We introduce a new denoising framework for denoising magnitude diffusion MRI. The framework synergistically combines the variance stabilizing transform with optimal singular-value manipulation. The usefulness of the proposed framework is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve signal-to-noise ratios across the entire brain, leading to substantially enhanced performances for estimating diffusion-tensor-related indices and for resolving crossing fibers when compared to another competing method. As such, the proposed denoising method is expected to have great utility for high-quality, high-resolution whole-brain diffusion MRI, desirable for many neuroscience and clinical applications.
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