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Magnetic Resonance Imaging

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Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties

Sabrina Zumbo¹, Stefano Mandija^2,3, Ettore F Meliado³

¹Department DIIESUniversità Mediterranea di Reggio Calabria 89124 Reggio Calabria Italy.

IEEE Open Journal of Engineering in Medicine and Biology

|July 25, 2024

Summary

This study introduces a novel, AI-driven method for Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) to map tissue electrical properties. The approach achieves high-quality 2D reconstructions comparable to existing methods but with significantly reduced computation time.

Keywords:

Convolutional neural network electrical properties inverse scattering problems learning methods magnetic resonance imaging

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Area of Science:

Biomedical Imaging
Medical Physics
Computational Electromagnetics

Background:

Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) is a non-invasive method for assessing biological tissue electrical properties (EPs).
Accurate EPs mapping is crucial for various MRI applications, including diagnostics and treatment planning.
Current MR-EPT reconstruction methods can be computationally intensive, limiting their clinical applicability.

Purpose of the Study:

To develop and evaluate an unrolled, physics-assisted deep learning method for accelerated 2D MR-EPT reconstructions.
To assess the performance of the proposed method in terms of accuracy and computational efficiency compared to established techniques.
To investigate the use of Convolutional Neural Networks (CNNs) within an iterative reconstruction framework for MR-EPT.

Main Methods:

A novel unrolled, physics-assisted method employing a cascade of CNNs for 2D MR-EPT reconstruction was developed.
Each CNN in the cascade was designed to compute contrast updates, incorporating physical principles via gradient descent directions.
The method was trained and validated in silico using realistic 2D brain models at 128 MHz.

Main Results:

The proposed physics-assisted deep learning method successfully reconstructed electrical properties (EPs) maps from 2D MR-EPT data.
Reconstruction quality was found to be comparable to the widely used Contrast Source Inversion-EPT (CSI-EPT) method.
A significant reduction in computational time was achieved compared to traditional methods.

Conclusions:

The developed unrolled, physics-assisted deep learning approach offers a promising alternative for fast and accurate 2D MR-EPT.
This AI-driven technique has the potential to accelerate EPs mapping in MRI, facilitating broader clinical adoption.
The integration of physics-based constraints within deep learning enhances the robustness and efficiency of MR-EPT reconstructions.