Table 4 List of original deep learning methods evaluated on clinical PET data

Ribeiro et al. [233]

Generation of template-based μ-maps [168] from UTE images using a three-layer network

4/N.A/N.A

(2-[¹⁸F]FDG)

Brain

3.4

N/A

Bradshaw et al. [269]

Generation of 4-class probability maps from T₁ and LAVA Flex images using the 3D DeepMedic network

12/6/N.A

(2-[¹⁸F]FDG)

Pelvis

 − 1.0 ± 1.3 (lesions)

Dixon: 0.0 ± 6.4

Gong et al. [214]

Generation of pseudo-CTs from Dixon and ZTE images using a 2D U-Net with group convolutional modules

32/8/N.A

(2-[¹⁸F]FDG)

Brain

 ~ 2.0 ± 0.5 ^

*Dixon_SB: ~ 5.5 ± 1.5

ZTE: ~ 4 ± 1.3

Jang et al. [231]

Generation of 3-class from rapidly acquired UTE images using a 2D VGG16 (encoder) and SegNet (decoder)

30 T1 + 6 UTE (transfer learning)/ N.A/ 8

(2-[¹⁸F]FDG)

Brain

0.2 ± 1

N/A

Liu et al. [229]

Generation of 3-class pseudo-CTs from AC PET 2-[¹⁸F]FDG images using a 2D VGG16

100/28/N.A

(2-[¹⁸F]FDG)

Brain

−0.6 ± 2.0 ^□

N/A

Liu et al. [114]

Same as [229] but using a 13-layer VGG16 with T₁ images as input

100/28/N.A

(2-[¹⁸F]FDG)

Brain

−0.7 ± 1.1 ^□

Dixon: −5.8 ± 3.1

Arabi et al. [38]

Generation of 4-class μ-maps from T₁ images using the 3D adversarial semantic structure

15/15/N.A

(2-[¹⁸F]FDG)

Brain

3.2 ± 3.4 (whole head) ^□

N/A

Blanc-Durand et al. [75]

Generation of pseudo-CTs from ZTE images using a 3D U-Net algorithm

50/43/N.A

(2-[¹⁸F]FDG)

Brain

−0.2 ± 5.6 ^□

ZTE:2.5

Dong et al. [247]

Generation of pseudo-CTs from NAC PET images using a 3D cycleGAN

80/39/N.A

(2-[¹⁸F]FDG)

Whole body

 − 1.1 ± 3.9 (Brain), 10.7 ± 7.7 (Lung), 0.7 ± 8.4 (Heart)

N/A

Hwang et al. [253]

Generation of CT-based μ-maps from MLAA images using a 2D U-Net

60/20/20

(2-[¹⁸F]FDG)

Whole body

 − 2.2 ± 1.78 (bone lesions)

1.3 ± 3.3 (soft-tissue lesions)

Dixon: −9.4 ± 5.2

Dixon: −2.9 ± 1.2

Ladefoged et al. [212]

Generation of pseudo-CTs for paediatric data from UTE, echo images and the R₂^* map using a 3D U-Net

60/19/28 ([¹⁸F]FET)

Brain

−0.1

N/A

Shiri et al. [220]

Generation of AC from NAC PET 2-[¹⁸F]FDG images using a 2D U-Net algorithm

91/20/18

Brain

 − 0.1 ± 2.14

N/A

Spuhler et al. [226]**

Generation of pseudo-transmission data from a spoiled gradient recalled acquisition using a 2D U-Net

66/11/11 ([¹¹C]WAY-100635) + 10 ([¹¹C]DASB)

Brain

 − 0.5 ± 1.7 ([¹¹C]WAY-100635)

 − 1.5 ± 0.7 ([¹¹C]DASB) ^□

N/A

Torrado-Carvajal et al. [241]

Generation of pseudo-CTs from Dixon images as input to a 2D U-Net algorithm

15/4/N.A

(2-[¹⁸F]FDG)

Pelvis

0.3 ± 2.6 (Fat),

 − 0.0 ± 3.0 (soft tissue),

 − 0.9 ± 5.1 (bone)

Dixon: 1.5 ± 6.5

Dixon: −0.3 ± 10.0

Dixon: −25.1 ± 12.7

Arabi et al. [237]

Generation of CT-derived pseudo-μ-maps from PET TOF sinogram data using the 2D HighRes framework

52/16/N.A

(2-[¹⁸F]FDG)

Brain

2.9 ± 3.1 (head), 2.0 ± 10.6 (soft tissue), 1.2 ± 10.2 (bone)

N/A

Armanious et al. [259]

Generation of pseudo-CT from NAC PET 2-[¹⁸F]FDG images using a 2D GAN framework

100/N.A/25

(2-[¹⁸F]FDG)

Whole body

 − 5.6 ± 7.2 (left lung), − 3 ± 11.7 (right lung)

N/A

Dong et al. [248]

Generation of AC from NAC PET images using a 2D cycleGAN framework

25/1 (leave-one-out)/30

(2-[¹⁸F]FDG)

Whole body

 − 17.0 ± 12.0 (lung), 2.1 ± 2.5 (heart), 2.8 ± 5.2 (lesions) ^□

N/A

Hu et al. [258]

Generation of pseudo-CT and AC from NAC PET images using a 2D Wasserstein GAN

40/5/N.A

(2-[¹⁸F]FDG)

