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Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI

Authors :
Zhou, Yirong
Wang, Chengyan
Lu, Mengtian
Guo, Kunyuan
Wang, Zi
Ruan, Dan
Guo, Rui
Zhao, Peijun
Wang, Jianhua
Wu, Naiming
Lin, Jianzhong
Chen, Yinyin
Jin, Hang
Xie, Lianxin
Wu, Lilan
Zhu, Liuhong
Zhou, Jianjun
Cai, Congbo
Wang, He
Qu, Xiaobo
Zhou, Yirong
Wang, Chengyan
Lu, Mengtian
Guo, Kunyuan
Wang, Zi
Ruan, Dan
Guo, Rui
Zhao, Peijun
Wang, Jianhua
Wu, Naiming
Lin, Jianzhong
Chen, Yinyin
Jin, Hang
Xie, Lianxin
Wu, Lilan
Zhu, Liuhong
Zhou, Jianjun
Cai, Congbo
Wang, He
Qu, Xiaobo
Publication Year :
2024

Abstract

In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural Network (CNN) components. SQNet features a T2-refine fusion decoder for quantitative analysis, leveraging global features from the Transformer, and a segmentation decoder with multiple local region supervision for enhanced accuracy. A tight coupling module aligns and fuses CNN and Transformer branch features, enabling SQNet to focus on myocardium regions. Evaluation on healthy controls (HC) and acute myocardial infarction patients (AMI) demonstrates superior segmentation dice scores (89.3/89.2) compared to state-of-the-art methods (87.7/87.9). T2 quantification yields strong linear correlations (Pearson coefficients: 0.84/0.93) with label values for HC/AMI, indicating accurate mapping. Radiologist evaluations confirm SQNet's superior image quality scores (4.60/4.58 for segmentation, 4.32/4.42 for T2 quantification) over state-of-the-art methods (4.50/4.44 for segmentation, 3.59/4.37 for T2 quantification). SQNet thus offers accurate simultaneous segmentation and quantification, enhancing cardiac disease diagnosis, such as AMI.<br />Comment: 10 pages, 8 figures, 6 tables

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1438557352
Document Type :
Electronic Resource