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A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI


Indexed by:会议论文

Date of Publication:2019-01-01

Included Journals:CPCI-S

Page Number:4368-4375

Abstract:Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indications. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by dee

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