Spatial Frequency Adaptive Spatiotemporal Learning for Accelerating CMR Reconstruction

Cardiac magnetic resonance (CMR) is a vital noninvasive imaging modality for evaluating cardiac morphology and function. However, current scanning systems require a long acquisition time, which often introduces motion artifacts and limits the spatiotemporal resolution needed for real-time imaging. Reconstructing images from highly undersampled data offers a promising solution to accelerate clinical examinations. To this end, this article proposes a novel dynamic reconstruction approach—spatial frequency adaptive modulation guided spatialtemporal learning (FASTC)—for accelerating CMR reconstruction. By leveraging the dynamic nature of the CMR acquisition process, we introduce a spatiotemporal adaptive modulation strategy that jointly learns from both temporal and spatial dimensions. Specifically, inspired by linear self-attention (LSA), we introduce an efficient spatiotemporal feature learning (STFL) module to fully exploit the complementary information hidden in consecutive scanning sequences. In addition, the designed frequency adaptive modulation (FAM) mechanism selectively amplifies or suppresses features across multiple frequency subbands to optimize feature information flow. Extensive experiments across diverse accelerated scenarios—such as varying undersampling patterns and acceleration rates—demonstrate that the proposed method outperforms state-of-the-art approaches and effectively reconstructs high-fidelity images.