Real‐Time Phase‐Contrast Cardiovascular MRI Using a Deep Learning Reconstruction Network With Combined Dictionary Learning and CNN

Purpose This study aims to develop real‐time phase‐contrast (PC) cardiovascular MRI with low latency. Methods In this study, a framework using golden‐angle radial sequence and a deep‐learning‐based reconstruction network, named DLCNet, is proposed for real‐time PC cardiovascular MRI. The DLCNet is designed to capture both spatial and temporal features by combining dictionary learning and CNN. A dataset of 15 normal subjects was acquired and utilized to train and test the DLCNet. The reconstructed image quality and flow measurements at the ascending aorta were compared with different reconstruction algorithms. Real‐time PC cardiovascular MRI was demonstrated with low latency using the proposed framework via Gadgetron platform at a scanner. The prospectively reconstructed results were compared with those obtained from electrocardiograph (ECG)‐gated, breath‐hold, segmented PC. Results The proposed reconstruction network outperformed other algorithms in both image reconstruction quality and flow quantification. The overall framework achieved an imaging speed of 14.6 frames per second, with an image display latency of less than 60 ms. The real‐time flow quantification results showed good agreement with ECG‐gated, breath‐hold, segmented PC‐MRI. Conclusion The proposed framework was successfully demonstrated for real‐time PC cardiovascular MRI with high‐quality image reconstruction and low‐latency image display.