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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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Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction.

Jun-Hyeok Lee¹, Junghwa Kang¹, Se-Hong Oh¹

¹Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning method, the multi-domain Neumann network with sensitivity maps (MDNNSM), to accelerate Magnetic Resonance Imaging (MRI) scans. MDNNSM reconstructs high-fidelity MR images from undersampled data, outperforming existing parallel MRI techniques.

Keywords:

Neumann network deep learning magnetic resonance imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Magnetic Resonance Imaging (MRI) provides high-quality medical images but suffers from long scan times, leading to patient discomfort and artifacts.
Parallel MRI (pMRI) accelerates scans by reconstructing images from undersampled k-space data using multi-coil arrays.
Existing pMRI reconstruction methods face challenges in maintaining image fidelity at higher acceleration factors.

Purpose of the Study:

To develop a novel deep learning-based method for reconstructing high-fidelity MR images from undersampled multi-coil k-space data.
To improve the accuracy and efficiency of parallel MRI reconstruction.
To address the limitations of long scan times in MRI.

Main Methods:

Proposed a multi-domain Neumann network with sensitivity maps (MDNNSM) for parallel MRI reconstruction.
MDNNSM incorporates a CNN-based block for estimating coil sensitivity maps and a recursive block for MR image reconstruction.
Utilized a forward model including coil sensitivity maps and skip connections for output accumulation.

Main Results:

The MDNNSM method demonstrated superior performance in reconstructing MR images compared to GRAPPA and the original Neumann network.
Experiments were conducted on the fastMRI T1-weighted brain dataset at acceleration factors of 2, 4, and 8.
Both qualitative and quantitative results confirmed the accuracy of the proposed MDNNSM method.

Conclusions:

The proposed MDNNSM deep learning approach effectively reconstructs high-fidelity MR images from undersampled data.
MDNNSM offers a promising solution for accelerating MRI scans while preserving image quality.
This method has the potential to significantly reduce patient scan times and improve diagnostic accuracy.