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.