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 # Partial Fourier MRI Reconstruction using Deep Complex-valued Convolutional Neural Networks
+In this study, we aim to develop a deep complex-valued CNN with an unrolled network structure for general PF reconstruction by iteratively reconstructing PF sampled data and enforcing data consistency. Our proposed method can achieve a better recovery on magnitude and phase images without noise amplification compared to conventional PF reconstruction methods. Further, this approach can be extended to 2D PF reconstruction and joint multi-slice PF reconstruction with complementary sampling across adjacent slices. We train and demonstrate our approach in spin-echo and gradient-echo data, including SWI.
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 Publicly available datasets, including T1w brain data from Calgary-Campinas Public Brain MR Database<sup>1</sup>, T2w brain data from fastMRI database<sup>2</sup> , and SWI data from OpenNeuro database<sup>3</sup>, were used for training and evaluating our proposed approach.