Multi-channel Generative Adversarial Network for Parallel Magnetic Resonance Image Reconstruction in K-space

  • Pengyue ZhangEmail author
  • Fusheng Wang
  • Wei Xu
  • Yu Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11070)


Magnetic Resonance Imaging (MRI) typically collects data below the Nyquist sampling rate for imaging acceleration. To remove aliasing artifacts, we propose a multi-channel deep generative adversarial network (GAN) model for MRI reconstruction. Because multi-channel GAN matches the parallel data acquisition system architecture on a modern MRI scanner, this model can effectively learn intrinsic data correlation associated with MRI hardware from originally-collected multi-channel complex data. By estimating missing data directly with the trained network, images may be generated from undersampled multi-channel raw data, providing an “end-to-end” approach to parallel MRI reconstruction. By experimentally comparing with other methods, it is demonstrated that multi-channel GAN can perform image reconstruction with an affordable computation cost and an imaging acceleration factor higher than the current clinical standard.



This research is supported in part by grants from NIH R01 EB022405, NSF ACI 1443054 and IIS 1350885.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceStony Brook UniversityStony BrookUSA
  2. 2.Computational Science InitiativeBrookhaven National LaboratoryUptonUSA
  3. 3.Department of Cardiac ImagingDeMatteis Center for Cardiac Research and Education, St. Francis HospitalGreenvaleUSA

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