Development of fast and three-dimensional focused x-ray luminescence tomography system

Abstract

X-ray luminescence computed tomography (XLCT) is a hybrid molecular imaging modality combining the merits of both X-ray imaging (high spatial resolution) and optical imaging (high sensitivity to tracer nanophosphors). Narrow X-ray beam based XLCT imaging has been demonstrated to have the capacity of high spatial resolution imaging at the cost of the data acquisition time. We have primarily focused on improving the performance of the narrow X-ray beam based XLCT imaging. In a previous study, we proposed a scanning strategy achieved by reducing the scanning step size for improving the spatial resolution from double the X-ray beam size to close to the X-ray beam size. For the imaging speed, we recently introduced a continuous scanning scheme to replace the selective excitation scheme and used a photon counter to replace the oscilloscope to acquire measurement data, yielding a 16 times faster scanning time compared with what used in traditional XLCT systems. In addition, we developed a deep learning based XLCT reconstruction algorithm to reduce the number of projection views in a previous work. Moreover, we previously synthesized and compared biocompatible nanophosphors of distinct X-ray luminescence spectra to make multi-color XLCT imaging possible. Here, based on the previous work, we designed and built a first-of-its-kind fast and three-dimensional XLCT imaging system with the capacity of multi-wavelength measurements. A lab-made image acquisition software has been developed to control the system. We have performed physical experiments and verified the system performance.