Deep learning enhanced NIR-Ⅱ volumetric imaging of whole mice vasculature

被引:1
|
作者
Sitong Wu [1 ,2 ]
Zhichao Yang [1 ,2 ]
Chenguang Ma [1 ]
Xun Zhang [1 ]
Chao Mi [1 ]
Jiajia Zhou [2 ]
Zhiyong Guo [1 ,3 ]
Dayong Jin [1 ,2 ,3 ]
机构
[1] UTS-SUSTech Joint Research Centre for Biomedical Materials & Devices, Department of Biomedical Engineering, Southern University of Science and Technology
[2] Institute for Biomedical Materials & Devices, Faculty of Science, University of Technology Sydney
[3] Guangdong Provincial Key Laboratory of Advanced Biomaterials, Southern University of Science and Technology
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论]; TP391.41 []; R318 [生物医学工程];
学科分类号
080203 ; 081104 ; 0812 ; 0831 ; 0835 ; 1405 ;
摘要
Fluorescence imaging through the second near-infrared window(NIR-Ⅱ,1000–1700 nm) allows in-depth imaging.However, current imaging systems use wide-field illumination and can only provide low-contrast 2D information, without depth resolution. Here, we systematically apply a light-sheet illumination, a time-gated detection, and a deep-learning algorithm to yield high-contrast high-resolution volumetric images. To achieve a large Fo V(field of view) and minimize the scattering effect, we generate a light sheet as thin as 100.5 μm with a Rayleigh length of 8 mm to yield an axial resolution of 220 μm. To further suppress the background, we time-gate to only detect long lifetime luminescence achieving a high contrast of up to 0.45 Icontrast. To enhance the resolution, we develop an algorithm based on profile protrusions detection and a deep neural network and distinguish vasculature from a low-contrast area of 0.07 Icontrast to resolve the 100μm small vessels. The system can rapidly scan a volume of view of 75 × 55 × 20 mm3and collect 750 images within 6mins. By adding a scattering-based modality to acquire the 3D surface profile of the mice skin, we reveal the whole volumetric vasculature network with clear depth resolution within more than 1 mm from the skin. High-contrast large-scale 3D animal imaging helps us expand a new dimension in NIR-Ⅱ imaging.
引用
收藏
页码:8 / 17
页数:10
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