Compressed Sensing with Self-Validation

Compressed sensing offers a capacity for accelerating data acquisition while keeping aliasing and noise effects subdued. Theories and experiences however are yet to establish a more robust guidance on random sampling, sparse model and non-linear solver, to help manage the challenge of using the technology in diagnostic MR. In this work we took a new angle and investigated the feasibility of introducing self-validation into compressed sensing MR. The goal is to assist fidelity assessment and improvement in practice with validation tests that can be automatically performed on any specific imaging instance itself, without requiring additional data or comparison references.

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