Scientific article
OA Policy
English

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET

Published inEJNMMI physics, vol. 11, no. 1, 66
Publication date2024-07-19
First online date2024-07-19
Abstract

Background

Low-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.

Methods

Clinical uEXPLORER total-body PET/CT datasets of [ 18 F]FDG ( N = 52), [ 18 F]FAPI ( N = 46) and [ 68 Ga]FAPI ( N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([ 18 F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference.

Results

CZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% ( p < 0.05), outperforming NASC ( p < 0.0001) in [ 18 F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [ 18 F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [ 68 Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC ( p < 0.0001) and NFT-ASC ( p < 0.0001) in terms of NMAE results.

Conclusions

CZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.

Keywords
  • Artificial intelligence
  • Attenuation correction
  • PET/CT
  • Scatter correction
  • Total-body
Citation (ISO format)
SUN, Hao et al. Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET. In: EJNMMI physics, 2024, vol. 11, n° 1, p. 66. doi: 10.1186/s40658-024-00666-8
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Article (Published version)
Identifiers
Journal ISSN2197-7364
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Technical informations

Creation19/07/2024 21:02:29
First validation30/07/2024 08:08:00
Update21/11/2025 10:20:14
Status update21/11/2025 10:20:14
Last indexation21/11/2025 10:21:13
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