The recently proposed Plug-and-Play (PnP) methods provide an avenue to combine physics-driven MR models with sophisticated, learned image models instantiated by image denoising subroutines. The performance of PnP methods, however, is sensitive to changes in the measurement signal-to-noise ratio (SNR) and algorithmic parameters that balance the contributions from the data fidelity and denoising terms. We propose a discrepancy-principle-based scheme that mediates the impact of the denoising subroutine, leading to more consistent performance across different measurement SNRs without manual intervention. For validation, the proposed scheme is applied to cine images collected at 3T, 1.5T, and 0.35T scanners.
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