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Meeting Abstract

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

MPG-Autoren
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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;

Externe Ressourcen

http://dev.ismrm.org/2016/2071.html
(beliebiger Volltext)

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awesmoe_dwi.pdf
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Zitation

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).


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-002A-4B40-6
Zusammenfassung
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.