Accelerated reconstruction of highly undersampled 3D cardiac MRI image navigators

Intraprocedural 3D real-time magnetic resonance imaging (MRI) provides a way for accurate and precise radiofrequency catheter targeting during ventricular tachycardia ablation. However, the limited data acquisition time needed to freeze cardiac motion results in highly undersampled k-space data that are challenging to reconstruct. In this work, we evaluated several deep learning (DL) based methods for real-time reconstruction of highly undersampled 3D real-time cardiac MRI. Algorithm reconstruction performance and speed were compared between classical algorithms and DL-based methods. Generative adversarial networks with attention layers in the generator were used to perform reconstructions in the image domain, which strived to balance reconstruction speed and image quality. In addition, variational networks were implemented by iterating data consistency in k-space and enforcing image smoothness via neural network-based regularization. In a preliminary study of heartbeat-resolved highly undersampled 3D cardiac MRI for 11 healthy volunteers, we observed that DL reconstruction methods provided good image quality with a significant increase in computational speed.