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.

翻译：在心脏磁共振成像分析中，心肌分割与T2定量同步进行对于评估心肌病理至关重要。现有方法通常分别处理这两项任务，限制了其协同潜力。为此，我们提出SQNet——一种融合Transformer与卷积神经网络组件的双任务网络。SQNet具备面向定量分析的T2优化融合解码器（利用Transformer的全局特征）以及配备多局部区域监督的分割解码器以提升精度。紧密耦合模块对齐并融合CNN与Transformer分支特征，使SQNet能聚焦于心肌区域。在健康对照组与急性心肌梗死患者上的评估显示，其分割Dice分数（89.3/89.2）优于现有最佳方法（87.7/87.9）。T2定量结果与健康对照组/急性心肌梗死患者的标注值呈现强线性相关性（皮尔逊系数：0.84/0.93），表明映射准确。放射科医师评估证实SQNet在图像质量评分上（分割任务：4.60/4.58；T2定量：4.32/4.42）优于现有最佳方法（分割任务：4.50/4.44；T2定量：3.59/4.37）。因此，SQNet能实现精确的同步分割与定量，为急性心肌梗死等心脏疾病诊断提供增强支持。