A novel approach to estimate noise and Rician signal bias in diffusion MRI magnitude data is proposed. Rather than relying on repeat measurements for estimation of noise and expected signal, the methods uses multi-b measurements and non-monoexponential signal fits. In addition to noise and bias estimation this approach also provides signal averaging over all b-factors and permits determination of non-monoexponential tissue water diffusion signal decay. Testing was performed with Monte-Carlo simulations and on diffusion-weighted high-b prostate image data obtained with an external coil array.
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