Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images

Pan, Kai1; Cheng, Pujin1; Huang, Ziqi1; Lin, Li2; Tang, Xiaoying1

10.1109/EMBC48229.2022.9871183

2022

44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC)

2375-7477

978-1-7281-2783-5

44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC)

5062-5065

11-15 July 2022

Glasgow, Scotland, United Kingdom

Multi-modality magnetic resonance (MR) images provide complementary information for disease diagnoses. However, modality missing is quite usual in real-life clinical practice. Current methods usually employ convolution-based generative adversarial network (GAN) or its variants to synthesize the missing modality. With the development of vision transformer, we explore its application in the MRI modality synthesis task in this work. We propose a novel supervised deep learning method for synthesizing a missing modality, making use of a transformer-based encoder. Specifically, a model is trained for translating 2D MR images from T1-weighted to T2-weighted based on conditional GAN (cGAN). We replace the encoder with transformer and input adjacent slices to enrich spatial prior knowledge. Experimental results on a private dataset and a public dataset demonstrate that our proposed model outperforms state-of-the-art supervised methods for MR image synthesis, both quantitatively and qualitatively. Clinical relevance— This work proposes a method to synthesize T2-weighted images from T1-weighted ones to address the modality missing issue in MRI.

Deep Learning Image Synthesis Magnetic Resonance Imaging Biological System Modeling Magnetic Resonance Transformers Generative Adversarial Networks

第一 ; 通讯

英语

CPCI-S ; EI

IEEE

1.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
2.Department of Electronic and Electrical Engineering, The University of Hong Kong, Hong Kong, China

Pan, Kai,Cheng, Pujin,Huang, Ziqi,et al. Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images[C],2022:5062-5065.