Structure Preserving Compressive Sensing MRI Reconstruction Using Generative Adversarial Networks
Abstract
Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit. In this work, a novel generative adversarial network (GAN) based framework for CS-MRI reconstruction is proposed. Leveraging a combination of patch-based discriminator and structural similarity index based loss, our model focuses on preserving high frequency content as well as fine textural details in the reconstructed image. Dense and residual connections have been incorporated in a U-net based generator architecture to allow easier transfer of information as well as variable network length. We show that our algorithm outperforms state-of-the-art methods in terms of quality of reconstruction and robustness to noise. Also, the reconstruction time, which is of the order of milliseconds, makes it highly suitable for real-time clinical use.
Cite
Text
Deora et al. "Structure Preserving Compressive Sensing MRI Reconstruction Using Generative Adversarial Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00269Markdown
[Deora et al. "Structure Preserving Compressive Sensing MRI Reconstruction Using Generative Adversarial Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/deora2020cvprw-structure/) doi:10.1109/CVPRW50498.2020.00269BibTeX
@inproceedings{deora2020cvprw-structure,
title = {{Structure Preserving Compressive Sensing MRI Reconstruction Using Generative Adversarial Networks}},
author = {Deora, Puneesh and Vasudeva, Bhavya and Bhattacharya, Saumik and Pradhan, Pyari Mohan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {2211-2219},
doi = {10.1109/CVPRW50498.2020.00269},
url = {https://mlanthology.org/cvprw/2020/deora2020cvprw-structure/}
}