An improved Patlak-based Kiparametric imaging approach for clinical18F-FDG total-body PET

The objective is to generate reliable Ki parametric images from 18F-FDG total-body PET with clinically acceptable scan durations using Patlak and shallow machine learning algorithms, under conditions of limited computational and data resources.OBJECTIVEThe objective is to generate reliable Ki parametric images from 18F-FDG total-body PET with clinically acceptable scan durations using Patlak and shallow machine learning algorithms, under conditions of limited computational and data resources.We proposed a robust and fast algorithm named Patlak-KXD to generate Ki images from dynamic PET images with shortened scan durations. In the training phase, K-means is employed to generate a Ki-balanced training dataset. Subsequently, XGBoost is utilized to learn the mapping relationship between the tissue-to-blood standardized uptake ratio (SUR) time curves and Patlak-based Ki values using this balanced dataset. In the prediction phase, the trained XGBoost can generate Ki images by calculating the Ki values from voxel-based SUR time curves obtained from the dynamic images. We compared the accuracy of Ki images generated by both the Patlak-KXD and the traditional Patlak methods across a range of shortened scan durations, and the differences in Ki images generated by the XGBoost model using static (Patlak-KXS) and dynamic PET inputs.APPROACHWe proposed a robust and fast algorithm named Patlak-KXD to generate Ki images from dynamic PET images with shortened scan durations. In the training phase, K-means is employed to generate a Ki-balanced training dataset. Subsequently, XGBoost is utilized to learn the mapping relationship between the tissue-to-blood standardized uptake ratio (SUR) time curves and Patlak-based Ki values using this balanced dataset. In the prediction phase, the trained XGBoost can generate Ki images by calculating the Ki values from voxel-based SUR time curves obtained from the dynamic images. We compared the accuracy of Ki images generated by both the Patlak-KXD and the traditional Patlak methods across a range of shortened scan durations, and the differences in Ki images generated by the XGBoost model using static (Patlak-KXS) and dynamic PET inputs.The Ki images generated by the Patlak-KXD from just a 4-minute (56-60 minutes) dynamic 18F-FDG total-body PET scan are comparable to those generated by the traditional Patlak method using 40-minute (20-60 minutes) dynamic PET images, as demonstrated by a normalized mean square error of 0.13 and a Pearson's