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A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction by Zhao, Ruiyang; He, Zhao; Wang, Tao; Qiu, Suhao; Herman, Pawel; Hu, Yanle; Zhang, Chencheng; Shen, Dinggang; Sun, Bomin; Yang, Guang-Zhong; Feng, Yuan is a scholarly article available to read on EtoBox.

What is A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction about?

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

Author

Zhao, Ruiyang; He, Zhao; Wang, Tao; Qiu, Suhao; Herman, Pawel; Hu, Yanle; Zhang, Chencheng; Shen, Dinggang; Sun, Bomin; Yang, Guang-Zhong; Feng, Yuan

Published

2022

Language

EN