Interventional magnetic resonance imaging (i-MRI) for surgical guidance could help visualize the interventional process such as deep brain stimulation (DBS), improving the surgery performance and patient outcome. Different from retrospective reconstruction in conventional dynamic imaging, i-MRI for DBS has to acquire and reconstruct the interventional images sequentially online. Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. By using an initializer and Conv-LSTM blocks, the priors from the pre-operative reference image and intra-operative frames were exploited for reconstructing the current frame. Data consistency for radial sampling was implemented by a soft-projection method. To improve the reconstruction accuracy, an adversarial learning strategy was adopted. A set of interventional images based on the pre-operative and post-operative MR images were simulated for algorithm validation. Results showed with only 10 radial spokes, ConvLR provided the best performance compared with state-of-the-art methods, giving an acceleration up to 40 folds. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
@article{arxiv.2203.14769,
title = {A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction},
author = {Ruiyang Zhao and Zhao He and Tao Wang and Suhao Qiu and Pawel Herman and Yanle Hu and Chencheng Zhang and Dinggang Shen and Bomin Sun and Guang-Zhong Yang and Yuan Feng},
journal= {arXiv preprint arXiv:2203.14769},
year = {2022}
}