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Fig. 1 | Journal of Cardiovascular Magnetic Resonance

Fig. 1

From: A dual-stage partially interpretable neural network for joint suppression of bSSFP banding and flow artifacts in non-phase-cycled cine imaging

Fig. 1

Schematic of the dual-stage network and generation of the training labels. A The dual-stage network consists of VI and AS sub-networks. Both sub-networks use a 3-dimensional U-Net as the backbone. The output from the VI sub-network provides a soft classification of banding and flow artifacts, which is used to guide artifact suppression by the AS sub-network and improve interpretability of the network. B Twelve cine movies each with a different center frequency offset were obtained. Average of the movies with 5 centric frequency offsets (red boxes) generated the label for training of the AS sub-network. The corresponding method was called “short-range phase cycling (SPC)”. The label for the VI sub-network was obtained through the equation \(sigmoid(SPC\_label/original\_cine-1)\) based on the SPC-combined image and the original cine. Since banding artifacts are dark and flow artifacts in concern of this paper are bright, this equation generates an intrinsic label for the VI sub-network

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