Partial Fourier (PF) acquisition schemes are a common way to increase the inherently low signal-to-noise ratio in diffusion-weighted (DW) images. The naïve solution of zero-filling k-space results in visible blurring and Gibbs ringing. Based on the circumstance that traditional methods such as homodyne reconstruction or POCS often fail to remove blurring and ringing without introducing new artifacts, this work aims to use a Convolutional Neural Network for robust PF reconstruction in prostate DWI. We show that our data-driven approach, which efficiently uses correlations across different b-values, outperforms traditional methods in terms of quantitative measures and visual impression of the images.
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