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2018 | OriginalPaper | Chapter

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

Authors : Pengyue Zhang, Fusheng Wang, Wei Xu, Yu Li

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

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.

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Title: Multi-channel Generative Adversarial Network for Parallel Magnetic Resonance Image Reconstruction in K-space
Authors: Pengyue Zhang
Fusheng Wang
Wei Xu
Yu Li
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00927-4

Electronic ISBN: 978-3-030-00928-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00928-1_21

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