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Mitigating impacts of tissue-heterogeneity and noise bias on MP-PCA denoising for high-quality diffusion MRI

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Eichner,  Cornelius
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Paquette,  Michael
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Friederici,  Angela D.
Department Neuropsychology, 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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Citation

Eichner, C., Paquette, M., Friederici, A. D., & Anwander, A. (2020). Mitigating impacts of tissue-heterogeneity and noise bias on MP-PCA denoising for high-quality diffusion MRI. Poster presented at 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference.


Cite as: http://hdl.handle.net/21.11116/0000-0006-D4D1-E
Abstract
Advanced diffusion MRI (dMRI) data with high resolution and strong diffusion contrast typically suffer from low SNR levels. Therefore, denoising algorithms such as MP-PCA became an essential part of current dMRI processing pipelines. To overcome challenges related to violations of MP-PCAs assumption of tissue homogeneity in typical dMRI data, we here introduce an informed-MP-PCA (iMP-PCA) algorithm taking local differences in tissue composition into account. Denoising-performance of iMP-PCA was compared to conventional MP-PCA and evaluated on both magnitude and real-valued dMRI data. iMP-PCA was shown to significantly improve denoising-performance, especially at tissue boundaries and in regions of low SNR.