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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: Jan 18, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Rapid spatio-temporal MR fingerprinting using physics-informed implicit neural representation.

Chaoguang Gong¹, Lixian Zou², Peng Li¹

¹The School of Electronics and Information Engineering, Harbin Institute of Technology,Harbin, Heilongjiang, China.

Medical Image Analysis

|January 16, 2026

Summary

This summary is machine-generated.

Magnetic Resonance Fingerprinting (MRF) can now overcome aliasing artifacts using a novel Physics-informed implicit neural MRF (πMRF) framework. This method enhances quantitative MRI accuracy and robustness, even with accelerated data acquisition.

Keywords:

Implicit neural representation Magnetic resonance fingerprinting Non-cartesian Parallel imaging Physics-informed neural network

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Area of Science:

Magnetic Resonance Imaging (MRI)
Quantitative Imaging
Medical Physics

Background:

Magnetic Resonance Fingerprinting (MRF) enables rapid, simultaneous multi-parametric quantitative MRI.
Aggressive undersampling in MRF causes aliasing artifacts, limiting its potential.
Conventional methods often remove artifacts, sacrificing speed or requiring large datasets.

Purpose of the Study:

To introduce a novel Physics-informed implicit neural MRF (πMRF) framework.
To extend MRF's encoding capacity to the global spatio-temporal domain by leveraging structured aliasing.
To enable unsupervised, joint estimation of quantitative tissue parameters and coil sensitivity maps (CSMs) with enhanced accuracy and robustness.

Main Methods:

Developed a πMRF framework integrating physics-informed spatio-temporal fingerprint modeling with implicit neural representations (INRs).
Utilized physics-informed neural networks (PINNs) for accurate high-dimensional signal modeling and efficient optimization.
Implemented subspace-guided sensitivity regularization for robust CSM estimation in undersampled scenarios.

Main Results:

πMRF demonstrated improved quantitative accuracy and robustness under highly accelerated acquisitions.
The framework achieved superior performance compared to state-of-the-art MRF methods.
Successful validation on simulated, phantom, and in vivo datasets.

Conclusions:

The proposed πMRF framework effectively addresses aliasing artifacts in quantitative MRI.
πMRF offers a promising approach for accelerated and robust quantitative imaging.
This method enhances the potential of MRF for clinical applications.