Temporally-Aware Neural Networks For Cine MRI Reconstruction From Severely Undersampled Data

Motivation: MRI guidance of an interventional procedure requires fast image reconstruction. A neural network(NN)-based approach can exploit the similarities between consecutive frames to improve iMRI image reconstruction. Goal(s): We investigate if an LSTM can reconstruct images from just ten spokes per frame in a timeframe compatible with iMRI. Approach: A convolutional (conv)LSTM was trained using the open-source ACDC dataset. Results were compared with Multi-domain convolutional neural network (MD-CNN) - a recently-published 3D NN-based method for undersampled MRI reconstruction. Results: ConvLSTMs can reconstruct frames at ~226 fps (17x faster than MD-CNN ~13 fps). SSIM for the convLSTM was slightly lower than the MD-CNN (0.85 vs 0.89). Impact: With our LSTM-based model, we have achieved a 17x speed-up in the iMRI acquisition process without significant loss in image quality. This suggests that an LSTM-based method could be used to improve iMRI image speed and quality.