Cardiovascular magnetic resonance (CMR) cine sequences serve as the cornerstone imaging technique for evaluating dynamic left atrial (LA) function in myocarditis patients. By capturing three-dimensional motion characteristics throughout the cardiac cycle with high temporal resolution, this modality provides critical data for analyzing myocardial contractile coordination and wall motion abnormalities. Key technological innovations, such as dynamic modeling and strain-encoded imaging, enable quantitative assessment of early-stage LA systolic-diastolic dysfunction in myocarditis. However, the primary challenges in cine sequence segmentation involve dynamic artifacts and spatiotemporal continuity modeling of thin-walled structures. Traditional threshold-based segmentation methods demonstrate limited consistency in dynamic sequences due to their inability to capture motion patterns. Deep learning approaches utilizing three-dimensional fully convolutional network (3D-FCN) achieved superior accuracy through three strategic enhancements: (1) Spatiotemporal feature fusion: This employed 3D convolutional kernels to simultaneously extract spatial structures and temporal dimensional features, thereby reducing motion blurring effects. (2) Dynamic skip connections: Incorporated within encoder-decoder architectures, these connections strengthened deformation correlation modeling across different cardiac phases through cross-temporal feature propagation. (3) Lightweight design: By utilizing patch-wise processing and depthwise separable convolutions, computational efficiency was optimized for real-time processing of large-scale four-dimensional datasets. The 3D-FCN achieved a Dice coefficient of 0.921 for LA segmentation, representing a 12.3% improvement over conventional methods. This design reduced the LA ejection fraction prediction error from 8.7% to 3.2%. The segmentation results directly facilitated the calculation of quantitative metrics, including LA volume-time curves and strain rates. These metrics supported the clinical diagnosis of myocarditis-associated atrial mechanical dysfunction.
Wang et al. (Fri,) studied this question.