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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Spatiotemporal absorption fluctuation imaging based on U-Net.

Min Yi^1,2, Lin-Chang Wu², Qian-Yi Du²

¹Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, Fos, China.

Journal of Biomedical Optics

|February 9, 2022

Summary

Spatiotemporal absorption fluctuation imaging (ST-AFI) uses a novel U-Net approach for dynamic blood flow imaging. This method achieves high spatial and temporal resolutions, enabling detailed visualization of microvasculature in research and clinical settings.

Keywords:

U-Net dynamic blood flow imaging optical angiography spatiotemporal absorption fluctuation imaging

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Area of Science:

Biomedical Optics
Medical Imaging
Vascular Biology

Background:

Full-field optical angiography is crucial for vascular disease research and diagnosis.
Current methods face limitations in simultaneously enhancing temporal and spatial resolutions.

Purpose of the Study:

To introduce Spatiotemporal Absorption Fluctuation Imaging (ST-AFI) for dynamic blood flow imaging.
To achieve high spatial and temporal resolutions in optical angiography.

Main Methods:

ST-AFI employs a low-coherence imaging system integrated with a U-Net deep learning model.
The system captures dynamic red blood cell signals and static tissue data.
U-Net analyzes spatial absorption and temporal fluctuation for precise imaging.

Main Results:

Achieved a spatial resolution of 4.33 μm and temporal resolution of 0.032 s.
Successfully visualized intermittent red blood cell flow in capillaries in chicken embryo models.
Demonstrated effective suppression of non-perfused vessels.

Conclusions:

ST-AFI presents a novel convolutional neural network (CNN)-based dynamic angiography technique.
This approach holds promise for various clinical applications.
CNNs can enhance other blood flow imaging methods for improved spatiotemporal optical property prediction.