Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging

By combining a nonlinear physical model with data-driven regularization, NSIDL substantially improves quantitative susceptibility estimation and image quality. This method demonstrates robust artifact suppression and high-fidelity measurements, showing great potential for precise clinical QSM applications.

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Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2026-01-21
DOI
https://doi.org/10.21037/qims-2025-1851
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging

Hongyu Guo, Mingyu Li, Zheng Zhang, Yue Sun
Quantitative Imaging in Medicine and Surgery
Advanced MRI Techniques and Applications
article

Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging

Hongyu Guo, Mingyu Li, Zheng Zhang, Yue Sun
article en

Abstract

By combining a nonlinear physical model with data-driven regularization, NSIDL substantially improves quantitative susceptibility estimation and image quality. This method demonstrates robust artifact suppression and high-fidelity measurements, showing great potential for precise clinical QSM applications.

Quantitative Imaging in Medicine and SurgeryVol. 16(2)
Shenyang University of Technology (CN)
Openalex Percentile: Top 83%
Advanced MRI Techniques and Applications
DOIOpen Access PDFOpenAlex
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Nonlinear inversion model-driven deep learning method for magnetic resonance imaging (MRI) quantitative susceptibility mapping imaging — Hongyu Guo, Mingyu Li, et al. · Quantitative Imaging in Medicine and Surgery (2026) | TGRS Research Map | TGRS