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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)

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

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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

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