雷诺分解运动引导纹理学习的瘢痕心肌分型方法

阮东升; 杨代国; 张晓琳; 马建华; 蒋明峰; 汪亚明

doi:10.11999/JEIT260330

雷诺分解运动引导纹理学习的瘢痕心肌分型方法

doi: 10.11999/JEIT260330 cstr: 32379.14.JEIT260330

1.
浙江理工大学计算机科学与工程学院(人工智能学院) 杭州 310018
2.
东南大学网络空间安全学院南京 211189

基金项目: 国家自然科学基金(62272415)

详细信息

作者简介:
阮东升：男，讲师，研究方向为医学图像处理

杨代国：男，硕士，研究方向为医学图像处理

张晓琳：女，硕士，研究方向为医学图像处理

马建华：男，博士生，研究方向为计算机视觉

蒋明峰：男，教授，研究方向为医学图像处理

汪亚明：男，教授，研究方向为医学图像处理

通讯作者:
蒋明峰　m.jiang@zstu.edu.cn

中图分类号: XXXX
计量
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- 被引次数: 0
出版历程
- 修回日期: 2026-07-28
- 录用日期: 2026-07-28
- 网络出版日期: 2026-08-28

Reynolds Decomposition Motion-Guided Texture Learning for Scarred Myocardium Phenotyping

1.
School of Computer Science and Technology, Zhejiang Sci-Tech University , Hangzhou 310018, China
2.
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China

Funds: National Natural Science Foundation of China (NSFC)(62272415)

摘要

摘要: 针对现有基于心脏电影磁共振成像的瘢痕心肌分型方法中运动表征过于离散化且多模态融合机制难以挖掘深层病理关联的问题，提出一种基于运动引导纹理学习的瘢痕心肌分型方法(Motion-Guided Texture Fusion Network，MGTNet)。该方法旨在通过精细化的运动场先验主动引导纹理特征的提取，实现运动与纹理的深度协同。首先引入流体力学中的雷诺分解思想构建雷诺分解运动网络，将心肌运动场解耦为规律性的平均运动与异常性的脉动运动以捕捉微小的运动异常。其次设计运动引导注意力模块，以提炼后的运动特征作为查询向量，通过交叉注意力机制定向增强纹理特征对病灶区域的感知能力。最后结合帧间运动交互与帧内纹理提取模块，实现端到端的精确分型。在CMRD与公开的ACDC数据集上的实验结果表明，该方法在各项关键指标上均优于现有的单模态及多模态融合方法。具体而言，MGTNet在两个数据集上的准确率分别达到95.6%和97.3%，AUC值均超过0.960。相比于基线网络MTNet，该方法在准确率和F1分数上最高分别提升了1.4%和2.3%。结果证实MGTNet通过利用运动先验信息引导纹理特征提取的策略，有效提升了瘢痕心肌的无创诊断精度。
- 瘢痕心肌分型 /
- Cine-MRI /
- 雷诺分解 /
- 运动引导注意力 /
- 多模态融合
Abstract: Objective Scarred myocardium is a key imaging marker for multiple cardiovascular diseases, and its accurate phenotyping holds substantial clinical value for treatment planning and prognosis. Although cine-MRI non-invasively provides both cardiac motion and anatomical texture, existing classification methods suffer from two major limitations: motion representation is often overly discretized and insensitive to subtle abnormalities, and multimodal fusion relies on simple feature concatenation, failing to capture the deep pathological link between motion impairment and texture alteration. To address these issues, we develop a motion-guided texture learning framework to improve non-invasive classification accuracy of scarred myocardium. Methods We propose a motion-guided texture fusion network, termed MGTNet, to achieve deep collaboration between motion and texture features. First, inspired by Reynolds decomposition in fluid dynamics, a Reynolds decomposition motion network is designed within a diffeomorphic registration framework to decouple the myocardial motion field into a regular mean component and an abnormal pulsatile component, enabling refined and pathology-sensitive motion representation. Second, a motion-guided cross-attention module is introduced, where the refined motion features serve as query vectors to dynamically modulate texture features and enhance lesion-related region perception. Additionally, an inter-frame motion interaction module captures temporal dependencies across cardiac frames, while an intra-frame texture extraction module learns multi-scale spatial texture patterns within each frame. Through the serial pipeline of motion estimation, motion-guided texture enhancement, and joint feature representation, end-to-end scarred myocardium classification is achieved. Results and Discussions Experiments on CMRD and ACDC datasets show that MGTNet consistently outperforms existing single-modality and multimodal fusion methods (Table 1 and Table 2). It achieves accuracies of 95.6% (CMRD) and 97.3% (ACDC), with AUCs of 96.6% and 96.0%, respectively. Compared to baseline MTNet, MGTNet improves accuracy by up to 1.4 percentage points and F1-score by up to 2.3 points. Further comparisons with different motion estimation methods (Table 3 and Table 4) demonstrate that the proposed Reynolds decomposition motion network (RDMNet) provides superior discriminative motion priors, achieving best overall performance on both datasets. This indicates that fine-grained motion modeling and motion-guided texture enhancement effectively capture complementary pathological information from cine-MRI. ROC curves of nine methods on both datasets further verify the superiority of our approach. Conclusions A scarred myocardium classification method based on motion-guided texture learning is presented. By introducing Reynolds decomposition into motion estimation and using cross-attention to guide texture extraction with motion priors, the method overcomes discrete motion representation and shallow fusion. Results confirm that deep motion-texture synergy improves non-invasive classification accuracy and robustness, offering an effective approach for cine-MRI-based myocardial phenotyping.
- myocardial scar classification /
- Cine-MRI /
- Reynolds decomposition /
- motion-guided attention /
- multimodal fusion

