Table 2 List of original emission methods evaluated on clinical PET data

Siegel et al. [122]*

Segmentation of body contour from sinogram data

 ~ 300 (2-[¹⁸F]FDG, [¹⁸F]DOPA, [¹⁵O]H₂O and [¹³N]Ammonia)

Whole body

 < 8.9% (Brain 2-[¹⁸F]FDG), ± 10% (renal flow ([¹³N]Ammonia)

N/A

Chang et al. [145]

Segmentation of body organs from NAC PET images

14 (2-[¹⁸F]FDG)

Thorax

6 ± 7 (lung lesions), 3 ± 6 (bone lesions) ^▪

N/A

Mehranian et al. [142, 304]

MLAA with prior MR spatial and CT statistical information using Gaussian mixture model (MLAA-GMM)

5 (2-[¹⁸F]FDG)

Whole body

−11.6 ± 6.0 (cerebrum),

−3.5 ± 6.6 (lung),

−12.6 ± 8.6 (bone)

Dixon: −18.5 ± 11.3,

Dixon: −5.4 ± 12.0, Dixon: −19.8 ± 8.4

Mehranian et al. [137]

MLAA-GMM including lung segmentation from CT images

17 (2-[¹⁸F]FDG and 2 with [¹⁸F]Choline)

Whole body

−0.8 ± 6.3 (lung),

−5.7 ± 3.2 (myocardium),

−2.0 ± 2.1 (bone)

Dixon: −5.2 ± 7.1, Dixon: −9.5 ± 4.3, Dixon: −14.1 ± 2.5

Benoit et al. [143]

MLAA using UTE and T₁ images as anatomical prior information for updating the attenuation map

204 (2-[¹⁸F]FDG)

Brain

−1.5 ± 3.5

Dixon: −11.8 ± 4.5, UTE: −6.5 ± 2.0

Mehranian et al. [133]

MLAA with spatial constrains from MR images using penalty functions during image reconstruction for updating μ values (P-MLAA⁺) and both μ-map and activity distribution (P-MLAA⁺⁺)

8 (2-[¹⁸F]FDG)

Brain

−3.0 ± 3.5 (P-MLAA⁺), −4.2 ± 3.6 (P-MLAA⁺⁺)^□

Dixon: −13.5 ± 3.1