Deep learning requires a large number of parameter settings, which can be prone to reproducibility issues, undermining the reliability and validity of the outcomes. In this study, we list the common sources that may induce the reproducibility issues in deep learning-based MR reconstruction. The effect size of each source on the network performance was investigated. From the results of this study, we recommend to share a trained network.
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