Abstract
Slice thickness is a very important parameter for medical imaging such as magnetic resonance (MR) imaging or computed tomography (CT). Thinner slice imaging obviously provides higher spatial resolution and more diagnostic information, however also involves higher imaging cost both in time and expense. For the sake of efficiency, a relatively thick slice interval is usually used in the daily routine medical imaging. A novel generative adversarial network was proposed in this paper to reconstruct medical images with thinner slice thickness from regular thick slice images. A fully convolutional network with three-dimensional convolutional kernels and residual blocks was firstly applied to generate the slices between the imaging intervals. A novel perceptual loss function was proposed to guarantee both the pixel similarity and the spatial coherence in 3D. Moreover, a discriminator network with a sustained adversarial loss was utilized to push the solution to be more realistic. 43 pairs of MR images were used to validate the performance of the proposed method. The presented method is able to recover preoperative t2flair MR images with slice thickness of 2 mm from routine t2flair MR images with thickness of 6 mm. The reconstruction results on two datasets show the superiority of the presented method over other competitive image reconstruction methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., et al.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802 (2016)
Acknowledgments
This work was supported by the National Basic Research Program of China (2015CB755500), the National Natural Science Foundation of China (11474071).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, Z., Wang, Y., Yu, J. (2017). Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)