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Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery.

Saurav K Shastri¹, Rizwan Ahmad², Christopher A Metzler³

¹Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43201, USA.

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|March 27, 2023

Summary

This summary is machine-generated.

This study introduces a new plug-and-play (PnP) algorithm using generalized expectation-consistent (GEC) approximation for improved inverse problem solving. It enhances magnetic resonance image recovery by using a novel deep neural network (DNN) denoiser tailored to predictable error statistics.

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Area of Science:

Computational imaging
Optimization algorithms
Machine learning for signal processing

Background:

Plug-and-play (PnP) methods leverage deep neural network (DNN) denoisers for solving inverse problems.
Standard PnP denoisers often assume white Gaussian noise, which mismatches the actual input error in iterative algorithms.
Approximate message passing (AMP) methods achieve better denoiser performance but require random forward operators.

Purpose of the Study:

To develop an improved PnP algorithm for inverse problems, particularly for Fourier-based operators.
To address the mismatch between denoiser assumptions and actual input error in PnP methods.
To enhance magnetic resonance (MR) image recovery using a novel approach.

Main Methods:

Proposed a PnP algorithm based on generalized expectation-consistent (GEC) approximation, a variant of AMP.
Developed a new DNN denoiser specifically designed to utilize predictable error statistics from the GEC algorithm.
Applied the GEC-based PnP method to magnetic resonance (MR) image recovery tasks.

Main Results:

The GEC-based PnP algorithm provides predictable error statistics at each iteration.
The novel DNN denoiser effectively leverages these statistics for improved performance.
Demonstrated significant advantages of the proposed approach over existing PnP and AMP methods in MR image recovery.

Conclusions:

The proposed GEC-based PnP method offers a robust framework for inverse problems with Fourier-based operators.
This approach improves image recovery by aligning denoiser capabilities with algorithm-generated error characteristics.
The study highlights the potential of tailored denoisers within advanced iterative optimization algorithms for enhanced scientific imaging.