Multimodal MR Image Synthesis Using Gradient Prior and Adversarial Learning

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Multimodal MR Image Synthesis Using Gradient Prior and Adversarial Learning

Xiaoming Liu; Aihui Yu; Xiangkai Wei; Zhifang Pan; Jinshan Tang

In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis. However, the images under specific imaging settings may not be available due to the limitation of scanning time or corruption caused by noises. It is attractive to derive missing images with some settings from the available MR images. In this paper, we propose a novel end-to-end multisetting MR image synthesis method. The proposed method is based on generative adversarial networks (GANs) - a deep learning model. In the proposed method, different MR images obtained by different settings are used as the inputs of a GANs and each image is encoded by an encoder. Each encoder includes a refinement structure which is used to extract a multiscale feature map from an input image. The multiscale feature maps from different input images are then fused to generate several desired target images under specific settings. Because the resultant images obtained with GANs have blurred edges, we fuse gradient prior information in the model to protect high frequency information such as important tissue textures of medical images. In the proposed model, the multiscale information is also adopted in the adversarial learning (not just in the generator or discriminator) so that we can produce high quality synthesized images. We evaluated the proposed method on two public datasets: BRATS and ISLES. Experimental results demonstrate that the proposed approach is superior to current state-of-the-art methods.

SPS on Twitter

  • DEADLINE EXTENDED: There's still time to submit your proposal to host the 2023 IEEE International Symposium on Biom…
  • The 35th Picture Coding Symposium is heading to Bristol, UK and is now accepting papers for their June event! Head…
  • Deadline to submit to has been extended to 25 January!
  • CALL FOR PAPERS: The 2021 IEEE International Conference on Autonomous Systems is now accepting papers for their Aug…
  • The Brain Space Initiative Talk Series continues Friday, 15 January at 11:00 AM EST when Dr. Eva Dyer presents "Rep…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar