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. 2025 Mar;52(4):1436-1447.
doi: 10.1007/s00259-024-07008-x. Epub 2024 Dec 2.

Self-supervised neural network for Patlak-based parametric imaging in dynamic [18F]FDG total-body PET

Wenjian Gu  1   2 , Zhanshi Zhu  1   2 , Ze Liu  2   3 , Yihan Wang  2 , Yanxiao Li  2 , Tianyi Xu  2 , Weiping Liu  2   4 , Gongning Luo  5 , Kuanquan Wang  6 , Yun Zhou  7   8
Affiliations

Self-supervised neural network for Patlak-based parametric imaging in dynamic [18F]FDG total-body PET

Wenjian Gu et al. Eur J Nucl Med Mol Imaging. 2025 Mar.

Abstract

Purpose: The objective of this study is to generate reliable Ki parametric images from a shortened [18F]FDG total-body PET for clinical applications using a self-supervised neural network algorithm.

Methods: We proposed a self-supervised neural network algorithm with Patlak graphical analysis (SN-Patlak) to generate Ki images from shortened dynamic [18F]FDG PET without 60-min full-dynamic PET-based training. The algorithm deeply integrates neural network architecture with a Patlak method, employing the fitting error of the Patlak plot as the neural network's loss function. As the 0-60 min blood time activity curve (TAC) required by the standard Patlak plot is unobtainable from shortened dynamic PET scans, a population-based "normalized time" (integral-to-instantaneous blood concentration ratio) was used for the linear fitting of Patlak plot of t* to 60 min, and the modified Patlak plot equation was then incorporated into the neural network. Ki images were generated by minimizing the difference between the input layer (measured tissue-to-blood concentration ratios) and the output layer (predicted tissue-to-blood concentration ratios). The effects of t* (20 to 50 min post injection) on the Ki images generated from the SN-Patlak and standard Patlak was evaluated using the normalized mean square error (NMSE), and Pearson's correlation coefficient (Pearson's r).

Results: The Ki images generated by the SN-Patlak are robust to the dynamic PET scan duration, and the Ki images generated by the SN-Patlak from just a 10-minute (50-60 min post-injection) dynamic [18F]FDG total-body PET scan are comparable to those generated by the standard Patlak method from 40-min (20-60 min post injection) with NMSE = 0.15 ± 0.03 and Pearson's r = 0.93 ± 0.01.

Conclusions: The SN-Patlak parametric imaging algorithm is robust and reliable for quantification of 10-min dynamic [18F]FDG total-body PET.

Keywords: Parametric image; Patlak plot; Self-supervised neural network; Total-body PET; [18F]FDG.

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Conflict of interest statement

Declarations. Ethics approval: The study involving human participants was in line with the principles of the ethics committee in Zhongshan Hospital and the declaration of Helsinki in 1964. Consent to participate: The informed consent document was signed by all participants prior to their voluntary enrollment in this study. Conflict of interest: The authors declare no competing interests.

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