Abstract
Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.
Highlights
Magnetic Resonance imaging (MRI) has been intensively used to study the normal and pathological human brain
Simulated dataset A numerical phantom consisting of a set of diffusion-weighted images was generated using the Numerical Fiber Generator (NFG) software package [41]
The simulated images were generated with a diffusion-weighted response function based on the diffusion tensor model with a fractional anisotropy of Fractional Anisotropy (FA) = 0.8, apparent diffusion coefficient Apparent Diffusion Coefficient (ADC) = 0.9610–3 mm2/s, 7 b = 0 s/mm2 images and 60 Diffusion Weighted Imaging (DWI) along 60 uniformly distributed diffusiongradient directions (b = 3000 s/mm2), with volume field of view dimensions of 10061006100 voxels, with a voxel size of 26262 mm3
Summary
Magnetic Resonance imaging (MRI) has been intensively used to study the normal and pathological human brain. One of the most prominent applications of DWI is Diffusion Tensor Imaging (DTI), where the directionality and the magnitude of water diffusion is estimated using a tensor model yielding images of normal and abnormal white matter fiber structure and maps of brain connectivity through fiber tracking [1][2]. Diffusion weighted images have an inherently low signal to noise ratio (SNR) due to low signal amplitude and pronounced thermal noise, which is more evident than in conventional MRI due to extremely fast echo-planar acquisition strategies. Such low SNR makes DWI analysis complicated and biases the estimation of quantitative diffusion parameters [3]. This limited SNR makes automated processing of these images challenging and potentially misleading
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