Convolutional recurrent U-net for cardiac cine MRI reconstruction via effective spatio-temporal feature exploitation.

BACKGROUND: Cardiac Cine Magnetic Resonance Imaging (MRI) provides dynamic visualization of the heart's structure and function but is hindered by slow acquisition, requiring repeated breath-holds that challenge sick patients. Accelerated imaging can mitigate these issues but potentially reduce spatial and temporal resolution. Therefore, innovative approaches are essential to ensure effective performance under high acceleration conditions. Deep learning-based reconstruction methods show promise in enhancing image quality from highly undersampled data, accelerating scans while maintaining diagnostic accuracy. However, they often fail to effectively exploit the spatio-temporal features inherent to cine MRI, which are essential for accurate reconstruction, thereby leaving room for further improvement. PURPOSE: We aim to more effectively exploit the spatio-temporal features inherent in cine MRI sequences by integrating convolutional recurrent operations with a U-Net architecture, enhancing the reconstruction performance of cine MRI. METHODS: We developed a new deep learning model called CRUNet-MR that enhances the extraction of spatio-temporal features by combining convolutional recurrent operations with a U-Net structure. This design ensures continuous extraction of temporal features while fusing fine-grained spatial details with high-level semantic information. Furthermore, dilated convolutions are incorporated to expand the spatial receptive field, and appropriate combinations of dilation factors are explored to further enhance overall performance. RESULTS: Training, validation, and testing were performed on the public CMRxRecon2023 dataset, using two views and four acceleration factors ranging from 4 to 24 with the given Auto-Calibration Signal (ACS) area. The dataset consists of 120 subjects for training, 60 for validation, and 120 for testing. In general, the proposed CRUNet-MR shows statistically significant differences with benchmark models and consistently outperforms them, particularly showcasing better reconstruction quality in dynamic regions, highlighting its effective extraction of spatio-temporal features. Ablation studies further validated the design choices of CRUNet-MR. The model demonstrated strong reconstruction performance, achieving an average SSIM of 0.986 at an acceleration factor of 4 and 0.971 at a factor of 8 across both views. Furthermore, CRUNet-MR was validated on a small in-house LUMC dataset, showing its generalization capability and rapid adaptability through fine-tuning. CONCLUSIONS: The proposed CRUNet-MR model is well-suited for cine MRI reconstruction, effectively leveraging spatio-temporal features to reconstruct high-quality images, especially in dynamic cardiac regions. This capability highlights its potential to support higher acceleration factors, enabling faster and more patient-friendly cardiac imaging.