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

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

We proposed a self-supervised neural network algorithm with Patlak graphical analysis (SN-Patlak) to generate K_i 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. K_i 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 K_i 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).

The K_i images generated by the SN-Patlak are robust to the dynamic PET scan duration, and the K_i 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.

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