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图 1 运动引导纹理融合网络

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图 2 雷诺分解运动网络RDMNet

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图 3 帧间运动交互模块和帧内纹理提取模块示意图

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图 4 CMRD 和 ACDC 数据集下九种方法的 ROC 曲线

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图 5 对 MGTNet 中帧间运动交互模块和帧内纹理提取模块最佳数量的讨论

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图 6 对六种案例的运动场的径向运动和环形运动可视化

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表 1 在CMRD数据集上对比九种方法的分类性能

方法	CMRD 数据集
方法	准确率	精确率	敏感度	特异性	F1分数	AUC值
Bi-LSTM	85.5±1.8	56.5±3.0	54.4±3.8	90.4±1.1	53.0±3.2	88.5±1.7
MMENet	86.2±1.7	60.3±3.1	57.5±3.7	90.1±0.7	57.2±2.9	90.6±1.5
STTNet	91.0±1.0	75.2±2.3	71.0±3.0	92.9±0.3	71.3±2.0	93.7±1.0
VMamba	89.6±0.8	70.8±2.8	61.9±3.5	92.4±0.6	62.3±2.7	92.6±1.0
DENet	90.8±1.0	74.6±2.8	68.3±3.0	93.3±0.7	68.4±2.8	93.1±1.3
CNN-LSTM	91.9±0.8	78.5±2.5	74.7±2.5	94.2±0.5	74.6±2.3	93.8±1.1
FRNet	92.1±0.6	82.3±2.0	81.1±2.0	92.0±0.4	80.7±2.0	92.4±0.9
MTNet	94.2±0.4	86.1±1.7	87.5±1.8	95.9±0.3	86.8±1.8	95.1±0.8
MGTNet(ours)	95.6±0.4	88.7±1.5	86.9±1.7	96.3±0.3	89.1±1.7	96.6±0.7

