  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.
