High-resolution 3D fusion method for optical-resolution photoacoustic microscopy using deep learning

2021 ◽  
Author(s):  
Sihang Li ◽  
Zhuangzhuang Wang ◽  
Bofang Chen ◽  
Chenghao Gu ◽  
Ziming Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Optical Resolution ◽  
Fusion Method ◽  
Photoacoustic Microscopy

scholarly journals Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Xingxing Chen ◽  
Weizhi Qi ◽  
Lei Xi
Keyword(s):  
Deep Learning ◽  
Optical Resolution ◽  
Motion Artifacts ◽  
Photoacoustic Microscopy ◽  
Simulation Studies ◽  
Correction Algorithm ◽  
Brain Vessels ◽  
Capillary Networks ◽  
Motion Corrections

Abstract In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.

scholarly journals Deep Learning-powered Bessel-beam Multi-parametric Photoacoustic Microscopy

2021 ◽  
Author(s):  
Yifeng Zhou ◽  
Naidi Sun ◽  
Song Hu
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Gaussian Beam ◽  
Mouse Brain ◽  
Bessel Beam ◽  
Large Field ◽  
Depth Range ◽  
Photoacoustic Microscopy ◽  
Beam Excitation ