# MR image reconstruction using deep learning: evaluation of network structure and loss functions.

@article{Ghodrati2019MRIR,
title={MR image reconstruction using deep learning: evaluation of network structure and loss functions.},
author={Vahid Ghodrati and Jiaxin Shao and Mark Bydder and Ziwu Zhou and Wotao Yin and Kim‐Lien Nguyen and Yingli Yang and Peng Hu},
journal={Quantitative imaging in medicine and surgery},
year={2019},
volume={9 9},
pages={
1516-1527
}
}
• Published 1 September 2019
• Medicine
• Quantitative imaging in medicine and surgery
Background To review and evaluate approaches to convolutional neural network (CNN) reconstruction for accelerated cardiac MR imaging in the real clinical context. Methods Two CNN architectures, Unet and residual network (Resnet) were evaluated using quantitative and qualitative assessment by radiologist. Four different loss functions were also considered: pixel-wise (L1 and L2), patch-wise structural dissimilarity (Dssim) and feature-wise (perceptual loss). The networks were evaluated using…

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