Compressed Sensing Dynamic MRI Reconstruction Using GPU-accelerated 3D Convolutional Sparse Coding
In this paper, we introduce a fast alternating method for reconstructing highly undersampled dynamic MRI data using 3D convolutional sparse coding. The proposed solution leverages Fourier Convolution Theorem to accelerate the process of learning a set of 3D filters and iteratively refine the MRI reconstruction based on the sparse codes found subsequently. In contrast to conventional CS methods which exploit the sparsity by applying universal transforms such as wavelet and total variation, our approach extracts and adapts the temporal information directly from the MRI data using compact shift-invariant 3D filters. We provide a highly parallel algorithm with GPU support for efficient computation, and therefore, the reconstruction outperforms CPU implementation of the state-of-the art dictionary learning-based approaches by up to two orders of magnitude.
This work was partially supported by the 2016 Research Fund (1.160047.01) of UNIST, the R&D program of MOTIE/KEIT (10054548), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2058773) and the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (NRF-2015M3A9A7029725).
- 1.Data science bowl cardiac challenge data (2015). https://www.kaggle.com/c/second-annual-data-science-bowl/data
- 3.Awate, S., DiBella, E.: Spatiotemporal dictionary learning for undersampled dynamic MRI reconstruction via joint frame-based and dictionary-based sparsity. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 318–321, May 2012Google Scholar
- 4.Bristow, H., Eriksson, A., Lucey, S.: Fast convolutional sparse coding. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 391–398, June 2013Google Scholar
- 13.Wohlberg, B.: Efficient convolutional sparse coding. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7173–7177, May 2014Google Scholar
- 14.Yao, J., Xu, Z., Huang, X., Huang, J.: Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 635–642. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24571-3_76CrossRefGoogle Scholar
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.