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22 September 2015 Compression and denoising in magnetic resonance imaging via SVD on the Fourier domain using computer algebra
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Abstract
Magnetic resonance (MR) data reconstruction can be computationally a challenging task. The signal-to-noise ratio might also present complications, especially with high-resolution images. In this sense, data compression can be useful not only for reducing the complexity and memory requirements, but also to reduce noise, even to allow eliminate spurious components.This article proposes the use of a system based on singular value decomposition of low order for noise reconstruction and reduction in MR imaging system. The proposed method is evaluated using in vivo MRI data. Rebuilt images with less than 20 of the original data and with similar quality in terms of visual inspection are presented. Also a quantitative evaluation of the method is presented.
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Felipe Díaz "Compression and denoising in magnetic resonance imaging via SVD on the Fourier domain using computer algebra", Proc. SPIE 9599, Applications of Digital Image Processing XXXVIII, 95990P (22 September 2015); https://doi.org/10.1117/12.2189140
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