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

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Marschner,  Henrik
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Eichner,  Cornelius
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Pampel,  André
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Möller,  Harald E.
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-4B56-5
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.