Learned Half-Quadratic Splitting Network for MR Image Reconstruction

Bingyu Xin, Timothy Phan, Leon Axel, Dimitris Metaxas
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, PMLR 172:1403-1412, 2022.

Abstract

Magnetic Resonance (MR) image reconstruction from highly undersampled $k$-space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a learned half-quadratic splitting algorithm for MR image reconstruction and implements the algorithm in an unrolled deep learning network architecture. We compare the performance of our proposed method on a public cardiac MR dataset against DC-CNN, ISTANet$^+$ and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is $1.00$ dB and $1.76$ dB over LPDNet in peak signal-to-noise ratio on $5\times$ and $10\times$ acceleration, respectively. Code for our method is publicly available at \url{https://github.com/hellopipu/HQS-Net.}

Cite this Paper

BibTeX
@InProceedings{pmlr-v172-xin22a, title = {Learned Half-Quadratic Splitting Network for MR Image Reconstruction}, author = {Xin, Bingyu and Phan, Timothy and Axel, Leon and Metaxas, Dimitris}, booktitle = {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning}, pages = {1403--1412}, year = {2022}, editor = {Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi}, volume = {172}, series = {Proceedings of Machine Learning Research}, month = {06--08 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v172/xin22a/xin22a.pdf}, url = {https://proceedings.mlr.press/v172/xin22a.html}, abstract = {Magnetic Resonance (MR) image reconstruction from highly undersampled $k$-space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a learned half-quadratic splitting algorithm for MR image reconstruction and implements the algorithm in an unrolled deep learning network architecture. We compare the performance of our proposed method on a public cardiac MR dataset against DC-CNN, ISTANet$^+$ and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is $1.00$ dB and $1.76$ dB over LPDNet in peak signal-to-noise ratio on $5\times$ and $10\times$ acceleration, respectively. Code for our method is publicly available at \url{https://github.com/hellopipu/HQS-Net.}} }
Endnote
%0 Conference Paper %T Learned Half-Quadratic Splitting Network for MR Image Reconstruction %A Bingyu Xin %A Timothy Phan %A Leon Axel %A Dimitris Metaxas %B Proceedings of The 5th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2022 %E Ender Konukoglu %E Bjoern Menze %E Archana Venkataraman %E Christian Baumgartner %E Qi Dou %E Shadi Albarqouni %F pmlr-v172-xin22a %I PMLR %P 1403--1412 %U https://proceedings.mlr.press/v172/xin22a.html %V 172 %X Magnetic Resonance (MR) image reconstruction from highly undersampled $k$-space data is critical in accelerated MR imaging (MRI) techniques. In recent years, deep learning-based methods have shown great potential in this task. This paper proposes a learned half-quadratic splitting algorithm for MR image reconstruction and implements the algorithm in an unrolled deep learning network architecture. We compare the performance of our proposed method on a public cardiac MR dataset against DC-CNN, ISTANet$^+$ and LPDNet, and our method outperforms other methods in both quantitative results and qualitative results. Finally, we enlarge our model to achieve superior reconstruction quality, and the improvement is $1.00$ dB and $1.76$ dB over LPDNet in peak signal-to-noise ratio on $5\times$ and $10\times$ acceleration, respectively. Code for our method is publicly available at \url{https://github.com/hellopipu/HQS-Net.}
APA
Xin, B., Phan, T., Axel, L. & Metaxas, D.. (2022). Learned Half-Quadratic Splitting Network for MR Image Reconstruction. Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 172:1403-1412 Available from https://proceedings.mlr.press/v172/xin22a.html.

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