Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI

Motivation: Densely sampled k-space leads to high-quality MR but can be impractical due to lengthy scanning time. Accelerating MR acquisition by reducing sampling density can decrease image quality and/or increase reconstruction complexity and time. Goal(s): This work aims to design an algorithm for efficient and high-quality reconstruction of highly accelerated radial-sampling liver 4D MR. Approach: We proposed a novel Paired Conditional generative adversarial network term Re-Con-GAN, evaluated on a 4D liver MR dataset at 3x, 6x, and 10x acceleration ratios. Results: Re-Con-GAN achieved better PSNR, SSIM, and RMSE with sub-second inference speed (0.15s) than compressed sensing (120s) and non-GAN deep learning methods (0.15s-0.73s). Impact: A robust and efficient framework, Re-Con-GAN, is proposed in the current work with sub-second inference speed (0.15s) and promising reconstruction results demonstrated on an in-house curated 4D liver MRI dataset.