Denoising High-Field Multi-Dimensional MRI With Local Complex PCA

Pierre-Louis Bazin; Anneke Alkemade; Wietske van der Zwaag; Matthan Caan; Martijn Mulder; Birte U Forstmann

doi:10.3389/fnins.2019.01066

Denoising High-Field Multi-Dimensional MRI With Local Complex PCA

Pierre-Louis Bazin, Anneke Alkemade, Wietske van der Zwaag, Matthan Caan, Martijn Mulder, Birte U Forstmann

Netherlands Institute for Neuroscience (NIN)

Research output: Contribution to journal/periodical › Article › Scientific › peer-review

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Abstract

Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.

Original language	English
Pages (from-to)	1066
Journal	Frontiers in Neuroscience
Volume	13
DOIs	https://doi.org/10.3389/fnins.2019.01066
Publication status	Published - 2019

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title = "Denoising High-Field Multi-Dimensional MRI With Local Complex PCA",

abstract = "Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.",

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AU - Bazin, Pierre-Louis

AU - Alkemade, Anneke

AU - van der Zwaag, Wietske

AU - Caan, Matthan

AU - Mulder, Martijn

AU - Forstmann, Birte U

PY - 2019

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AB - Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.

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JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

SN - 1662-4548

ER -

Denoising High-Field Multi-Dimensional MRI With Local Complex PCA

Abstract

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