The application of deep learning for reconstruction of dynamic contrast-enhanced MRI presents significant challenges caused by the rapid passage of the contrast agent, which makes it difficult to acquire fully-sampled images to train a neural network. This work proposes to use images from a delayed contrast phase, where contrast changes are in a relatively steady state, for training, and to apply the trained neural network for reconstruction of undersampled data acquired in other contrast phases. The proposed contrast transfer learning reconstruction was trained on 55 post-contrast liver cases and tested on a first-pass liver DCE-MR acquisition.
This abstract and the presentation materials are available to members only; a login is required.