Synthesizing realistic brain mr images with noise control

Lianrui Zuo, Blake E. Dewey, Aaron Carass, Yufan He, Muhan Shao, Jacob C. Reinhold, Jerry L. Prince

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

Abstract

Image synthesis in magnetic resonance (MR) imaging has been an active area of research for more than ten years. MR image synthesis can be used to create images that were not acquired or replace images that are corrupted by artifacts, which can be of great benefit in automatic image analysis. Although synthetic images have been used with success in many applications, it is quite often true that they do not look like real images. In practice, an expert can usually distinguish synthetic images from real ones. Generative adversarial networks (GANs) have significantly improved the realism of synthetic images. However, we argue that further improvements can be made through the introduction of noise in the synthesis process, which better models the actual imaging process. Accordingly, we propose a novel approach that incorporates randomness into the model in order to better approximate the distribution of real MR images. Results show that the proposed method has comparable accuracy with the state-of-the-art approaches as measured by multiple similarity measurements while also being able to control the noise level in synthetic images. To further demonstrate the superiority of this model, we present results from a human observer study on synthetic images, which shows that our results capture the essential features of real MR images.

Original languageEnglish (US)
Title of host publicationSimulation and Synthesis in Medical Imaging - 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsNinon Burgos, David Svoboda, Jelmer M. Wolterink, Can Zhao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages21-31
Number of pages11
ISBN (Print)9783030595197
DOIs
StatePublished - 2020
Event5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12417 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/4/20

Keywords

  • Deep learning
  • MRI
  • Noise control
  • Randomness
  • Synthesis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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