A feature‐based convolutional neural network for reconstruction of interventional MRI. (19th December 2019) - Zufiria, Blanca; Qiu, Suhao; Yan, Kang; Zhao, Ruiyang; Wang, Runke; She, Huajun; Zhang, Chengcheng; Sun, Bomin; Herman, Pawel; Du, Yiping; Feng, Yuan
Abstract : Real‐time interventional MRI (I‐MRI) could help to visualize the position of the interventional feature, thus improving patient outcomes in MR‐guided neurosurgery. In particular, in deep brain stimulation, real‐time visualization of the intervention procedure using I‐MRI could improve the accuracy of the electrode placement. However, the requirements of a high undersampling rate and fast reconstruction speed for real‐time imaging pose a great challenge for reconstruction of the interventional images. Based on recent advances in deep learning (DL), we proposed a feature‐based convolutional neural network (FbCNN) for reconstructing interventional images from golden‐angle radially sampled data. The method was composed of two stages: (a) reconstruction of the interventional feature and (b) feature refinement and postprocessing. With only five radially sampled spokes, the interventional feature was reconstructed with a cascade CNN. The final interventional image was constructed with a refined feature and a fully sampled reference image. With a comparison of traditional reconstruction techniques and recent DL‐based methods, it was shown that only FbCNN could reconstruct the interventional feature and the final interventional image. With a reconstruction time of ~ 500 ms per frame and an acceleration factor of ~ 80, it was demonstrated that FbCNN had the potential for application in real‐time I‐MRI. Abstract : The feature‐based convolutional neural network (FbCNN) is a two‐stage reconstruction algorithm for real‐time interventional MRI. With only five golden‐angle radially sampled spokes, FbCNN outperforms other deep‐learning based algorithms with a latency time of less than 0.5 seconds.