Voxel-by-voxel fitting of intravoxel incoherent motion (IVIM) MRI data using a bi-exponential model is challenging especially with low signal-to-noise ratio (SNR) diffusion-MR images. We propose to combine and use a supervised Deep Neural Network (DNN) approach to increase SNR of the acquired images and thus improve extraction of reliable parameter estimation using a segmented least square fitting algorithm. The effectiveness of the proposed method was demonstrated in both simulated and acquired in vivo data. The proposed approach is promising and can increase performance of the fitting algorithms especially for the case of images with high background noise.
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