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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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Updated: Nov 12, 2025

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip

Yohan Jun¹, Hyungseob Shin¹, Taejoon Eo¹

¹School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.

Medical Image Analysis

|March 15, 2021

Summary

This summary is machine-generated.

DOPAMINE, a novel deep learning network, significantly accelerates quantitative magnetic resonance (MR) parameter mapping. This method reconstructs MR parameter maps from undersampled data, reducing scan times for clinical applications.

Keywords:

Deep learning Magnetic resonance imaging Parameter mapping Variable flip angle

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Last Updated: Nov 12, 2025

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Biomedical Engineering

Background:

Quantitative magnetic resonance (MR) parameter mapping provides crucial diagnostic information.
Current methods require long scan times, hindering clinical adoption.
Accelerating quantitative MR parameter mapping is essential for real-world applications.

Purpose of the Study:

To develop a fast and accurate method for MR parameter mapping.
To introduce DOPAMINE, a deep model-based network for reconstructing MR parameter maps from undersampled k-space data.
To reduce the acquisition time for quantitative MR parameter mapping.

Main Methods:

Proposed DOPAMINE, a hybrid deep learning and model-based network.
DOPAMINE utilizes a CNN for initial parameter map estimation (CNN-based mapping).
A reconstruction network with a deep CNN and data consistency layer refines parameter maps, removing aliasing artifacts.

Main Results:

DOPAMINE demonstrated superior performance in T1 map reconstruction compared to conventional and deep learning methods.
Outperformed methods across various reduction factors (R=3, 5, 7) and sampling patterns (1D Cartesian, 2D Poisson-disk).
Achieved quantitatively and qualitatively superior MR parameter map reconstruction from undersampled data.

Conclusions:

DOPAMINE effectively reduces scan time for quantitative MR parameter mapping using a variable flip angle (VFA) model.
The proposed method enables faster acquisition of valuable diagnostic information.
DOPAMINE shows significant potential for clinical translation of quantitative MR imaging.