An approximate message passing algorithm for rapid parameter-free compressed sensing MRI
| Autor: | Charles Millard, Boris Mailhe, Jared Tanner, Aaron T. Hess |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer science Computer Science - Information Theory Information Theory (cs.IT) Message passing Approximation algorithm 020206 networking & telecommunications Numerical Analysis (math.NA) G.1.3 02 engineering and technology Iterative reconstruction Image (mathematics) Matrix (mathematics) Compressed sensing FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Mathematics - Numerical Analysis Electrical Engineering and Systems Science - Signal Processing Algorithm Fourier series Free parameter |
| Zdroj: | ICIP |
| Popis: | For certain sensing matrices, the Approximate Message Passing (AMP) algorithm efficiently reconstructs undersampled signals. However, in Magnetic Resonance Imaging (MRI), where Fourier coefficients of a natural image are sampled with variable density, AMP encounters convergence problems. In response we present an algorithm based on Orthogonal AMP constructed specifically for variable density partial Fourier sensing matrices. For the first time in this setting a state evolution has been observed. A practical advantage of state evolution is that Stein's Unbiased Risk Estimate (SURE) can be effectively implemented, yielding an algorithm with no free parameters. We empirically evaluate the effectiveness of the parameter-free algorithm on simulated data and find that it converges over 5x faster and to a lower mean-squared error solution than Fast Iterative Shrinkage-Thresholding (FISTA). Comment: 5 pages, 5 figures, IEEE International Conference on Image Processing (ICIP) 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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