Dual-Domain Tripartite Complex GAN for Compressed Sensing MRI Reconstruction

WANG Yulu; WU Min; ZHAN Tianming; SUN Yubao

doi:10.12263/DZXB.20250371

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Dual-Domain Tripartite Complex GAN for Compressed Sensing MRI Reconstruction

PAPERS | 更新时间：2026-06-22

- Dual-Domain Tripartite Complex GAN for Compressed Sensing MRI Reconstruction
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 381-394(2026)
- 作者机构：
  
  1.南京信息工程大学计算机学院，江苏南京 210044
  2.中国人民解放军东部战区总医院医学工程科，江苏南京 210016
  3.南京审计大学计算机学院，江苏南京 211815
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(92470126;62276139);Major Research Plan of Jiangsu Province(BG2024042);Jiangsu Provincial Qinglan Project(R2024Q06)
- DOI：10.12263/DZXB.20250371
  CLC： TP391.41;TP751
- Received：10 May 2025，
  
  Accepted：26 December 2025，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
王玉露, 吴敏, 詹天明, 等. 压缩感知磁共振成像双域三边复数GAN模型[J]. 电子学报, 2026, 54(01): 381-394.

WANG Yulu, WU Min, ZHAN Tianming, et al. Dual-Domain Tripartite Complex GAN for Compressed Sensing MRI Reconstruction[J]. Acta Electronica Sinica, 2026, 54(01): 381-394.
王玉露, 吴敏, 詹天明, 等. 压缩感知磁共振成像双域三边复数GAN模型[J]. 电子学报, 2026, 54(01): 381-394. DOI：10.12263/DZXB.20250371

WANG Yulu, WU Min, ZHAN Tianming, et al. Dual-Domain Tripartite Complex GAN for Compressed Sensing MRI Reconstruction[J]. Acta Electronica Sinica, 2026, 54(01): 381-394. DOI：10.12263/DZXB.20250371

摘要

压缩感知磁共振成像（Compressed Sensing Magnetic Resonance Imaging，CS-MRI）通过减少频域

-空间采样数据量来加速成像过程，其核心科学问题在于如何从欠采样的、不完备的

-空间数据中高效地重建出原高质量的核磁共振成像（Magnetic Resonance Imaging，MRI）图像。近年来，基于深度神经网络的重建方法取得了重要进展，有效推动了重建图像质量的持续提升。然而，现有的深度重建网络大多采用实数网络模型，而原始

-空间采样数据为复数信息，实数网络模型不利于充分捕获复数采样所蕴含的图像细节和结构特征，这直接影响了重建质量的进一步提升。同时，现有重建网络通常缺乏频域

-空间与图像域之间的交互约束机制，双域特征学习存在不足。针对这些问题，本文创新性地提出了一种双域三边复数生成对抗网络（Dual-domain Tri-edge Complex Generative Adversarial Network，DualTri-CGAN）重建模型。该模型主要包含空间生成器和图像域生成器两个生成器，它们共同构成了双域生成器框架，并采用实数判别器对生成结果进行评估。双域生成器均采用多尺度编解码架构，能够有效捕捉不同尺度的图像特征。此外，双域生成器通过残差连接实现多尺度特征的有效融合，增强了特征表示能力。为进一步提升双域生成器的协同生成能力，本文采用了三边对抗博弈学习模式。该模式不仅包含双域生成器与判别器间的常规对抗训练，还引入了双域子生成器间的对抗机制。在损失函数设计方面，除常规对抗损失外，本文专门设计了一种新颖的相似性对抗损失函数。该损失函数通过约束两个生成器输出间的一致性，促使它们在对抗训练中相互促进、协同优化，提升MRI重建图像质量。在实验验证方面，本文使用公开的IXI Brain（Information eXtraction from Images Brain）数据集对模型性能进行系统测试。实验结果表明，相较于现有的生成对抗网络（Generative Adversarial Network，GAN）重建模型，DualTri-CGAN能够更好地处理

-空间复采样数据，避免因实部与虚部分离处理而引入重建误差。同时，得益于所提出的双域生成器框架和三边对抗博弈学习模式，该模型有效提升了重建图像的峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）和结构相似性指数（Structural Similarity Index Measure，SSIM），在10%低采样率条件下，DualTri-CGAN仍然能够有效恢复MRI图像的边缘结构和纹理

细节，展现出良好的重建性能和鲁棒性。

Abstract

Compressed sensing magnetic resonance imaging (CS-MRI) accelerates image acquisition by substantially reducing the amount of data sampled in the frequency domain (

-space). The core scientific challenge lies in the high-fidelity reconstruction of MRI images from such incomplete

under sampled

-space data. While deep neural network-based reconstruction methods have recently driven significant progress and continuously improved output quality

a key limitation remains: most existing deep models employ real-valued network architectures. This creates a critical mismatch

as the raw data from magnetic resonance imaging (MRI) scanners is inherently complex-valued

containing both magnitude and phase information. Real-valued networks typically process this data by separating or discarding its complex components

which hinders the full exploitation of the detailed structural features inherent in complex

-space signals

thereby limiting further gains in reconstruction fidelity. Furthermore

prevailing reconstruction networks often operate within a single domain (image or

-space) or use simple sequential processing

lacking a sophisticated interactive mechanism that explicitly enforces consistency between the frequency and image domains. This leads to insufficient and sub-optimal dual-domain feature learning

leaving potential performance improvements unrealized.To address these issues

this paper proposes an innovative dual-domain

three-party complex-valued generative adversarial network named the dual-domain tri-edge complex generative adversarial network (DualTri-CGAN) reconstruction model. Its core architecture features two principal generators: a

-space generator and an image-domain generator

forming a comprehensive dual-domain generation framework. This framework is paired with a real-valued discriminator that evaluates the authenticity

of the generated outputs. Both generators are built on a multi-scale encoder-decoder structure

enabling effective extraction and utilization of image features across different scales

from local textures to global anatomy. Additionally

residual connections are integrated within the generators to effectively fuse multi-scale features

significantly enhancing overall feature representation. A pivotal innovation is the introduction of a three-party adversarial learning paradigm. This advanced scheme goes beyond the conventional adversarial game between the generators and the discriminator by incorporating a novel

direct adversarial mechanism between the two sub-generators

fostering a competitive yet collaborative dynamic. For the loss function

alongside standard adversarial losses

a novel similarity adversarial loss is designed. This specialized loss explicitly enforces consistency and alignment between the outputs of the two generators

compelling them to mutually inform

regularize

and optimize each other during adversarial training. This results in superior collaborative performance and

ultimately

higher-quality MRI reconstructions.For experimental validation

the proposed DualTri-CGAN model was systematically evaluated on the public information extraction from images brain (IXI Brain) dataset. Results demonstrate that

compared to existing state-of-the-art generative adversarial network (GAN)-based models

DualTri-CGAN exhibits superior native handling of complex-valued

-space data. This approach effectively avoids the reconstruction errors and information loss typically arising from the separate processing of real and imaginary components in real-valued networks. Moreover

the synergistic benefits of the dual-domain generator framework and the three-party adversarial learning strategy collectively lead to measurable improvements in key image quality metrics

namely higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Notably

even under a stringent 10% sam

pling rate

DualTri-CGAN maintains a robust capability to accurately recover fine edge details and nuanced textures in MRI images. These findings underscore the model’s excellent reconstruction performance

generalization ability

and strong robustness

marking a promising advancement for fast

high-quality CS-MRI.

关键词

Keywords

references

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