Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity

Nicholas Dwork; Jeremy W. Gordon; Erin K. Englund

doi:10.1117/1.JMI.11.3.033504

26 June 2024 Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity

Nicholas Dwork, Jeremy W. Gordon, Erin K. Englund

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 3, 033504 (June 2024). https://doi.org/10.1117/1.JMI.11.3.033504

Abstract

Purpose

We present a method that combines compressed sensing with parallel imaging that takes advantage of the structure of the sparsifying transformation.

Approach

Previous work has combined compressed sensing with parallel imaging using model-based reconstruction but without taking advantage of the structured sparsity. Blurry images for each coil are reconstructed from the fully sampled center region. The optimization problem of compressed sensing is modified to take these blurry images into account, and it is solved to estimate the missing details.

Results

Using data of brain, ankle, and shoulder anatomies, the combination of compressed sensing with structured sparsity and parallel imaging reconstructs an image with a lower relative error than does sparse SENSE or L1 ESPIRiT, which do not use structured sparsity.

Conclusions

Taking advantage of structured sparsity improves the image quality for a given amount of data as long as a fully sampled region centered on the zero frequency of the appropriate size is acquired.

Citation Download Citation

Nicholas Dwork, Jeremy W. Gordon, and Erin K. Englund "Accelerated parallel magnetic resonance imaging with compressed sensing using structured sparsity," Journal of Medical Imaging 11(3), 033504 (26 June 2024). https://doi.org/10.1117/1.JMI.11.3.033504

Received: 21 November 2023; Accepted: 3 June 2024; Published: 26 June 2024

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KEYWORDS

Compressed sensing

Magnetic resonance imaging

Image restoration

Medical image reconstruction

Brain

Matrices

Model based design

Purpose

Approach

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Conclusions

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