Abstract
First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.
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References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org
Aggarwal, H.K., Mani, M.P., Jacob, M.: MoDL: model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imaging 38(2), 394–405 (2018)
Chollet, F., et al.: Keras (2015). https://keras.io
Correia, T., Schneider, T., Chiribiri, A.: Model-based reconstruction for highly accelerated first-pass perfusion cardiac MRI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 514–522. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_57
Do, W.J., Seo, S., Han, Y., Ye, J.C., Choi, S.H., Park, S.H.: Reconstruction of multicontrast MR images through deep learning. Med. Phys. 47(3), 983–997 (2020)
Foley, J.R.J.: Cardiovascular magnetic resonance imaging for the investigation of ischaemic heart disease. Ph.D. thesis, University of Leeds (2018)
Hendel, R.C., et al.: CMR first-pass perfusion for suspected inducible myocardial ischemia. JACC Cardiovasc. Imaging 9(11), 1338–1348 (2016)
Heo, R., Nakazato, R., Kalra, D., Min, J.K.: Noninvasive imaging in coronary artery disease. In: Seminars in Nuclear Medicine, vol. 44, pp. 398–409. Elsevier (2014)
Hsu, L.Y., et al.: Diagnostic performance of fully automated pixel-wise quantitative myocardial perfusion imaging by cardiovascular magnetic resonance. JACC Cardiovasc. Imag. 11(5), 697–707 (2018)
Huang, Q., Yang, D., Wu, P., Qu, H., Yi, J., Metaxas, D.: MRI reconstruction via cascaded channel-wise attention network. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1622–1626. IEEE (2019)
Kellman, P., et al.: Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification. J. Cardiovasc. Magn. Reson. 19(1), 1–14 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kocanaogullari, D., Eksioglu, E.M.: Deep learning for MRI reconstruction using a novel projection based cascaded network. In: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2019)
Lingala, S.G., Hu, Y., DiBella, E., Jacob, M.: Accelerated dynamic MRI exploiting sparsity and low-rank structure: KT SLR. IEEE Trans. Med. Imaging 30(5), 1042–1054 (2011)
Liu, F., Kijowski, R., El Fakhri, G., Feng, L.: Magnetic resonance parameter mapping using model-guided self-supervised deep learning. Magn. Reson. Med. 85, 3211–3226 (2021)
Otazo, R., Kim, D., Axel, L., Sodickson, D.K.: Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn. Reson. Med. 64(3), 767–776 (2010)
Patlak, C.S., Blasberg, R.G., Fenstermacher, J.D.: Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J. Cereb. Blood Flow Metab. 3(1), 1–7 (1983)
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_28
Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491–503 (2017)
Schwitter, J., et al.: MR-IMPACT II: magnetic resonance imaging for myocardial perfusion assessment in coronary artery disease trial: perfusion-cardiac magnetic resonance vs. single-photon emission computed tomography for the detection of coronary artery disease: a comparative multicentre, multivendor trial. Eur. Heart J. 34(10), 775–781 (2013)
Vitanis, V., et al.: High resolution three-dimensional cardiac perfusion imaging using compartment-based K-T principal component analysis. Magn. Reson. Med. 65(2), 575–587 (2011)
Yaman, B., et al.: Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn. Reson. Med. 84(6), 3172–3191 (2020)
Acknowledgements
This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 867450. Authors also thank European Social Fund, Operational Programme of Castilla y León, and the Junta de Castilla y León. This work has also been supported by Agencia Estatal de Investigación through grant TEC2017-82408-R and by Fundação para a Ciência e Tecnologia (FCT) through grant UIDP/50009/2020.
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Martín-González, E. et al. (2021). Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI. In: Haq, N., Johnson, P., Maier, A., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2021. Lecture Notes in Computer Science(), vol 12964. Springer, Cham. https://doi.org/10.1007/978-3-030-88552-6_9
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