Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI
<|reference_start|>Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI: Purpose: 4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.   Methods: Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3x, 6x and 10x) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer, were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D+t) as temporal slices (2D+t). A total of 48 patients with 12332 temporal slices were split into training (37 patients with 10721 slices) and test (11 patients with 1611 slices).   Results: Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15s, 0.16s, and 120s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3x Re-Con-GAN 80.98%; 3x CS 80.74%; 3x UNet 79.88%) of unprocessed under-sampled images (3x 69.61%).   Conclusion: A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.<|reference_end|>
@article{xu2024paired,
    title={Paired Conditional Generative Adversarial Network for Highly Accelerated
  Liver 4D MRI},
    author={Di Xu, Xin Miao, Hengjie Liu, Jessica E. Scholey, Wensha Yang, Mary
  Feng, Michael Ohliger, Hui Lin, Yi Lao, Yang Yang and Ke Sheng},
    journal={arXiv preprint arXiv:2405.12357},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12357},
    primaryClass={eess.IV cs.CV}
}