Multi-modal MRI synthesization based on StarGAN

Fuhai，Sun; Tang，Xiaoying

10.1145/3451421.3451426

2020-12-05

ACM International Conference Proceeding Series

19-22

In magnetic resonance image (MRI) analysis, it is often necessary and beneficial to analyze multi-modal MRIs. However, it is typically costly to acquire images of multiple modalities. In this context, cross-modality MRI synthesization has a great potential, for which task the generative adversarial network (GAN) technique has been identified to be useful. The main limitation of GAN is that it can only transfer between two modalities and will not work if more than one modalities need to be generated from another single one. In this work, we propose to use StarGAN for multi-modal MRI synthesization. In other words, StarGAN is used to generate MRIs of multiple modalities from a single modality at one shot. In our experiment, we show that StarGAN is more time-saving than multiple GANs and also more accurate.

generative adversarial network MRI synthesis

第一

英语

EI

20213610870612

Computer applications ; Computer programming

Computer Programming:723.1 ; Computer Applications:723.5 ; Imaging Techniques:746

2-s2.0-85114283067

Scopus

Southern University of Science and Technology,Shenzhen,China

Fuhai，Sun,Tang，Xiaoying. Multi-modal MRI synthesization based on StarGAN[C],2020:19-22.