  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.
