Purpose: Quantitative images of metabolic activity can be derived through dynamic PET. However, the conventional approach necessitates invasive blood sampling to acquire the input function, thus limiting its noninvasive nature. The aim of this study was to devise a system based on convolutional neural network (CNN) capable of estimating the time-radioactivity curve of arterial plasma and accurately quantify the cerebral metabolic rate of glucose (CMRGlc) directly from PET data, thereby eliminating the requirement for invasive sampling.
Methods: This retrospective investigation analyzed 29 patients with neurological disorders who underwent comprehensive whole-body 18 F-FDG-PET/CT examinations. Each patient received an intravenous infusion of 185 MBq of 18 F-FDG, followed by dynamic PET data acquisition and arterial blood sampling. A CNN architecture was developed to accurately estimate the time-radioactivity curve of arterial plasma.
Results: The CNN estimated the time-radioactivity curve using the leave-one-out technique. In all cases, there was at least one frame with a prediction error within 10% in at least one frame. Furthermore, the correlation coefficient between CMRGlc obtained from the sampled blood and CNN yielded a highly significant value of 0.99.
Conclusion: The time-radioactivity curve of arterial plasma and CMRGlc was determined from 18 F-FDG dynamic brain PET data using a CNN. The utilization of CNN has facilitated noninvasive measurements of input functions from dynamic PET data. This method can be applied to various forms of quantitative analysis of dynamic medical image data.
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.