2019/03/08 by Jeffrey A. Fessler, Jeffrey A Fessler, Fessler, Jeffrey A · 1 citation
Engineering · Mathematics · Medicine · #Advanced MRI Techniques and Applications #FOS: Electrical engineering #FOS: Mathematics #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #eess.IV #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.1903.03510
Extended (and revised) version of invited paper submitted to IEEE SPMag special issue on "Computational MRI: Compressed Sensing and Beyond."
openalex publication_date 2019/03/08 · arxiv created 2019/06/13 · arxiv updated 2019/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.