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.