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Deep unfolding networks (DUNs) are the foremost methods in the realm ofcompressed sensing MRI, as they can employ learnable networks to facilitateinterpretable forward-inference operators. However, several daunting issuesstill exist, including the heavy dependency on the first-order optimizationalgorithms, the insufficient information fusion mechanisms, and the limitationof capturing long-range relationships. To address the issues, we propose aGenerically Accelerated Half-Quadratic Splitting (GA-HQS) algorithm thatincorporates second-order gradient information and pyramid attention modulesfor the delicate fusion of inputs at the pixel level. Moreover, a multi-scalesplit transformer is also designed to enhance the global featurerepresentation. Comprehensive experiments demonstrate that our method surpassesprevious ones on single-coil MRI acceleration tasks.

GA-HQS: MRI reconstruction via a generically accelerated unfolding approach