Abstract #3638

A deep-learning based synthesized T2 weighted imaging with multi-modality information and k-space correction

Qing Tang¹, Ye Li¹, Hangfei Liu¹, and Tao Zhang^1,2,3

¹School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, ²High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu, China, ³Key Laboratory for Neuro Information, Ministry of Education, Chengdu, China

T2 weighted image (T2WI) usually takes more time and thus is more vulnerable to motion artifacts. With the recent development of applying deep learning to MR imaging, many neural networks are proposed to synthesize high-quality T2 images from under-sampled T2 or other modalities (such as T1). Here we develop a Simple-ResNet network to synthesize high-quality T2 images based on multi-modality information and followed by a k-space correction module. Results show that our model is very easy to train and the synthesized T2 images can achieve comparable image quality as the fully-sampled T2 images.

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