Current status of deep learning in abdominal image reconstruction

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.