High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network With Attention and Cyclic Loss

IEEE Access

Guang Yang¹  Chengyan Wang²  Guangyuan Li³  Xiangrong Tong³  Jun Lv³ 

[1] Cardiovascular Research Centre, Royal Brompton Hospital, London, U.K;Human Phenome Institute, Fudan University, Shanghai, China;School of Computer and Control Engineering, Yantai University, Yantai, China;

关键词: Super-resolution reconstruction;    pelvic;    generative adversarial network;    cyclic loss;    attention;   

DOI  :  10.1109/ACCESS.2021.3099695

来源: DOAJ

【 摘 要 】

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. Therefore, we proposed a novel super-resolution method that uses a generative adversarial network with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by upsampling factors of $2\times $ and $4\times $ . We implemented our model on pelvic images from healthy subjects as training and validation data, while those data from patients were used for testing. The MR dataset was obtained using different imaging sequences, including T2, T2W SPAIR, and mDIXON-W. Four methods, i.e., BICUBIC, SRCNN, SRGAN, and EDSR were used for comparison. Structural similarity, peak signal to noise ratio, root mean square error, and variance inflation factor were used as calculation indicators to evaluate the performances of the proposed method. Various experimental results showed that our method can better restore the details of the high-resolution MR image as compared to the other methods. In addition, the reconstructed high-resolution MR image can provide better lesion textures in the tumor patients, which is promising to be used in clinical diagnosis.

【 授权许可】

Unknown