motion. The resulting diffusion weighted images (DWI) display and allow quantification of how water diffuses along axes or diffusion encoding directions. This can help to measure and quantify the tissue’s orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. In this chapter the authors discuss the theoretical aspects of DTI, the information that can be extracted from DTI data, and the use of the extracted information for the reconstruction of fiber tracts and the diagnosis of a disease. In addition, a review of known fiber tracking algorithms is presented.

Diffusion Tensor Imaging of Cerebral White Matter*

2008 ◽  
Vol 18 (3) ◽  
pp. 155-162 ◽  
Author(s):  
Peter Stoeter ◽  
Paulo Roberto Dellani ◽  
Goran Vucurevic
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Cerebral White Matter
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