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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to

Shanshan Wang¹, Ruoyou Wu¹, Sen Jia¹

¹Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Magnetic Resonance in Medicine

|April 16, 2024

Summary

This summary is machine-generated.

Deep learning (DL) accelerates Magnetic Resonance Imaging (MRI) by using neural networks for image reconstruction. This review explores challenges and solutions for integrating DL with MRI physics, advancing reliable imaging systems.

Keywords:

MR reconstruction deep learning fast MR imaging

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Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Magnetic Resonance Imaging (MRI)

Background:

Deep learning (DL) is a powerful tool for accelerating MRI acquisition and reconstruction.
MRI reconstruction presents unique challenges due to physics-based processes and data properties, differing from natural image restoration.
Integrating domain knowledge with data-driven DL approaches is crucial for accurate MRI.

Purpose of the Study:

To review significant challenges in knowledge-driven DL for fast MRI.
To present notable solutions and trends in DL-based MRI reconstruction.
To discuss MR vendor adoption, open questions, and future directions in DL for reliable MRI systems.

Main Methods:

Review of existing literature on DL applications in MRI.
Analysis of knowledge-driven DL approaches, including neural network architectures.
Examination of learning paradigms: supervised, semi-supervised, and unsupervised learning in MRI reconstruction.

Main Results:

Identified key challenges in applying DL to MRI's physics-based nature.
Highlighted successful integration of domain knowledge with DL models.
Documented the evolution of learning strategies from supervised to unsupervised methods.

Conclusions:

Knowledge-driven DL offers significant potential for accelerating MRI.
Addressing specific MRI challenges requires tailored DL solutions and learning strategies.
Future research should focus on open questions for robust and reliable DL-based MRI systems.