  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.
