Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

Abstract

Background Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images. Methods In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image. Results A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min. Conclusions The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model *

“…Compared to the gold standard of kinetic modeling with arterial blood sampling, the best nSIME method showed high correlation ( r = 0.83) and low bias when estimating the regional cerebral metabolic rate of glucose and performed better than method using a population-based input function (Takikawa et al 1993 ). Recently, Liang et al ( 2023 ) proposed a method combining deep learning and kinetic modeling to directly estimate all kinetic parameters and the fractional blood volume for 30 min of dynamic FDG PET data without access to the input function.…”