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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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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

A robust MRI water-fat separation algorithm based on field map and fat fraction map smoothness.

Kecheng Yuan¹, Qingyun Liu¹, Xuhe Huangfu¹

¹Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China.

Medical Physics

|October 31, 2025

Summary

The novel Field Map and Fat Fraction Map Smoothing (FMFMS) algorithm enhances magnetic resonance water-fat separation. This method improves accuracy in challenging low signal-to-noise ratio scenarios and across various field strengths.

Keywords:

fat fraction map field map smooth property water‐fat separation

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Area of Science:

Medical Imaging
Biophysics
Image Processing

Background:

Magnetic resonance (MR) water-fat separation is crucial for tissue characterization and fat quantification.
Robust separation is challenging due to background noise and magnetic field inhomogeneities.

Purpose of the Study:

To enhance robustness in challenging MR imaging, including low field strengths and large fields of view (FOV).
To leverage the inherent spatial smoothness of field maps and fat fraction maps for improved accuracy.

Main Methods:

Proposed the Field Map and Fat Fraction Map Smoothing (FMFMS) algorithm, incorporating multi-peak fat modeling, T2* decay correction, and field inhomogeneity compensation.
Utilized a two-stage optimization framework for nonlinear parameter estimation and least-squares for water/fat amplitudes.
Integrated spatial smoothness of field and fat fraction maps, identifying erroneous voxels via local field discontinuities and Local Polynomial Surface Fitting (LPSF).

Main Results:

FMFMS outperformed existing methods (GOOSE, FFM) on ISMRM 2012 Challenge, phantom, and in vivo datasets.
Achieved 99.5% average accuracy on ISMRM Challenge cases (excluding #3) with minimal artifacts.
Demonstrated strong performance in low-field (0.35T) and large-FOV (1.5T) acquisitions across various anatomical regions.

Conclusions:

The FMFMS method offers superior performance in low signal-to-noise ratio (SNR) scenarios.
Provides reliable water-fat separation across diverse field strengths and anatomical locations.