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.
Highlights
Ultra-high field magnetic resonance (MR) imaging at 7 Tesla and beyond has enabled neuroscientists to probe the human brain in vivo beyond the macroscopic scale (Weiskopf et al, 2015)
Recent work in diffusion analysis demonstrated that the intrinsic redundancy of the signal across these images can be employed to separate signal from noise with a principal component analysis (PCA) over small patches of the images (Manjón et al, 2013; Veraart et al, 2016)
This principle can be transferred to multi-parametric quantitative magnetic resonance imaging (MRI): in this work we present an extension of the PCA denoising to the recently described MP2RAGEME sequence, Denoising High-Field Multi-Dimensional MRI
Summary
Ultra-high field magnetic resonance (MR) imaging at 7 Tesla and beyond has enabled neuroscientists to probe the human brain in vivo beyond the macroscopic scale (Weiskopf et al, 2015). Multi-parametric quantitative methods acquire multiple images within a single sequence, in order to estimate the underlying quantity (Helms et al, 2008; Metere et al, 2017; Caan et al, 2018). Recent work in diffusion analysis demonstrated that the intrinsic redundancy of the signal across these images can be employed to separate signal from noise with a principal component analysis (PCA) over small patches of the images (Manjón et al, 2013; Veraart et al, 2016) This principle can be transferred to multi-parametric quantitative MRI: in this work we present an extension of the PCA denoising to the recently described MP2RAGEME sequence, Denoising High-Field Multi-Dimensional MRI which provides estimates of R1 and R2∗ relaxation rates as well as quantitative susceptibility maps (QSM) in a very compact imaging sequence (Caan et al, 2018)
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