Optimization methods for MR image reconstruction (long version)

Abstract

The development of compressed sensing methods for magnetic resonance (MR) image reconstruction led to an explosion of research on models and optimization algorithms for MR imaging (MRI). Roughly 10 years after such methods first appeared in the MRI literature, the U.S. Food and Drug Administration (FDA) approved certain compressed sensing methods for commercial use, making compressed sensing a clinical success story for MRI. This review paper summarizes several key models and optimization algorithms for MR image reconstruction, including both the type of methods that have FDA approval for clinical use, as well as more recent methods being considered in the research community that use data-adaptive regularizers. Many algorithms have been devised that exploit the structure of the system model and regularizers used in MRI; this paper strives to collect such algorithms in a single survey. Many of the ideas used in optimization methods for MRI are also useful for solving other inverse problems.

Extended (and revised) version of invited paper submitted to IEEE SPMag special issue on "Computational MRI: Compressed Sensing and Beyond."

References in corpus (14)

• On instabilities of deep learning in image reconstruction - Does AI come at a cost?
• Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting
• Optimized first-order methods for smooth convex minimization
• Convolutional Dictionary Learning: Acceleration and Convergence
• Neural Proximal Gradient Descent for Compressive Imaging
• Learning-based Compressive Subsampling
• ADMM Penalty Parameter Selection by Residual Balancing
• Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
• Accelerating Non-Cartesian MRI Reconstruction Convergence using k-space Preconditioning
• Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
• Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
• Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
• Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
• Generic Feasibility of Perfect Reconstruction with Short FIR Filters in Multi-channel Systems

Cited by in corpus (3)

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• Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI