Current status of deep learning in abdominal image reconstruction

Table 2 Abdominal image reconstruction based on generative adversarial network and recurrent neural network

Ref.	Task	Method	Images	Metric
Mardani et al[41], 2017	Compressed sensing automates MRI reconstruction	GANCS	Abdominal MR images	SNR: 20.48; SSIM: 0.87
Yang et al[50], 2018	Low dose CT image denoising	WGAN	Abdominal CT images	PSNR: 23.39; SSIM: 0.79
Kuanar et al[52], 2019	Low-dose abdominal CT image reconstruction	Auto-encoderWGAN	Abdominal CT images	PSNR: 37.76; SSIM: 0.94; RMSE: 0.92
Lv et al[45], 2021	A comparative study of GAN-based fast MRI reconstruction	DAGANKIGANReconGANRefineGAN	T2-weighted liver images; 3D FSE CUBE knee images; T1-weighted brain images	Liver: PSNR: 36.25 ± 3.39; SSIM: 0.95 ± 0.02; RMSE: 2.12 ± 1.54; VIF: 0.93 ± 0.05; FID: 31.94
Zhang et al[53], 2020	3D reconstruction for super-resolution CT images	Conditional GAN	3D-IRCADb-01database liver CT images	Male: PSNR: 34.51; SSIM: 0.90Female: PSNR: 34.75; SSIM: 0.90
Cole et al[49], 2020	Unsupervised MRI reconstruction	UnsupervisedGAN	3D FSE CUBE knee images; DCE abdominal MR images	PSNR: 31.55; NRMSE: 0.23; SSIM: 0.83
Lv et al[48], 2021	Accelerated multichannel MRI reconstruction	PIGAN	3D FSE CUBE knee MR images; abdominal MR images	Abdominal: PSNR: 31.76 ± 3.04; SSIM: 0.86 ± 0.02; NMSE: 1.22 ± 0.97
Zhang et al[54], 2019	4D abdominal and in utero MR imaging	Self-supervised RNN	bSSFP uterus MR images; bSSFP kidney MR images	PSNR: 36.08 ± 1.13; SSIM: 0.96 ± 0.01

RMSE (× 10^-2); NMSE (× 10^-5). MRI: Magnetic resonance imaging; CT: Computed tomography; SNR: Signal-to-noise ratio; PSNR: Peak signal to noise ratio; SSIM: Structural similarity; RMSE: Root mean square error; NRMSE: Normalized root mean square error; VIF: Variance inflation factor; FID: Frechet inception distance; GAN: Generative adversarial network; RNN: Recurrent neural network; PI: Parallel imaging.