Non-Cartesian MRI enables efficient coverage in k-space and is leveraged to accelerate acquisitions of images in multiple contrasts. However, denoising such data is non-trivial, since the noise statistics is neither independent nor normally distributed in reconstructed images. Here, we propose a random-matrix-theory-based denoising and noise-mapping pipeline applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the denoising pipeline on diffusion MRI data, including a numerical phantom and ex vivo mouse brain data in radial trajectories. The proposed pipeline robustly estimates the noise level, removes the noise, and corrects the bias in parametric maps of diffusion and kurtosis metrics.
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