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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Sparse-View CT Reconstruction Using Wasserstein GANs
verfasst von : Franz Thaler, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern
Erschienen in: Machine Learning for Medical Image Reconstruction
Abstract
We propose a 2D computed tomography (CT) slice image reconstruction method from a limited number of projection images using Wasserstein generative adversarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an \(L_1\) content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two loss functions and compare to a convolutional neural network (CNN) optimized on \(L_1\) and the Filtered Backprojection (FBP) method. The evaluation shows that the results generated by the machine learning based approaches are substantially better than those from the FBP method. In contrast to the blurrier looking images generated by the CNNs trained on \(L_1\), the wGANs results appear sharper and seem to contain more structural information. We show that a certain amount of projection data is needed to get a correct representation of the anatomical correspondences.