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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. In Proceedings of the 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002A-4B40-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-EE19-8
Genre: Meeting Abstract

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 Creators:
Marschner, Henrik1, Author           
Eichner, Cornelius1, Author           
Anwander, Alfred2, Author           
Pampel, André1, Author           
Möller, Harald E.1, Author           
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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: Published online
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Title: Proceedings of the 24th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM)
Source Genre: Proceedings
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