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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.

Mojtaba Safari¹, Zach Eidex¹, Chih-Wei Chang¹

¹Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.

|May 15, 2024

Summary

This summary is machine-generated.

Deep learning combined with compressed sensing accelerates magnetic resonance imaging (MRI) acquisition times. This review explores deep learning-based compressed sensing MRI techniques for faster, high-quality medical imaging.

Keywords:

Compressed sensing (CS)MRI acceleration MRI reconstruction fast MRI magnetic resonance imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Signal Processing

Background:

Magnetic Resonance Imaging (MRI) offers detailed, non-invasive visualization but suffers from long scan times, leading to patient discomfort and motion artifacts.
Compressed Sensing (CS) is a technique to reduce MRI data acquisition by exploiting image sparsity.
Integrating Deep Learning (DL) with CS-MRI has emerged as a powerful framework for accelerating image acquisition.

Conclusions:

DL-based CS-MRI holds immense potential for advancing medical imaging by enabling faster scans.
Continued research in DL-CS-MRI is crucial for improving patient experience and expanding real-time imaging applications.
A curated GitHub repository with publications and datasets is provided to foster further research.