Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14507))

  • 427 Accesses


Cardiac magnetic resonance imaging (CMR) is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardiac function and anatomy. On the other hand, multi-contrast (T1 and T2) mapping has the potential to assess pathologies and abnormalities in the myocardium and interstitium. However, voluntary breath-holding and often arrhythmia, in combination with MRI’s slow imaging speed, can lead to motion artifacts, hindering real-time acquisition image quality. Although performing accelerated acquisitions can facilitate dynamic imaging, it induces aliasing, causing low reconstructed image quality in Cine MRI and inaccurate T1 and T2 mapping estimation. In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging. We formulate the reconstruction problem as a least squares regularized optimization task, and employ vSHARP, a state-of-the-art Deep Learning-based inverse problem solver, which incorporates half-quadratic variable splitting and the alternating direction method of multipliers (ADMM) with neural networks. We treat the problem in two setups; a 2D reconstruction and a 2D dynamic reconstruction task, and employ 2D and 3D deep learning networks, respectively. Our method optimizes in both the image and k-space domains, allowing for high reconstruction fidelity. Although the target data is undersampled with a Cartesian equispaced scheme, we train our deep neural network using both Cartesian and simulated non-Cartesian undersampling schemes to enhance generalization of the model to unseen data, a key ingredient of our method. Furthermore, our model adopts a deep neural network to learn and refine the sensitivity maps of multi-coil k-space data. Lastly, our method is jointly trained on both, undersampled cine and multi-contrast data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Arai, A.E.: The cardiac magnetic resonance (CMR) approach to assessing myocardial viability. J. Nucl. Cardiol. 18(6), 1095–1102 (2011).

  2. Kim, P.K., et al.: Myocardial T1 and T2 mapping: techniques and clinical applications. Korean J. Radiol. 18(1), 113 (2017).

  3. Larose, E., Rodés-Cabau, J., Delarochelliere, R., Barbeau, G., Noel, B., Bertrand, O.: Cardiovascular magnetic resonance for the clinical cardiologist. Can. J. Cardiol. 23, 84B–88B (2007).

  4. Shannon, C.: Communication in the presence of noise. Proc. IRE 37(1), 10–21 (1949)

    Google Scholar 

  5. Griswold, M.A., et al.: Generalized auto calibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002).

  6. Niendorf, T., Sodickson, D.K.: Parallel imaging in cardiovascular MRI: methods and applications. NMR in Biomed. 19(3), 325–341 (2006).

  7. Geethanath, S., et al.: Compressed sensing MRI: A review. Crit. Rev. Biomed. Eng. 41(3), 183–204 (2013).

  8. Kido, T., et al.: Compressed sensing real-time cine cardiovascular magnetic resonance: accurate assessment of left ventricular function in a single-breath-hold. J. Cardiovasc. Magn. Reson. 18(1), 50 (2016).

  9. Pal, A., Rathi, Y.: A review and experimental evaluation of deep learning methods for MRI reconstruction (2022)

    Google Scholar 

  10. Beauferris, Y., et al.: Multi-coil MRI reconstruction challenge-assessing brain MRI reconstruction models and their generalizability to varying coil configurations. Front. Neurosci. 16, 919186 (2022).

  11. Küstner, T., et al.: CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. In: Scientific Reports, vol. 10, no. 1 (2020).

  12. Sriram, A., et al.: End-to-end variational networks for accelerated MRI reconstruction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 64–73. Springer, Cham (2020).

    Chapter  Google Scholar 

  13. Yiasemis, G., Sonke, J.-J., Sánchez, C., Teuwen, J.: Recurrent variational network: a deep learning inverse problem solver applied to the task of accelerated MRI reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 732–741 (2022)

    Google Scholar 

  14. Hamilton, J.I.: A self-supervised deep learning reconstruction for shortening the breathhold and acquisition window in cardiac magnetic resonance fingerprinting. Front. Cardiovasc. Med. 9, 928546 (2022).

  15. Yiasemis, G., Moriakov, N., Sonke, J.-J., Teuwen, J.: vSHARP: variable splitting half-quadratic admm algorithm for reconstruction of inverse-problems. (2023). arXiv:2309.09954 [eess.IV],

  16. Ye, J.C.: Compressed sensing MRI: a review from signal processing perspective. BMC Biomed. Eng. 1(1), 8 (2019).

  17. Uecker, M., et al.: ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn. Reson. Med. 71(3), 990–1001, (2013).

  18. 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).

    Chapter  Google Scholar 

  19. Li, R., Luo, L., Zhang, Y.: Convolutional neural network combined with half-quadratic splitting method for image restoration. J. Sens. 2020, 1–12 (2020).

  20. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends® Mach. Learn. 3(1), 1–122 (2011)

    Google Scholar 

  21. Yiasemis, G., Sánchez, C.I., Sonke, J.-J., Teuwen, J.: On retrospective k-space subsampling schemes for deep MRI reconstruction. Mag. Reson. Imaging S0730725X23002199 (2024).

  22. Wang, C., et al.: CMR x Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction (2023)

    Google Scholar 

  23. Wang, C., et al.: Recommendation for cardiac magnetic resonance imaging-based phenotypic study: imaging part. Phenomics 1(4), 151–170 (2021).

  24. Paszke, A., et al.: Automatic differentiation in pytorch (2017)

    Google Scholar 

  25. Yiasemis, G., Moriakov, N., Karkalousos, D., Caan, M., Teuwen, J.: Direct: deep image reconstruction toolkit. J. Open Source Softw. 7(73), 4278 (2022).

  26. Zhang, C., Caan, M.W., Navest, R., Teuwen, J., Sonke, J.-J.: Radial-rim: accelerated radial 4D MRI using the recurrent inference machine. In: Proceedings of International Society for Magnetic Resonance in Medicine, vol. 31 (2023)

    Google Scholar 

Download references


This work was funded by an institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport.

Author information

Authors and Affiliations


Corresponding author

Correspondence to George Yiasemis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yiasemis, G., Moriakov, N., Sonke, JJ., Teuwen, J. (2024). Deep Cardiac MRI Reconstruction with ADMM. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. STACOM 2023. Lecture Notes in Computer Science, vol 14507. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52447-9

  • Online ISBN: 978-3-031-52448-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics