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

TL;DR

This paper introduces a novel GAN-based super-resolution method with attention and cyclic loss to enhance low-resolution pelvic MRI images, improving detail restoration and clinical diagnostic potential.

Contribution

The study presents a new GAN model with cyclic loss and attention mechanisms specifically designed for high-quality MRI super-resolution, outperforming existing methods.

Findings

Our method achieves superior structural similarity and PSNR compared to BICUBIC, SRCNN, SRGAN, and EDSR.

Enhanced MRI resolution improves lesion texture visualization in tumor patients.

The approach demonstrates potential for clinical diagnosis enhancement.

Abstract

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 (GAN) with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by a factor of 2. 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.