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Updated: Apr 28, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

DeepRelaxo: Fast Mono-Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self-Supervised Deep

Samiha Prima¹, Zhuang Xiong², Alan H Wilman³

¹School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, Queensland, Australia.

Magnetic Resonance in Medicine

|April 27, 2026

Summary

This summary is machine-generated.

DeepRelaxo, a new deep learning method, accurately estimates brain R2* maps from fast multi-echo gradient echo scans. This approach enhances imaging speed and robustness, even with reduced data acquisition.

Keywords:

DeepRelaxo Echo reduction R2* mapping deep learning multi‐echo gradient echo (ME‐GRE)transformer

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Area of Science:

Neuroimaging
Artificial Intelligence
Medical Physics

Background:

Quantitative susceptibility mapping (QSM) relies on accurate R2* estimation.
Multi-echo gradient echo (ME-GRE) sequences are crucial for R2* mapping.
Accelerated imaging protocols are needed for clinical applications.

Purpose of the Study:

Introduce DeepRelaxo, a deep learning method for fast and generalizable R2* mapping.
Enable R2* estimation from ME-GRE acquisitions with arbitrary echo configurations.
Support accelerated scans using shortened echo trains.

Main Methods:

DeepRelaxo employs a two-stage self-supervised network: Transformer-MLP for initial R2* estimation and 3D U-Net for denoising.
The network is trained on simulated multi-echo gradient echo data with varied parameters.
Evaluated against non-linear least squares (NLLS) and Transformer-MLP on simulated and in vivo data.

Main Results:

DeepRelaxo outperforms NLLS and Transformer-MLP in simulations, especially under accelerated conditions (e.g., 4x speedup).
Achieved significant improvements in SSIM (13.5%) and RMSE (76%) at low SNR (SNR=10).
In vivo data (3T and 7T) showed consistent R2* values in deep gray matter and preserved anatomical detail.

Conclusions:

DeepRelaxo accurately models ME-GRE decay using temporal and spatial context.
Provides robust and computationally efficient R2* mapping.
Enables reliable reconstruction for accelerated protocols in time-sensitive workflows.