Table 3 List of original atlas methods evaluated on clinical PET data

Kops et al. [166]*

Generation of pseudo-transmission images by bringing the averaged database images to the patients’ space

4 (2-[¹⁸F]FDG)

Brain

9

N/A

Hofmann et al. [188]

Generation of pseudo-CTs using pattern recognition to find similar patches between a brain MR-CT pair database and target MRI

3 (N/A)

Brain

3.2 ± 2.5 (VOIs in brain)

N/A

Malone et al. [167]*

Generation of pseudo-transmission images by registering a brain MR-transmission maps database to the patients’ T₁

10 (2-[¹⁸F]FDG)

Brain

−1.2 ± 1.2

N/A

Wollenweber et al. [170]

Pseudo-CT generation by registration of patient’s T₁ image to a CT database

13 ([¹⁸F]FDG)

Brain

0.2 ± 2.6 (VOIs in brain)

N/A

Arabi et al. [190]

Adaptation of [188] using voxel-wise local normalised cross-correlation

14 (2-[¹⁸F]FDG)

Whole Body

 ~ 5 ± 4 (cerebellum),

 ~ 4 ± 10 (lung)

N/A

Burgos et al. [60]

Generation of pseudo-CTs by registering a MR-CT pair database to patients’ T₁ image a using local image similarity measure

41 (2-[¹⁸F]FDG)

Brain

0.2 ± 2.1

UTE: −11.8 ± 2.1

Izquierdo-Garcia et al. [172]

Generation of 6-class probability maps from patients’ T₁ image and coregistration to a similarly created probability map database with paired CT images. Averaged CT is brought back to patients’ space

15 (2-[¹⁸F]FDG or [¹⁸F]FET)

Brain

−1.0 ± 2.5

Dixon: 13.0 ± 10.2

Poynton et al. [173]

Registration of 3-class probability maps from CT data-dUTE-T₁ database to patients’ corresponding MR images to generate a tissue probability map

13

(N.A.)

Brain

1.8 ± 2.4^

N/A

Chen et al. [181]

Generation of pseudo-CTs by registering a MR-CT pair database to patients’ T₁ image. Air segmentation is performed using an air probabilistic map and sparse regression to identify similar patches between atlas and patients’ MR

20 ([¹⁸F]Florbetapir)

Brain

2.4 ± 1^

Dixon: 12.7 ± 2.2^

Burgos et al. [177]

Use of convolution-based local normalised correlation coefficient to irregular regions to account for mismatches in the FOV between CT and MRI and extension of the database to include both T₁ and T₂ images

15 (2-[¹⁸F]FDG or [¹⁸F]Florbetapir)

Brain

1.6 ± 0.6

(2-[¹⁸F]FDG),

1.9 ± 0.6 ([¹⁸F]Florbetapir) ^□

N/A

Arabi et al. [186]

Pseudo-CT generation by registering an averaged atlas image to the patients’ T₁ and use the precomputed transformation matrices to bring all atlas images to the patients’ space

41 (2-[¹⁸F]FDG)

Whole body

3.5 ± 6.1 (cerebellum),

3.2 ± 9.8 (lung),

−4.9 ± 5.9 (bone)

N/A

Teuho et al. [174]

Assignment of μ values on a tissue probability map generated from T₁ images in SPM

7 (2-[¹⁸F]FDG)

Brain

−0.3 ± 2.7 ^□

UTE: −7 ± 2.3^□

Wallstén et al. [187]

Statistical decomposition algorithm to generate pseudo-CTs from the patients’ T₂ images

15 ([¹¹C]acetate)

Pelvis

−2.3(prostate lesion)

−4.2 ± 5.7 (bone)

Dixon: −5.6

Dixon: −17.7 ± 8.4

Sousa et al. [165]*

A database of paired of ⁶⁸Ge-transmission AC maps-T1 images registered to the patients’ T₁ image and averaging all transmission AC maps

9 ([¹¹C]PE2I)

Brain

−0.1 ± 3.2

N/A