Full‐field optical multi‐functional angiography based on endogenous hemodynamic characteristics

Guan, Caizhong; Yi, Min; Du, Qianyi; Xiong, Honglian; Tan, Haishu; Wang, Mingyi; Zeng, Yaguang

doi:10.1002/jbio.202000411

Cited by 4 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used the same experimental imaging setup as that in a previous study. 30 Low-coherence light with a central wavelength of 540 nm, bandwidth of 10 nm, and power of 100 mW was used to illuminate the sample. Validatory experiments were conducted using 2.5-day-old chicken embryos, which served as live biological samples.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal absorption fluctuation imaging based on U-Net

et al. 2022

J. Biomed. Opt.

Self Cite

View full text Add to dashboard Cite

. Significance: Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously. Aim: Spatiotemporal absorption fluctuation imaging (ST-AFI) is proposed to achieve dynamic blood flow imaging with high spatial and temporal resolutions. Approach: ST-AFI is a dynamic optical angiography based on a low-coherence imaging system and U-Net. The system was used to acquire a series of dynamic red blood cell (RBC) signals and static background tissue signals, and U-Net is used to predict optical absorption properties and spatiotemporal fluctuation information. U-Net was generally used in two-dimensional blood flow segmentation as an image processing algorithm for biomedical imaging. In the proposed approach, the network simultaneously analyzes the spatial absorption coefficient differences and the temporal dynamic absorption fluctuation. Results: The spatial resolution of ST-AFI is up to , and the temporal resolution is up to 0.032 s. In vivo experiments on 2.5-day-old chicken embryos were conducted. The results demonstrate that intermittent RBCs flow in capillaries can be resolved, and the blood vessels without blood flow can be suppressed. Conclusions: Using ST-AFI to achieve convolutional neural network (CNN)-based dynamic angiography is a novel approach that may be useful for several clinical applications. Owing to their strong feature extraction ability, CNNs exhibit the potential to be expanded to other blood flow imaging methods for the prediction of the spatiotemporal optical properties with improved temporal and spatial resolutions.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatiotemporal absorption fluctuation imaging based on U-Net

et al. 2022

J. Biomed. Opt.

Self Cite

View full text Add to dashboard Cite

show abstract

Dual-Angle Mueller Matrix Polarimetry for Single Cell

Guan,

Zeng,

Jiang

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Label-free in-vivo classification and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

Guan,

He,

Zhang

et al. 2024

J. Innov. Opt. Health Sci.

View full text Add to dashboard Cite

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment, which renders it a valuable tool for both scientific research and clinical applications. However, the conventional approach for improving classification accuracy often involves labeling cells with fluorescence, which can lead to potential phototoxicity. This study proposes a label-free in-vivo flow cytometry technique, called dynamic YOLOv4 (D-YOLOv4), which improves classification accuracy by integrating absorption intensity fluctuation modulation (AIFM) into YOLOv4 to demodulate the temporal features of moving red blood cells (RBCs) and platelets. Using zebrafish as an experimental model, the D-YOLOv4 method achieved average precisions (APs) of 0.90 for RBCs and 0.64 for thrombocytes (similar to platelets in mammals), resulting in an overall AP of 0.77. These scores notably surpass those attained by alternative network models, thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivo flow cytometry, which holds promise for diverse in-vivo cell classification applications.

show abstract

Full‐field optical multi‐functional angiography based on endogenous hemodynamic characteristics

Cited by 4 publications

References 33 publications

Spatiotemporal absorption fluctuation imaging based on U-Net

Spatiotemporal absorption fluctuation imaging based on U-Net

Dual-Angle Mueller Matrix Polarimetry for Single Cell

Label-free in-vivo classification and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

Contact Info

Product

Resources

About