Cardiac Cine MRI with Dimension-Reduced Deep Separable Spatiotemporal Learning

Motivation: Cardiac cine MRI reconstruction is a natural high-dimensional problem that poses great challenges to deep learning. Goal(s): To develop a new deep learning method that can work efficiently in cardiac cine MRI, even with limited training data. Approach: In this work, the proposed method DeepSSL significantly alleviates training and generalization challenges of deep learning in cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling. Results: Extensive results show that DeepSSL can work efficiently even with highly limited training data (5~10 cases), and provides state-of-the-art reconstructions while reduces data demand by up to 75%. It further shows robustness in prospective real-time MRI. Impact: The proposed deep separable spatiotemporal learning (DeepSSL) significantly alleviates the training and generalization challenges of deep learning in high-dimensional cardiac cine MRI through efficient dimension-reduced separable learning and spatiotemporal modeling.