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（MRI）分析中，同步进行心肌分割与T2定量分析对评估心肌病理状态至关重要。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（CNN）组件的双任务网络。SQNet通过T2精炼融合解码器实现定量分析，该解码器利用Transformer的全局特征；同时采用多局部区域监督的分割解码器提升精度。紧密耦合模块对齐并融合CNN与Transformer分支特征，使SQNet聚焦心肌区域。在健康对照组（HC）与急性心肌梗死患者（AMI）的评估中，SQNet的分割Dice分数（89.3/89.2）优于现有最优方法（87.7/87.9）。T2定量分析与标注值呈现强线性相关性（Pearson系数：HC为0.84/AMI为0.93），表明映射准确性高。放射科医师评估证实，SQNet的图像质量评分（分割4.60/4.58，T2定量4.32/4.42）优于最优方法（分割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分析，增强对AMI等心脏疾病的诊断能力。