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

Research output: Contribution to journal/periodicalArticleScientificpeer-review

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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 languageEnglish
Pages (from-to)1066
JournalFrontiers in Neuroscience
Publication statusPublished - 2019


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