Lumbar Spine CT synthesis from MR images using CycleGAN: A preliminary study

Mariusz Bajger, Minh Son To, Gobert Lee, Adam Wells, Chee Chong, Marc Agzarian, Santosh Poonnoose

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this paper, we investigate the generation of Lumbar Spine synthetic CT (sCT) images based on MR images for MR-only spinal cord injury treatment and surgery planning. CT and MRI provide complementary information and are both important for spine treatment and surgery planning. However, the acquisition of images of two different modalities interrupts the clinical workflow, adds to health care cost and poses challenges in registering the images for analysis. Translating MR images to CT images would result in seamless correlation between images and also save patients from exposure to ionizing radiation due to a CT examination. Using a large clinical dataset of 800 patients, we showed that a cycle consistent generative adversarial network (CycleGAN) can be trained with the unpaired, unaligned MR and CT images of the Lumbar Spine in generating realistic synthetic CT images. The trained model was evaluated using the paired MR and CT images of 8 patients, the average Mean Absolute Error (MAE) was found to be 184 HU with a standard deviation of 24 HU.

Original languageEnglish
Title of host publication2021 Digital Image Computing
Subtitle of host publicationTechniques and Applications (DICTA)
EditorsJun Zhou, Olivier Salvado, Ferdous Sohel, Paulo Borges, Shilin Wang
Place of PublicationNew York, USA
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781665417099, 9781665417082
DOIs
Publication statusPublished - 2021
Event2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021 - Gold Coast, Australia
Duration: 29 Nov 20211 Dec 2021

Conference

Conference2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021
Country/TerritoryAustralia
CityGold Coast
Period29/11/211/12/21

Keywords

  • CT
  • CycleGAN
  • Deep Learning
  • GAN
  • Generative Adversarial Networks
  • MR

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