REFRACTION-CONTRAST COMPUTED TOMOGRAPHIC IMAGES USING SYNCHROTRON RADIATION IN HUMAN LUNG ADENOCARCINOMA AND HISTOLOGIC CORRELATIONS

초록

PURPOSE: The aim of this study is to evaluate the clinical implication of synchrotron radiation (SR) imaging techniques for human lung adenocarcinoma in comparison with pathologic examination. METHODS: Refraction based tomographic imaging technique called X-ray dark field imaging (XDFI) method was used to obtain computed tomographic imaging of human lung adenocarcinoma at the beam line at Photon Factory BL14B at High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan. The X-ray emanated from the 5T vertical wiggler installed in the electron storage ring with an electron energy of 2.5 GeV, and with a typical beam current of 320mA. The images of normal lung tissue were also obtained using same methods, and reconstructed as 3D images. Both reconstructed images were compared with pathologic examinations from histologic slides which made with the identical samples. RESULTS: Pulmonary alveolar structure including terminal bronchioles, alveolar sacs, and vasculatures could be identified in SR images of Normal lung. The hyperplasia of interstitial tissue and dysplasia of alveolar structures were noticed in images from lung adenocarcinoma. Both SR images were considerably correlated with images from histologic slides. The lepidic patterns of cancer tissue was distinguished from invasive area in synchrotron radiation images of lung adenocarcinoma. CONCLUSIONS: Refraction-contrast tomographic techniques using SR could provide high resolution images of lung adenocarcinoma which are compatible to those from pathologic examinations. However, more refinement should be proceeded for clinical applications. CLINICAL IMPLICATIONS: Refraction-contrast tomographic images using synchrotron X-rays are considered to be one of the best way to examine biological soft tissue including lung cancer. DISCLOSURE: This research was supported by the National Research Foundation of Korea (NRF) grant funded by Korea government (MSIT) (No. NRF2019R1G1A1009904)