Compton Background Elimination for in Vivo X-Ray Fluorescence Imaging of Gold Nanoparticles Using Convolutional Neural Network

Abstract

This article reports the first application of a convolutional neural network (CNN) to in vivo X-ray fluorescence (XRF) images of gold nanoparticles (GNPs) obtained by a benchtop X-ray system to eliminate Compton-scattered photons. The XRF imaging system comprises a 2-D cadmium zinc telluride (CZT) gamma camera, a pinhole collimator, and fan-beam polychromatic X-rays. An architecture of the 2-D CNN model for Compton background elimination was optimally designed and trained with data sets obtained by the measurements of water only and GNP-embedded imaging phantoms. The XRF images generated by the trained 2-D CNN were compared with those generated by the direct subtraction method. The developed 2-D CNN was also applied to generate in vivo XRF images of gold in living mice exposed to GNPs. The in vivo XRF images generated by the two methods were then compared in terms of the difference in GNP concentrations. The results demonstrate that the 2-D CNN model was successfully applied to in vivo XRF images for detecting the concentration and location of GNPs in living mice. This also implies that in vivo XRF images can be acquired using the 2-D CNN without preinjection scanning; thus, the image acquisition time and imaging dose can be reduced by half. Furthermore, in vivo XRF images of GNPs can be acquired at any time after the injection, as the 2-D CNN eliminates the need to restrict a living object during the interscans.