Table 4.

Quantitative comparison of $\text{ADC}$ maps on DeepADC-Net and alternative state-of-the-art methods for 8x accelerated testing datasets on different ROIs. Results are shown as (Mean ± Standard Deviation).

Models	ROIs	Correlation $\left(\times {10}^{-2}\right)$	SSIM $\left(\times {10}^{-2}\right)$	PSNR	NMSE $\left(\times {10}^{-2}\right)$
Least-Squares-Fitting	Tumor	45.64 ±18.5	89.24 ±7.92	10.51 ±3.11	33.84 ±20.72
Compressed Sensing		58.18 ±22.3	97.25 ±5.27	16.14 ±3.89	10.79 ±13.10
FBPConvNet		72.11 ±14.0	99.10 ±0.51	18.99 ±2.52	4.42 ±2.52
AttUnet		72.68 ±14.2	99.06 ±0.53	18.85 ±2.01	4.63 ±2.89
DenseUnet		73.01 ±16.3	99.16 ±0.46	19.17 ±1.97	4.17 ±2.16
Unet		71.94 ±14.0	99.09 ±0.50	19.00 ±1.97	4.42 ±2.52
DeepADC-Net		83.82 ±9.7	99.45 ±0.31	21.08 ±2.00	2.76 ±1.32
Least-Squares-Fitting	Muscle	27.32 ±17.10	75.14 ±9.48	6.24 ±1.84	68.55 ±19.74
Compressed Sensing		49.77 ±20.9	95.81 ±6.84	13.85 ±3.36	14.23 ±16.53
FBPConvNet		59.19 ±17.5	98.92 ±0.48	17.57 ±1.62	5.11 ±1.93
AttUnet		59.64 ±17.2	98.88 ±0.55	17.48 ±1.68	5.27 ±2.43
DenseUnet		58.80 ±17.5	98.91 ±0.21	17.55 ±1.60	5.13 ±1.97
Unet		59.23 ±16.7	98.92 ±0.46	17.55 ±1.58	5.12 ±1.91
DeepADC-Net		80.70 ±7.4	99.41 ±0.30	19.63 ±1.55	2.94 ±1.76
Least-Squares-Fitting	Kidney	34.10 ±16.8	80.11 ±10.11	7.58 ±2.28	56.61 ±22.48
Compressed Sensing		49.99 ±22.5	96.53 ±5.48	14.55 ±3.57	13.34 ±14.81
FBPConvNet		62.29 ±17.7	98.91 ±0.60	17.99 ±1.92	5.16 ±2.61
AttUnet		62.36 ±17.5	98.85 ±0.66	17.84 ±1.92	5.42 ±3.23
DenseUnet		61.99 ±17.8	98.91 ±5.63	18.01 ±1.92	5.13 ±2.47
Unet		62.53 ±17.1	98.91 ±0.57	18.01 ±1.94	5.12 ±2.43
DeepADC-Net		78.15 ±11.5	99.30 ±0.32	19.73 ±1.80	3.43 ±1.69