Current status of deep learning in abdominal image reconstruction

Table 1 Abdominal image reconstruction algorithms based on a convolutional neural network

Ref.	Task	Method	Images	Metric
Kang et al[30], 2017	Low-dose CT reconstruction	CNN	Abdominal CT images	PSNR: 34.55
Chen et al[31], 2017	Low-dose CT reconstruction	RED-CNN	Low-dose abdominal CT images	PSNR: 43.79 ± 2.01; SSIM: 0.98 ± 0.01; RMSE: 0.69 ± 0.07
Han et al[27], 2018	Accelerated projection-reconstruction MRI	U-netCNN	Low-dose abdominal CT images; synthetic radial abdominal MR images	PSNR: 31.55
Lv et al[26], 2018	Undersampled radial free-breathing 3D abdominal MRI	Auto-encoderCNN	3D golden angle-radial SOS liver MR images	P < 0.001
Ge et al[32], 2020	CT image reconstruction directly from a sinogram	Residual encoder-decoder + CNN	Low-dose abdominal CT images	PSNR: 43.15 ± 1.93; SSIM: 0.97 ± 0.01; NRMSE: 0.71 ± 0.16
MacDougall et al[33], 2019	Improving low-dose pediatric abdominal CT	CNN	Liver CT images;Spleen CT images	P < 0.001
Tamada et al[29], 2020	DCE MR imaging of the liver	CNN	T1-weighted liver MR images	SSIM: 0.91
Zhou et al[28], 2019	Applications in low-latency accelerated real-time imaging	PICNN	bSSFP cardiac MR images; bSSFP abdominal MR images	Abdominal: NRMSE: 0.08 ± 0.02; SSIM: 0.90 ± 0.02
Zhang et al[34], 2020	Reconstructing 3D liver vessel morphology from contrasted CT images	GNNCNN	Multi-phase contrasted liver CT images	F1 score: 0.8762 ± 0.0549
Zhou et al[35], 2020	Limited view tomographic reconstruction	Residual dense spatial-channel attention + CNN	Whole body CT images	LAR: PSNR: 35.82; SSIM: 0.97 SVR: PSNR: 41.98; SSIM: 0.97
Kazuo et al[36], 2021	Image reconstructionin low-dose and sparse-view CT	CS + CNN	Low-dose abdominal CT images; Sparse-view abdominal CT images	Low-Dose CT case: PSNR: 33.2; SSIM: 0.91 Sparse-View CT case: PSNR: 29.2; SSIM: 0.91

NRMSE (× 10^-2); RMSE (10^-2). MRI: Magnetic resonance imaging; CT: Computed tomography; CNN: Convolutional neural network; PSNR: Peak signal to noise ratio; SSIM: Structural similarity; RMSE: Root mean square error; NRMSE: Normalized root mean square error; RED: Residual encoder-decoder; DCE: Dynamic contrast-enhanced; PI: Parallel imaging; CS: Compressed sensing; LAR: Limited angle reconstruction; SVR: Sparse view reconstruction; GNN: Graph neural network; RNN: Recurrent neural network; SOS: Stack-of-stars.

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.

Table 3 Applications of deep learning in abdominal reconstruction

Ref.	Task	Method	Images	Metric
Lv et al[55], 2018	Respiratory motion correction for free-breathing 3D abdominal MRI	CNN	3D golden angle-radial SOS abdominal images	SNR: 207.42 ± 96.73
Jiang et al[56], 2019	Respiratory motion correction in abdominal MRI	U-NetGAN	T1-weighted abdominal images	FSE: 0.920; GRE: 0.910; Simulated motion: 0.928
Küstner et al[57], 2020	Motion-corrected image reconstruction in 4D MRI	U-netCNN	T1-weighted in-vivo 4D MR images	EPE: 0.17 ± 0.26; EAE: 7.9 ± 9.9; SSIM: 0.94 ± 0.04; NRMSE: 0.5 ± 0.1
Akagi et al[58], 2019	Improving image quality of abdominal U-HRCT using DLR method	DLR	U-HRCT abdominal CT images	P < 0.01
Nakamura et al[59], 2019	To evaluate the effect of a DLR method	DLR	Abdominal CT images	P < 0.001

NRMSE (× 10^-2). MRI: Magnetic resonance imaging; CT: Computed tomography; CNN: Convolutional neural network; GAN: Generative adversarial network; SNR: Signal-to-noise ratio; SSIM: Structural similarity; NRMSE: Normalized root mean square error; EPE: End-point error; EAE: End-angulation error; U-HRCT: Ultra-high-resolution computed tomography; DLR: Deep learning reconstruction; SOS: Stack-of-stars; FSE: Fast-spin echo; GRE: Gradient echo.