@inproceedings{0766e5bf681c4f059ed760a00d86f1f7,
title = "Multi-Contrast Mr Reconstruction with Enhanced Denoising Autoencoder Prior Learning",
abstract = "This paper proposes an enhanced denoising autoencoder prior (EDAEP) learning framework for accurate multi-contrast MR image reconstruction. A multi-model structure with various noise levels is designed to capture features of different scales from different contrast images. Furthermore, a weighted aggregation strategy is proposed to balance the impact of different model outputs, making the performance of the proposed model more robust and stable while facing noise attacks. The model was trained to handle three different sampling patterns and different acceleration factors on two public datasets. Results demonstrate that our proposed method can improve the quality of reconstructed images and outperform the previous state-of-the-art approaches. The code is available at https://github.com/yqx7150.",
keywords = "Au-toencoder prior, deep learning, Multi-contrast MR reconstruction",
author = "Xiangshun Liu and Minghui Zhang and Qiegen Liu and Taohui Xiao and Hairong Zheng and Leslie Ying and Shanshan Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098334",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1432--1436",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}