Whole Body

6.4 ± 3.8 (brain), 4.4 ± 3.2 (heart), 4.3 ± 5.4 (lung) ^□

N/A

Ladefoged et al. [215]

Similar to [212] but using DIXON images as input

403 + 5 (transfer learning)/207/104

(2-[¹⁸F]FDG)

Brain

 ~ −0.3

Dixon_SB: 0.8 ± 2.4

Anaya et al. [303]***

Generation of pseudo-CT from Dixon water images using the (2D) pix2pix framework

9/2/1

(N/A)

Head & neck

2.1^{^}

N/A

Chen et al. [210]

Generation of pseudo-CTs from R1 images as derived from UTE using a residual 3D U-Net

72/18/84

([¹⁸F]Florbetapir)

Brain

0.1 ± 0.6 ^□

N/A

Choi et al. [225]

Generation of tracer-specific pseudo-μ-map from MLAA images using a 3D U-Net

60/20/20

(2-[¹⁸F]FDG)

Brain

 < 5

N/A

Gong et al. [217]

Same as [214] but using images from a UTE/multi-echo sequence as input

30/5/N.A ([¹¹C]PiB, [¹⁸F]MK6240)

Brain

 < 2

N/A

Gong et al. [228]

Generation of the pseudo-CTs from Dixon images using a 3D cycleGAN

28/4/N.A

([¹⁸F]FDG)

Brain

 ~ 3

Dixon: ~ 8

Hashimoto et al. [41]*

Generation of pseudo-CTs from NAC PET images using a 2D U-Net algorithm and mixed tracer training data

1091/N.A/70

(6 tracers)

Brain

 − 5.7 ± 5.0 (2-[¹⁸F]FDG)

N/A

Kläser et al. [236]

Pseudo-CT generation from T₁ & T₂ images using HighRes3DNET and imitation learning

16/4/23

(2-[¹⁸F]FDG)

Brain

4.04 ± 0.5 ^^□

N/A

Pozaruk et al. [243]

Generation of pseudo-CTs from Dixon images using a 2D cycleGAN framework

18 /N.A/10

([⁶⁸ Ga]Ga-PSMA)

Pelvis

2.2 ^^□

Dixon: 10.3, Dixon_SB: 8.7

Shiri et al. [305]

Generation of tracer and sight-specific AC from NAC PET images using a 2D U-Net and transfer learning techniques

1110 (2-[¹⁸F]FDG) & 855 ([⁶⁸ Ga]Ga-PSMA)/N.A./95 ([⁶⁸ Ga]Ga-PSMA)

Whole body

2.72 ± 7.5

N/A

Ahangari et al. [242]

Generation of pseudo-CT from Dixon images using a 3D U-Net

11 (transfer learning)/N.A./15

Whole body

2.1 ± 2.4 (Brain),

 − 4.9 ± 12.1 (lung),

 − 4.0 ± 6.5 (bone)

Dixon_SB: 2.1 ± 3.2,

Dixon_SB: −4.3 ± 20.3,

Dixon_SB: −7 ± 12.4

Arabi et al. [271]

Pseudo-CT generation from in-phase Dixon images using the 2D HighResNet

20/5/N.A

(2-[¹⁸F]FDG)

Whole body

 − 3.7 ± 5.5 (lung), 1.1 ± 3.1 (bone), 2.1 ± 3.7 (cerebellum)

N/A

Hwang et al. [306]

Generation of tracer-specific pseudo-μ-map from MLAA images using a 3D U-Net

60/20/20 (2-[¹⁸F]FDG)

Whole body

1.2 ± 5.7 (lung lesions),

0.2 ± 3.8 (bone lesions)

N/A

Olin et al. [211]

Generation of pseudo-CTs from Dixon images using a 3D U-Net

800 heads + 17 head & neck (transfer learning)/leave-one-out/10 (2-[¹⁸F]FDG)

Head & neck

 − 0.6 ± 2.0 (lesions)

Dixon_SB: −3.5 ± 4.6

Sari et al. [289]

Air pocket segmentation from Dixon images using a 3D U-Net followed by generation of pseudo-CTs from Dixon images using a second 3D U-Net

30/5/N.A

(2-[¹⁸F]FDG)

Pelvis

2.6

Dixon_SB: 5.1

Toyonaga et al. [255]

Generation of tracer-specific pseudo-μ-map from MLAA images using a 3D U-Net

40/22/73 (2-[¹⁸F]FDG)

40/22/36 ([⁶⁸ Ga]DOTATATE)

40/22/50 ([¹⁸F]Fluciclovine)

Whole body

Thorax: − 1.5 ± 2.3

(2-[¹⁸F]FDG), 2.2 ± 2.3 ([¹⁸F]Dotatate), − 2.9 ± 1.8 ([¹⁸F]Fluciclovine)

N/A

Wang et al. [307]

Generation of AC from NAC PET images using a 2D U-Net with deformable transformer layers

21/4/5

(¹³N-Ammonia)

Thorax

10.1 ± 2.9 (myocardium)

N/A

Shiri et al. [275]

Training of a “global” model for multi-centre trials, by feeding sight-specific trained models to it. AC from NAC PET images using a 2D U-Net are generated

180/60/60

(2-[¹⁸F]FDG)

Whole body

-0.1 ± 0.1

N/A