@inproceedings{adab00ed17df41a4a6c54d02d0a45852,
title = "Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth",
abstract = "We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior is specified through a convolutional neural network (CNN) trained without any artifact-free ground truth to remove under-sampling artifacts from MR images. The results on reconstructing free-breathing MRI data into ten respiratory phases show that the method can form high-quality 4D images from severely undersampled measurements corresponding to acquisitions of about 1 and 2 minutes in length. The results also highlight the competitive performance of the method compared to several popular alternatives, including the TGV regularization and traditional UNet3D.",
keywords = "Image reconstruction, deep learning, magnetic resonance imaging, plug-and-play priors",
author = "Weijie Gan and Cihat Eldeniz and Jiaming Liu and Sihao Chen and Hongyu An and Kamilov, \{Ulugbek S.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 ; Conference date: 01-11-2020 Through 05-11-2020",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/IEEECONF51394.2020.9443403",
language = "English",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "475--479",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020",
}