Fluorescence molecular imaging is a promising tool for molecular tracking, and thus can visualize the vascular structure of small animal. However, with the strong scattering in biological tissues, traditional fluorescence molecular imaging in the visible spectrum or the first near-infrared spectrum (NIR-I, 700-900 nm) has a limitation for high-resolution vascular imaging. Recently, the novel in vivo fluorescence molecular imaging in the longer second near-infrared window (NIR-IIb, 1500-1700 nm) is successfully developed for small animal imaging. The NIR-IIb window affords high imaging resolution and deep tissue penetration because of the diminished photon scattering effect, which is suitable for vascular imaging. However, the clinical applications of NIR-IIb fluorescence molecular imaging have been severely limited for lack of the clinical fluorophores. Here, we show that the clinically available dye, indocyanine green (ICG), can also emit the NIR-IIb signal, which provides high-resolution vascular imaging of small animal. We construct a novel imaging system for NIR-I and NIR-IIb imaging simultaneously and perform two vascular imaging experiments. The results demonstrate that the NIR-IIb imaging using ICG shows great superiority for high-resolution vascular imaging of small animal compared with NIR-I imaging. It is believed that this study will facilitate the preclinical and clinical applications of NIR-IIb molecular imaging using ICG in the future.
Cherenkov luminescence tomography (CLT) has become a novel three-dimensional (3D) non-invasive technology for biomedical applications such as tumor detection, pharmacodynamics evaluation, etc. However, the reconstruction of CLT still remains a challenging task because of the strong absorbing effect and scattering effect of Cherenkov photon transport process. In this study, we proposed a novel robust sparse reconstruction method named look ahead orthogonal matching pursuit (LAOMP) algorithm to improve the robustness and accuracy of reconstruction for CLT instead of traditional OMP algorithm based on a look ahead strategy. To validate the reconstruction performance of LAOMP method, a series of numerical simulations were conducted. The results showed that LAOMP method obtained the higher robustness and accuracy in locating the optical sources compared with the OMP and StOMP algorithms.
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