Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network

  • Sahar YousefiEmail author
  • Lydiane Hirschler
  • Merlijn van der Plas
  • Mohamed S. Elmahdy
  • Hessam Sokooti
  • Matthias Van Osch
  • Marius Staring
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)


Hadamard time-encoded pseudo-continuous arterial spin labeling (te-pCASL) is a signal-to-noise ratio (SNR)-efficient MRI technique for acquiring dynamic pCASL signals that encodes the temporal information into the labeling according to a Hadamard matrix. In the decoding step, the contribution of each sub-bolus can be isolated resulting in dynamic perfusion scans. When acquiring te-ASL both with and without flow-crushing, the ASL-signal in the arteries can be isolated resulting in 4D-angiographic information. However, obtaining multi-timepoint perfusion and angiographic data requires two acquisitions. In this study, we propose a 3D Dense-Unet convolutional neural network with a multi-level loss function for reconstructing multi-timepoint perfusion and angiographic information from an interleaved \(50\%\)-sampled crushed and \(50\%\)-sampled non-crushed data, thereby negating the additional scan time. We present a framework to generate dynamic pCASL training and validation data, based on models of the intravascular and extravascular te-pCASL signals. The proposed network achieved SSIM values of \(97.3 \pm 1.1\) and \(96.2 \pm 11.1\) respectively for 4D perfusion and angiographic data reconstruction for 313 test data-sets.


Pseudo-continuous arterial spin labeling (pCASL) Hadamard time-encoded ASL Convolutional neural network (CNN) 4D magnetic resonance angiography (MRA) 4D perfusion MRI reconstruction 



This work is financed by the Netherlands Organization for Scientific Research (NWO), VICI project 016.160.351.


  1. 1.
    Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), 359–386 (2015)CrossRefGoogle Scholar
  2. 2.
    van Osch, M.J.P., Teeuwisse, W.M., Chen, Z., Suzuki, Y., Helle, M., Schmid, S.: Advances in arterial spin labelling MRI methods for measuring perfusion and collateral flow. J. Cereb. Blood Flow Metab. 38(9), 1461–1480 (2018)CrossRefGoogle Scholar
  3. 3.
    Petersen, E.T., Mouridsen, K., Golay, X.: The QUASAR reproducibility study, part II: results from a multi-center arterial spin labeling test-retest study. Neuroimage 49(1), 104–113 (2010)CrossRefGoogle Scholar
  4. 4.
    Yousefi, S., et al.: Esophageal gross tumor volume segmentation using a 3D convolutional neural network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 343–351. Springer, Cham (2018). Scholar
  5. 5.
    Elmahdy, M.S., et al.: Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer. Med. Phys. 46, 3329–3343 (2019)Google Scholar
  6. 6.
    Gong, K., et al.: Iterative pet image reconstruction using convolutional neural network representation. TMI 38(3), 675–685 (2018)Google Scholar
  7. 7.
    Gong, E., Pauly, J., Zaharchuk, G.: Boosting SNR and/or resolution of arterial spin label (ASL) imaging using multi-contrast approaches with multi-lateral guided filter and deep networks. In: Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii (2017)Google Scholar
  8. 8.
    Guo, J., Gong, E., Goubran, M., Fan, A., Khalighi, M., Zaharchuk, G.: Improving perfusion image quality and quantification accuracy using multi-contrast MRI and deep convolutional neural networks. In: ISMRM, Paris, France (2018)Google Scholar
  9. 9.
    Ho, K.C., Scalzo, F., Sarma, K.V., El-Saden, S., Arnold, C.W.: A temporal deep learning approach for MR perfusion parameter estimation in stroke. In: 23rd ICPR, pp. 1315–1320. IEEE (2016)Google Scholar
  10. 10.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700–4708 (2017)Google Scholar
  11. 11.
    Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: CVPR, pp. 11–19 (2017)Google Scholar
  12. 12.
    Zhao, L., Fielden, S.W., Feng, X., Wintermark, M., Mugler III, J.P., Meyer, C.H.: Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction. Neuroimage 121, 205–216 (2015)CrossRefGoogle Scholar
  13. 13.
    Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. TPAMI 38(2), 295–307 (2015)CrossRefGoogle Scholar
  14. 14.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). Scholar
  15. 15.
    Buxton, R.B., Frank, L.R., Wong, E.C., Siewert, B., Warach, S., Edelman, R.R.: A general kinetic model for quantitative perfusion imaging with arterial spin labeling. MRM 40(3), 383–396 (1998)CrossRefGoogle Scholar
  16. 16.
    Cocosco, C.A., Kollokian, V., Kwan, R.K.-S., Pike, G.B., Evans, A.C.: Brainweb: online interface to a 3D MRI simulated brain database. NeuroImage 5, 425 (1997)Google Scholar
  17. 17.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. TMI 29(1), 196–205 (2010)Google Scholar
  18. 18.
    Hirschler, L., et al.: Transit time mapping in the mouse brain using time-encoded pCASL. NMR Biomed. 31(2), e3855 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sahar Yousefi
    • 1
    Email author
  • Lydiane Hirschler
    • 1
  • Merlijn van der Plas
    • 1
  • Mohamed S. Elmahdy
    • 1
  • Hessam Sokooti
    • 1
  • Matthias Van Osch
    • 1
  • Marius Staring
    • 1
    • 2
  1. 1.Leiden University Medical Center, RadiologyLeidenThe Netherlands
  2. 2.Intelligent Systems DepartmentDelft University of TechnologyDelftThe Netherlands

Personalised recommendations