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Noise reduction of diffusion tensor images by sparse representation and dictionary learning

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Academic year: 2020

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Figure

Fig. 1 Consecutive slices of a diffusion weighted image volume. a–d are consecutive slices derived from a three dimensional diffusion weighted image
Fig. 2 Initial and learned dictionary for simulated datasets. a is the initial dictinoary and b is the learned dictionary
Fig. 4 Quantitative comparison of different denoising methods. a–d are the fractional anisotropy errors of noisy data and denoising results using MNLM, SR and our proposed method
Fig. 5 Quantitative comparison of different denoising methods with different noise levels
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