Sparse-view CT Reconstruction using Wasserstein GANs

Franz Thaler*, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 L1 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 L1 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 L1, 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.

Original languageEnglish
Title of host publicationMachine Learning for Medical Image Reconstruction - First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Proceedings
PublisherSpringer Verlag Heidelberg
Pages75-82
Number of pages8
Volume11074 LNCS
ISBN (Print)9783030001285
DOIs
Publication statusPublished - 16 Sep 2018
Event1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201816 Sep 2018

Publication series

NameLecture Notes in Computer Science
Volume11074
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period16/09/1816/09/18

Keywords

  • Computed tomography
  • Convolutional neural networks
  • Generative adversarial networks
  • L1 loss
  • Sparse-view reconstruction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing

Cooperations

  • BioTechMed-Graz

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