Open AccessReal‐time X‐ray imaging of mouse cerebral microvessels in vivo using a pixel temporal averaging method
Author(s) -
Yu Fucheng,
Wang Feixiang,
Li Ke,
Du Guohao,
Deng Biao,
Xie Honglan,
Yang Guoyuan,
Xiao Tiqiao
Publication year - 2022
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s1600577521012522
Subject(s) - preclinical imaging , biomedical engineering , microvessel , subtraction , noise (video) , in vivo , pixel , computer science , nuclear medicine , artificial intelligence , medicine , pathology , mathematics , biology , image (mathematics) , immunohistochemistry , microbiology and biotechnology , arithmetic
Rodents are used extensively as animal models for the preclinical investigation of microvascular‐related diseases. However, motion artifacts in currently available imaging methods preclude real‐time observation of microvessels in vivo . In this paper, a pixel temporal averaging (PTA) method that enables real‐time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast‐to‐noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real‐time angiography. In addition, experiments with less than one‐quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X‐ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X‐ray source. In conclusion, the proposed PTA method may facilitate the real‐time investigation of cerebral microvascular‐related diseases using small animal models.