Difference between revisions of "Projects:DTINoiseStatistics"
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Project Week Results: [[Media:Riemannian_DTI_ProgWeek2006.ppt|Jan 2006]], [[Media:2006_Summer_Project_Week_DTI_Processing.ppt|Jun 2006]], [[Media:2007_Project_Half_Week_TensorEstimation.ppt|Jan 2007]] | Project Week Results: [[Media:Riemannian_DTI_ProgWeek2006.ppt|Jan 2006]], [[Media:2006_Summer_Project_Week_DTI_Processing.ppt|Jun 2006]], [[Media:2007_Project_Half_Week_TensorEstimation.ppt|Jan 2007]] | ||
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Revision as of 16:40, 2 April 2008
Home < Projects:DTINoiseStatisticsBack to NA-MIC Collaborations, UNC Algorithms
DTI Noise Statistics
Clinical time limitations on the acquisition of diffusion weighted volumes in DTI present several key challenges for quantitative statistics of diffusion tensors and tensor-derived measures. First, the signal to noise ratio (SNR) in each individual diffusion weighted volume is relatively low due to the need for quick acquisition. Secondly, the presence of Rician noise in MR imaging can introduce bias in the estimation of anisotropy and trace. Unlike structural MRI where intensities are primarily used to obtain contrast, the goal of DTI is to quantify the local diffusion properties in each voxel. Therefore, an understanding of the influence of imaging noise on the distribution of measured values is important to understand the results of statistical analysis and to design new imaging protocols.
Publications
Key Investigators
Utah: Casey Goodlett, Guido Gerig, Tom Fletcher, Ross Whitaker
Project Week Results: Jan 2006, Jun 2006, Jan 2007
