Abstract
In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our correction method is inspired by work on undersampled CMR reconstruction, and uses deep learning to optimize a data-consistency term for under-sampled k-space reconstruction. Our main methodological contribution is the addition of a detection network to classify motion-corrupted k-space lines to convert the problem of artefact correction to a problem of reconstruction using the data consistency term. We train our network to automatically correct for motion-related artefacts using synthetically corrupted cine CMR k-space data as well as uncorrupted CMR images. Using a test set of 50 2D+time cine CMR datasets from the UK Biobank, we achieve good image quality in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for recovering good image quality and showcase better performance compared to state of the art denoising techniques with a PSNR of 37.1. Moreover, we show that our method preserves the quality of uncorrupted images and therefore can be also utilized as a general image reconstruction algorithm.
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References
Ferreira, P.F., et al.: Cardiovascular magnetic resonance artefacts. JCMR 15(1), 1–41 (2013)
Han, Y., et al.: k-Space Deep Learning for Accelerated MRI. arXiv:1805.03779 (2018)
Hauptmann, A., et al.: Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn. Reson. Med. 81, 1143–1156 (2019)
Hyun, C.M., et al.: Deep learning for under sampled MRI reconstruction. Phys. Med. Biol. 63(13), 135007 (2018)
Korshunova, I., et al.: Diagnosing heart diseases with deep neural networks (2016). http://irakorshunova.github.io/2016/03/15/heart.html
Liu, P., Fang, R.: Wide inference network for image denoising. arXiv preprint arXiv:1707.05414 (2017)
Lotjonen, J., et al.: Correction of motion artifacts from cardiac cine magnetic resonance images. Acad. Radiol. 12(10), 1273–1284 (2005)
Oksuz, I., et al., Deep learning using k-space based data augmentation for automated cardiac MR motion artefact detection. In: MICCAI, pp. 250–258 (2018)
Oksuz, I., et al.: Cardiac MR motion artefact correction from k-space using deep learning-based reconstruction. In: MICCAI-MLMIR (2018)
Oksuz, I., et al.: Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. MedIA 55, 250–258 (2018)
Petersen, S.E., et al.: UK Biobank’s cardiovascular magnetic resonance protocol. JCMR 18(1), 1–8 (2016)
Qin, C., et al.: Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE TMI 38(1), 280–290 (2019)
Schlemper, J., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE TMI 37(2), 491–503 (2018)
Tran, D., et al.: Learning spatiotemporal features with 3D convolutional networks. In: ICCV, pp. 4489–4497 (2015)
Xie, J., et al.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
Zhang, K., et al.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE TIP 26(7), 3142–3155 (2017)
Zhu, B., et al.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487 (2018)
Acknowledgments
This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Number 17806. The GPU used in this research was generously donated by the NVIDIA Corporation.
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Oksuz, I. et al. (2019). Detection and Correction of Cardiac MRI Motion Artefacts During Reconstruction from k-space. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_76
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DOI: https://doi.org/10.1007/978-3-030-32251-9_76
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