Undersampled Multi Coil Image Reconstruction for fast fMRI Using Adaptive Linear Neurons

نویسندگان

  • T. Grotz
  • B. Zahneisen
  • M. Reisert
  • M. Zaitsev
  • J. Hennig
چکیده

Introduction: Although functional MRI is based on the detection of a rather sparsely distributed signal in the spatial domain, it is nevertheless usually measured by acquiring full resolution EPI data, which therefore limits the temporal resolution that can be achieved. Recently, regularized noncartesian image reconstruction combined with multiple coils was used to demonstrate the feasibility of fMRI in the highly undersampled regime [1,2,3] to increase the temporal resolution by an order of magnitude. There, strongly undersampled fMRI data were acquired and l2-norm Tikhonov regularization was used to perform image reconstruction. Tikhonov regularized reconstruction still leads to strong streaking artefacts in the resulting activation maps. Here we would like to introduce a new approach, based on neural networks, to reconstruct the undersampled fMRI data that offers a significantly improved point spread function (PSF) with reduced spatial spread and hence improved spatial localization of activation.

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تاریخ انتشار 2009