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表 2 在ACDC数据集上对比九种方法的分类性能

方法	ACDC 数据集
方法	准确率	精确率	敏感度	特异性	F1分数	AUC值
Bi-LSTM	80.4±2.7	60.0±4.1	61.4±4.1	86.8±2.5	60.8±3.7	82.3±2.4
MMENet	78.6±2.7	64.4±3.8	60.7±4.6	85.5±2.7	59.6±3.5	81.8±2.6
STTNet	85.7±2.3	72.1±3.5	71.4±3.8	90.5±1.8	70.3±3.4	89.4±1.6
VMamba	91.1±1.8	82.3±3.2	82.0±2.6	94.1±1.3	81.1±3.4	89.8±1.7
DENet	92.5±1.5	84.6±2.8	85.0±2.4	95.2±1.1	84.0±2.9	91.2±1.4
CNN-LSTM	93.8±1.3	88.0±2.5	88.5±2.1	96.3±0.9	87.8±2.4	93.0±1.2
FRNet	93.4±1.0	92.0±1.8	91.2±1.7	96.3±1.0	90.1±2.0	93.5±1.0
MTNet	96.9±1.7	94.4±3.0	94.1±2.3	98.0±1.1	93.7±3.6	95.6±1.1
MGTNet(ours)	97.3±1.4	95.2±2.1	95.0±1.7	98.0±1.0	94.5±3.4	96.0±1.0

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表 3 以MGTNet为网络主干，对比不同的运动场提取方法在CMRD数据集上的性能

分支/方法		CMRD数据集
主干	运动	准确率	精确率	敏感度	特异性	F1分数	AUC值
MGTNet	BOF	88.5±1.5	79.6±3.0	74.4±4.0	84.9±1.8	85.0±3.2	89.1±1.8
	MEB	89.0±1.5	80.0±3.0	75.0±4.0	85.5±1.8	85.5±3.2	89.5±1.8
	MQS	85.2±1.8	75.0±3.5	70.0±4.5	83.0±2.0	80.0±3.5	86.0±2.0
	Voxelmorph	94.0±0.8	86.0±2.0	84.0±2.0	95.0±0.6	87.0±1.8	95.0±0.9
	Deeptag	95.0±0.5	88.0±1.5	86.0±1.7	96.4±0.3	88.8±1.6	96.2±0.7
	Transmatch	94.3±0.7	86.5±1.8	84.5±1.8	95.3±0.5	87.5±1.7	95.3±0.8
	CorrMLP	94.7±0.6	87.2±1.6	85.5±1.6	95.8±0.4	88.2±1.6	95.8±0.7
	RDMNet(ours)	95.6±0.4	88.7±1.5	86.9±1.7	96.3±0.3	89.1±1.7	96.6±0.7

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表 4 以MGTNet为网络主干，对比不同的运动场提取方法在ACDC数据集上的性能

分支/方法		ACDC数据集
主干	运动	准确率	精确率	敏感度	特异性	F1分数	AUC值
MGTNet	BOF	89.5±1.5	77.6±3.0	80.4±3.2	89.1±1.6	85.0±3.2	90.5±1.6
	MEB	90.0±1.5	78.5±3.0	81.0±3.2	89.5±1.6	85.5±3.2	91.0±1.6
	MQS	86.0±1.8	75.0±3.5	77.0±3.5	87.0±1.8	82.0±3.5	88.0±1.8
	Voxelmorph	92.0±1.3	85.0±2.8	87.0±2.5	93.0±1.3	88.0±3.0	92.5±1.3
	Deeptag	95.5±1.0	93.0±2.2	92.0±2.0	96.5±0.9	91.5±2.5	94.5±1.0
	Transmatch	93.5±1.2	88.0±2.5	90.0±2.3	94.5±1.1	90.0±2.8	93.5±1.2
	CorrMLP	96.5±0.6	93.5±2.0	94.5±1.5	97.5±0.5	93.5±2.0	95.8±0.6
	RDMNet(ours)	97.3±1.6	95.2±2.8	95.0±2.1	98.0±1.0	94.5±3.4	96.0±1.0

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