Diffusion MRI intrinsically suffers from low signal-to-noise ratio (SNR), especially when spatial resolution or b-value is high. A typical diffusion MRI scanning session produces image sets with same geometries but different diffusion directions and b-values, thus these diffusion-weighted (DW) images often share strong structural similarities. In this study, we developed a joint denoising method for DW images based on low-rank matrix approximation. This denoising method exploits structural similarities of DW image set. Both simulation and in vivo brain experiments demonstrate significant noise reduction in all DW images, revealing more microstructural details in quantitative diffusion maps.
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