To reduce inhomogeneous noise caused by parallel imaging, we developed a deep-learning-based noise reduction method that incorporates spatial distribution of noise. For noise distribution we used a g-factor map segmented into high and low g-factor regions. We reduced the noise by using a different optimized network in each region. Finally, a denoised image was generated by combining the two denoised regions. Denoised brain images demonstrated improved signal to noise ratio (SNR) and mean square error (MSE) between denoised and full sampling images throughout the brain regions. Our method was able to reduce the inhomogeneous noise proportional to the noise intensity.
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