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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: Sep 17, 2025

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

Published on: September 7, 2018

Fat-water MRI separation using deep complex convolution network.

Moorthy Ganeshkumar¹, Devasenathipathy Kandasamy², Raju Sharma²

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.

Magma (New York, N.Y.)

|July 3, 2025

Summary

This summary is machine-generated.

Deep complex convolutional networks (DCCNs) outperform real-valued U-Nets for MRI fat-water separation. DCCNs provide superior fat-water maps and liver proton density fat fraction (PDFF) accuracy compared to U-Nets.

Keywords:

Complex-valued convolution Deep complex network Fat–water separation MRI fat quantification Physics-informed deep learning

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Area of Science:

Medical Imaging
Deep Learning
Magnetic Resonance Imaging (MRI)

Background:

Deep complex convolutional networks (DCCNs) process complex-valued MRI signals directly.
Fat-water separation is crucial for quantitative MRI analysis.
Current methods often split complex MRI signals into magnitude and phase components.

Purpose of the Study:

To investigate the performance of DCCNs versus real-valued U-Nets for fat-water separation.
To compare DCCNs and U-Nets within a physics-informed, subject-specific ad-hoc reconstruction framework.
To evaluate the accuracy of DCCNs and U-Nets against a reference approach.

Main Methods:

Utilized a comprehensive dataset of 33 multi-echo MRI scans (abdomen, thigh, knee, phantoms) from the 2012 ISMRM fat-water separation workshop.
Included five additional multi-echo MRIs from MAFLD patients.
Employed a physics-informed, subject-specific ad-hoc reconstruction method for fat-water separation.

Main Results:

DCCNs produced fat-water maps with significantly better normalized RMS error and structural similarity index (SSIM) than real-valued U-Nets.
DCCNs achieved an average SSIM of 0.847 ± 0.069 for fat maps and 0.861 ± 0.078 for water maps.
The average liver proton density fat fraction (PDFF) from DCCNs showed a high correlation (R=0.847) with the reference approach, outperforming U-Nets.

Conclusions:

DCCNs demonstrate superior performance in fat-water separation compared to real-valued U-Nets.
The direct processing of complex-valued MRI signals by DCCNs leads to improved accuracy in quantitative MRI parameters like PDFF.
DCCNs represent a promising advancement for quantitative fat-water separation in MRI.