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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural

Ming Zhang¹, Ruimin Feng¹, Zhenghao Li¹

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Medical Image Analysis

|April 24, 2024

Summary

This summary is machine-generated.

This study introduces INR-QSM, an unsupervised deep learning method for quantitative susceptibility mapping (QSM) that overcomes data limitations and improves accuracy without needing paired training data.

Keywords:

Implicit neural representation Phase compensation Quantitative susceptibility mapping Unsupervised learning

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging (MRI)
Computational Neuroscience

Background:

Quantitative susceptibility mapping (QSM) reconstructs tissue magnetic susceptibility from MRI phase signals.
Deep learning (DL) methods for QSM face challenges with data availability and generalization.
Existing DL approaches may neglect non-local tissue phase effects, impacting reconstruction accuracy.

Purpose of the Study:

To develop an unsupervised, subject-specific DL method for QSM reconstruction.
To address limitations of existing DL methods in QSM, including data dependency and non-local effects.
To improve the accuracy and generalizability of QSM.

Main Methods:

Proposed INR-QSM, an unsupervised DL method using implicit neural representation (INR).
Represented susceptibility maps as continuous functions parameterized by neural networks.
Incorporated a data fidelity term with a physical model and regularization.
Introduced a novel phase compensation strategy to account for non-local tissue phase effects.

Main Results:

INR-QSM demonstrated superior qualitative and quantitative performance compared to traditional and unsupervised DL methods.
The method achieved competitive results against supervised DL approaches, even with data perturbations.
The novel phase compensation improved the physical model's accuracy.

Conclusions:

INR-QSM offers a promising unsupervised approach for accurate QSM reconstruction.
The method effectively handles data limitations and improves upon existing DL techniques.
Implicit neural representation combined with phase compensation advances QSM methodology.