Exploiting Motion for Deep Learning Reconstruction of Extremely-Undersampled Dynamic MRI
The problem of accelerated acquisition for dynamic MRI has been recently tackled with deep learning techniques. However, current state-of-the-art approaches do not incorporate a strategy to exploit the full temporal information of the k-space acquisition which would aid in producing higher quality reconstructions. In this paper, we propose a novel method for exploiting the full temporal dynamics for dynamic MRI reconstructions. Specifically, motion estimates are derived from undersampled MRI sequences. These are used to fuse data along the entire temporal axis to produce a novel data-consistent motion-augmented cine (DC-MAC). This is generated and utilised within an end-to-end trainable deep learning framework for MRI reconstruction. In particular, we find that for aggressive acceleration rates of \(\times 51.2 \) on our cardiac dataset, our method with 3-fold cross-validation, ME-CNN, outperforms the current widely-accepted state-of-the-art, DC-CNN, with an improvement of 12% and 16% in PSNR and SSIM respectively. We report an average PSNR of \(27.3\pm 2.5\) and SSIM of \(0.776\pm 0.054\). We also explore the robustness of using ME-CNN for unseen, out-of-domain examples.
GS is funded by the KCL & ICL EPSRC CDT in Medical Imaging (EP/L015226/1). Additionally, this work was supported by EPSRC programme Grant (EP/P001009/1).
- 1.Ahmadi, A., Patras, I.: Unsupervised convolutional neural networks for motion estimation. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1629–1633. IEEE (2016)Google Scholar
- 2.Aviles-Rivero, A.I., Williams, G., Graves, M.J., Schonlieb, C.B.: Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction. arXiv e-prints arXiv:1810.10828 (2018)
- 6.Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 472–480. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_53CrossRefGoogle Scholar
- 8.Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
- 10.Zhao, N., O’Connor, D., Basarab, A., Ruan, D., Sheng, K.: Motion compensated dynamic MRI reconstruction with local affine optical flow estimation. IEEE Trans. Biomed. Eng. 1 (2019). https://doi.org/10.1109/TBME.2019.2900037