Difference between revisions of "Projects:DTINoiseStatistics"
| (3 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| − | Back to | + | Back to [[Algorithm:Utah2|Utah 2 Algorithms]] |
__NOTOC__ | __NOTOC__ | ||
= DTI Noise Statistics = | = DTI Noise Statistics = | ||
| Line 8: | Line 8: | ||
Utah: Casey Goodlett, Guido Gerig, Tom Fletcher, Ross Whitaker | Utah: Casey Goodlett, Guido Gerig, Tom Fletcher, Ross Whitaker | ||
| + | |||
| + | = Publications = | ||
| + | |||
| + | ''In Print'' | ||
| + | * [http://www.na-mic.org/publications/pages/display?search=DTINoiseStatistics&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Influence of Imaging Noise on DTI Statistics] | ||
| + | |||
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]] | ||
[[Category: Statistics]] [[Category:Diffusion MRI]] | [[Category: Statistics]] [[Category:Diffusion MRI]] | ||
Latest revision as of 20:30, 11 May 2010
Home < Projects:DTINoiseStatisticsBack to Utah 2 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.
Key Investigators
Utah: Casey Goodlett, Guido Gerig, Tom Fletcher, Ross Whitaker
Publications
In Print
Project Week Results: Jan 2006, Jun 2006, Jan 2007
