Integrating hierarchical semantic networks with physical models for MRI reconstruction

Zhang Xiaomin; Bao Lingxin

doi:10.12086/oee.2025.250016

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Zhang X M, Bao L X. Integrating hierarchical semantic networks with physical models for MRI reconstruction[J]. Opto-Electron Eng, 2025, 52(5): 250016. doi: 10.12086/oee.2025.250016

Citation:

Zhang X M, Bao L X. Integrating hierarchical semantic networks with physical models for MRI reconstruction[J]. Opto-Electron Eng, 2025, 52(5): 250016. doi: 10.12086/oee.2025.250016

Integrating hierarchical semantic networks with physical models for MRI reconstruction

Zhang Xiaomin^1,,
Bao Lingxin^2, ,

1.
The Internet of Things and Artificial Intelligence College, Fujian Polytechnic of Information Technology, Fuzhou, Fujian 350003, China
2.
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

Fund Project: 2023 Cognition and Intelligence National Key Laboratory Topic (COGOS-2023HE03), Natural Science Foundation of Fujian Province (2023J01471), Special Fund for Key Laboratory Construction Project of Fujian Province for Smart Agriculture and Forestry in Universities (KJG23033A), and Science and Technology Innovation Special Fund of Fujian Agricultural and Forestry University (KFB23159)

More Information

^*Corresponding author: bolingxmu@sina.com
CSTR: 32245.14.oee.2025.250016

Received Date 20 January 2025

Revised Date 11 March 2025

Accepted Date 12 March 2025

Published Date 30 May 2025

Abstract

Abstract

To address the challenge of prolonged acquisition times in magnetic resonance imaging (MRI), data-driven algorithms and the model integration have emerged as crucial approaches for achieving high-quality MRI reconstruction. However, existing methods predominantly focus on visual feature extraction while neglecting deep semantic information critical for robust reconstruction. To bridge this gap, this study proposes a model-driven architecture that synergistically combines hierarchical semantic networks with physical model networks, aiming to enhance reconstruction performance while maintaining computational efficiency. The architecture comprises four core modules: a context extraction module to capture rich contextual features and mitigate background interference; a multi-scale aggregation module integrating multi-scale information to preserve coarse-to-fine anatomical details; a semantic graph reasoning module to model semantic relationships for improved tissue differentiation and artifact suppression; a dual-scale attention module to enhance critical feature representation across different detail levels. This hierarchical and semantic-aware design effectively reduces aliasing artifacts and significantly improves image fidelity. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both quantitative metrics and visual quality across diverse datasets with varying sampling rates. For instance, in 4× radial acceleration experiments on the IXI dataset, our approach achieved a peak signal-to-noise ratio (PSNR) of 48.15 dB, surpassing the latest comparison algorithms by approximately 1.00 dB on average, while enabling higher acceleration rates and maintaining reliable reconstruction outcomes.
- MRI acceleration /
- contextual information /
- model-guided approaches /
- semantic reasoning

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References

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Overview

Overview

Magnetic resonance imaging (MRI) is a critical tool in biomedical research and clinical practice due to its exceptional soft tissue contrast and non-invasive, radiation-free imaging capabilities. However, its prolonged acquisition times (resulting from sequential k-space sampling) limit its efficiency in clinical workflows, patient comfort, and applicability in time-sensitive or large-scale screening scenarios. To address this challenge, undersampled k-space reconstruction techniques, particularly compressive sensing (CS), have been widely adopted. While CS leverages signal sparsity and prior knowledge to preserve image quality, aggressive undersampling often introduces aliasing artifacts, which complicates accurate reconstruction. Traditional model-driven methods, which rely on handcrafted priors (such as, sparsity, total variation, low-rank constraints), struggle to adapt to the complexity of real-world MRI data distributions. Meanwhile, deep learning approaches, despite their success in feature learning, often neglect physical degradation mechanisms. Additionally, they fail to capture semantic-level features necessary for distinguishing anatomical structures and suppressing artifacts.

Recent efforts aim to bridge model-driven and data-driven paradigms, but existing methods remain constrained by rigid feature representations, inadequate multi-scale context integration, and insufficient semantic reasoning. For example, the prior models prioritize shallow visual features over deeper anatomical semantics, limiting their ability to generalize across diverse tissue types and reconstruct high-fidelity images under extreme undersampling. To address these limitations, this paper proposes a physics-informed hierarchical semantic network (PHSN), an innovative framework that unifies physical signal principles with semantic-aware deep learning. The architecture integrates four core modules: 1) a context extraction module to capture spatial dependencies across scales; 2) a scale-aggregation module for hierarchical feature fusion; 3) a semantic graph reasoning module to model tissue-specific relationships and enhance artifact suppression via graph-based inference; 4) a dual-scale attention module to prioritize anatomical details at both coarse (global structure) and fine (local tissue) spatial levels. The overall framework is illustrated in Fig. 7.

By embedding physical k-space sampling constraints into hierarchical feature learning and explicitly modeling semantic relationships, PHSN achieves robust reconstruction under high acceleration factors (e.g., 8-fold) while preserving diagnostic image fidelity. Comprehensive experiments on diverse datasets (e.g., IXI) demonstrate that the proposed method outperforms state-of-the-art approaches in suppressing artifacts, maintaining anatomical accuracy, and balancing acceleration efficiency with clinical reliability. The framework’s hierarchical design enables adaptive feature optimization across scales, effectively mitigating aliasing artifacts and improving visibility of complex anatomical structures. This study advances MRI reconstruction by integrating interpretable physics-based constraints with data-driven semantic understanding, offering a pathway toward faster, higher-quality imaging for clinical and research applications.