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Deep learning-guided weighted averaging for signal dropout compensation in diffusion-weighted imaging of the liver
Methods: Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed which aims to suppress the contribution of image regions affected by signal dropouts.
Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network
The algorithm is trained on DW liver data of 60 volunteers and evaluated on retrospectively and prospectively sub-sampled data of different anatomies and resolutions.
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