Deep-learning-based 3D blood flow reconstruction in transmissive laser speckle imaging

Abstract

Transmissive laser speckle imaging (LSI) is useful for monitoring large field-of-view (FOV) blood flow in thick tissues. However, after longer transmissions, the contrast of the transmitted speckle images is more likely to be blurred by multiple scattering, resulting in decreased accuracy and spatial resolution of deep vessels. This study proposes a deep-learning-based strategy for high spatiotemporal resolution three-dimensional (3D) reconstruction from a single transilluminated laser speckle contrast image, providing more structural and functional details without multifocus two-dimensional (2D) imaging or 3D optical imaging with point/line scanning. Based on the correlation transfer equation, a large training dataset is generated by convolving vessel masks with depth-dependent point spread functions (PSF). The UNet and ResNet are used for deblurring and depth estimation. The blood flow in the reconstructed 3D vessels is estimated by a depth-dependent contrast model. The proposed method is evaluated with simulated data and phantom experiments, achieving high-fidelity structural reconstruction with a depth-independent estimation of blood flow. This fast 3D blood flow imaging technique is suitable for real-time monitoring of thick tissue and the diagnosis of vascular diseases.