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This study introduces a semi-supervised spatiotemporal knowledge distillation framework for aortic tracking in cardiac cine-MRI, addressing the limitations of standard 2D segmentation networks that fail to maintain temporal consistency during rapid systolic flow. By leveraging a recurrent spatiotemporal bottleneck and a residual spatial bypass, the proposed method distills knowledge from a spatial teacher network to enhance the performance of a spatiotemporal student model. The results demonstrate a significant improvement in surface tracking accuracy and structural reliability, reducing population-wide anomalies by over 56% compared to traditional 2D approaches.
Achieving 92.3% tracking accuracy and cutting structural anomalies by over half, this method transforms how we analyze aortic motion in cardiac MRI.
Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural changes across the cardiac cycle is essential for measuring aortic distensibility, a primary marker of arterial stiffness. However, standard 2D segmentation networks focus on each frame independently. Consequently, when rapid systolic flow temporarily obscures the aorta's boundaries, this lack of continuous context results in frame-to-frame tracking dropouts and boundary inconsistencies. Spatiotemporal ($2\text{D}+t$) networks can enforce temporal consistency across the sequence but suffer from a scarcity of expert annotations. To address this, we present a semi-supervised spatiotemporal ($2\text{D}$ to $2\text{D}+t$) knowledge distillation framework exploiting the cardiac cycle. The framework distills a spatial teacher's expertise into a spatiotemporal student network by executing a dynamic latent interception, pairing a recurrent spatiotemporal bottleneck with a residual spatial bypass. Our model selection strategy applies a baseline validation threshold ($\text{DSC} \ge 0.50$) prior to selecting the epoch that maximizes anatomical consistency. This strategy enables the spatiotemporal student model to achieve superior surface tracking accuracy ($\text{NSD@1mm} = 92.3\% \pm 0.2\%$) and high structural reliability ($\text{Frac}_{2\text{CC}} = 99.2\% \pm 0.6\%$), reducing population-wide structural anomalies by over 56\% compared to a 2D nnU-Net baseline.
