Table 3 Mean±STD of our GAN method and the deep learning methods are summarized. CNN-Fuse, FusionGAN, and SESF-Fuse will be excluded from the quantitative comparisons as they did not generated fusion images that contain CT bone structures. Bold indicates the best results. Underline indicate a better result than ours that was excluded because it did not satisfy the fusion criteria

CNN-Fuse

2.06± 0.5

0.24± 0.03

46.5± 6.49

0.51± 0.07

0.08± 0.02

0.36± 0.06

0.9± 0.09*

0.87± 0.56

0.66± 0.31

SESF-Fuse

2.06± 0.51

0.23± 0.03

43.04 ± 8.41

0.46± 0.04

0.08± 0.02

0.38± 0.06

0.7± 0.15

0.47± 0.33*

0.58± 0.25*

SwinFusion

2.05± 0.46

0.28± 0.03

22.65± 1.78*

0.6± 0.04

0.07± 0.02

0.34± 0.06

0.89± 0.04

0.23± 0.09

0.67± 0.16

IFCNN

2.11± 0.48

0.24± 0.03

21.54± 1.96

0.6± 0.05

0.08± 0.02

0.36± 0.06

0.9± 0.04

0.03± 0.05

0.78\(\varvec{\pm }\)0.04

U2Fusion

2.14± 0.49

0.18± 0.03

18.11± 0.95

0.54± 0.07

0.06± 0.02

0.27± 0.05

0.91\(\varvec{\pm }\)0.04*

0.24± 0.07

0.15± 0.06

DSAGAN

2.19± 0.39

0.27± 0.01

28.5\(\varvec{\pm }\)3.33

0.4± 0.05

0.13± 0.02

0.58± 0.07

0.86± 0.07

0.66\(\varvec{\pm }\)0.54

0.11± 0.05

CU-Net

2.83 ± 0.55

0.19 ± 0.02

21.07 ± 1.84

0.31 ± 0.04

0.04 ± 0.01

0.17 ± 0.03

0.88 ± 0.04

0.29 ± 0.07

0.4 ± 0.09

FusionGAN

1.64± 0.3

0.21± 0.03

25.89± 2.88

0.35± 0.03

0.1± 0.03

0.31± 0.03

0.69± 0.16

1.0± 0.14

0.59± 0.18

Ours

5.2\(\varvec{\pm }\)0.38

0.44\(\varvec{\pm }\)0.05

23.02\(\pm\)3.5

0.64\(\varvec{\pm }\)0.1

0.20\(\varvec{\pm }\)0.05

0.67\(\varvec{\pm }\)0.14

0.91\(\varvec{\pm }\)0.04

0.42\(\pm\)0.29

0.62\(\pm\)0.22