Table 2.

Quantitative evaluation of $\text{ADC}$ maps generated by DeepADC-Net and alternative state-of-the-art methods on both 4x and 8x accelerated testing datasets. Results are shown as (Mean ± Standard Deviation).

Models	Sampling Factor	Correlation $\left(\times {10}^{-2}\right)$	SSIM $\left(\times {10}^{-2}\right)$	PSNR	NMSE $\left(\times {10}^{-2}\right)$
Least-Squares-Fitting	4x	68.35 ±6.25	96.13 ±1.60	14.98 ±1.69	13.39 ±3.76
Compressed Sensing		72.14 ± 6.87	98.79 ± 0.43	17.24 ± 1.44	7.81 ± 2.13
FBPConvNet		87.28 ±3.66	99.49 ±0.15	21.89 ±1.11	2.45 ±0.34
AttUnet		87.76 ±3.41	99.45 ±0.08	21.73 ±0.84	2.61 ±0.38
DenseUnet		87.68 ±3.48	99.47 ±0.10	21.85 ±0.96	2.47 ±0.30
Unet		87.41 ±3.57	99.48 ±0.11	21.89 ±1.09	2.46 ±0.32
DeepADC-Net		90.91 ±2.28	99.62 ±0.06	23.18 ±0.90	1.82 ±0.19
Least-Squares-Fitting	8x	46.00 ±7.90	84.86 ±4.44	9.60 ±1.53	43.15 ±9.34
Compressed Sensing		57.56 ±22.0	97.36 ±3.44	15.90 ±2.76	11.46 ±10.05
FBPConvNet		76.01 ±5.45	99.02 ±0.18	19.35 ±0.92	4.41 ±0.63
AttUnet		76.76 ±5.49	99.00 ±0.18	19.30 ±0.91	4.52 ±0.79
DenseUnet		76.16 ±5.31	99.03 ±0.19	19.40 ±0.92	4.37 ±0.64
Unet		76.27 ±5.43	99.03 ±0.18	19.39 ±0.92	4.36 ±0.62
DeepADC-Net		85.77 ±3.15	99.37 ±0.09	21.06 ±0.75	2.97 ±0.49