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  De-noising of diffusion-weighted MRI data by averaging of inconsistent input data in wavelet space

Marschner, H., Eichner, C., Anwander, A., Pampel, A., & Möller, H. E. (2016). De-noising of diffusion-weighted MRI data by averaging of inconsistent input data in wavelet space. Poster presented at 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Singapur, Singapur.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-4B56-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-D224-B
Genre: Poster

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awesmoe_dwi.pdf (Abstract), 373KB
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 Creators:
Marschner, Henrik1, Author              
Eichner, Cornelius1, Author              
Anwander, Alfred2, Author              
Pampel, André1, Author              
Möller, Harald E.1, Author              
Affiliations:
1Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
2Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              

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 Abstract: Diffusion Weighted Images datasets with high spatial resolution and strong diffusion weighting are often deteriorated with low SNR. Here, we demonstrate the feasibility of a recently presented repetition-free averaging based de-noising (AWESOME). That technique reduces noise by averaging over a series of N images with varying contrast in wavelet space and regains intensities and object features initially covered by noise. We show that high resolution DWIs are achievable in a quality that almost equals to that obtained from 6fold complex averaging.

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Language(s): eng - English
 Dates: 2016-05-07
 Publication Status: Not specified
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Title: 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine
Place of Event: Singapur, Singapur
Start-/End Date: 2016-05-07 - 2016-05-13

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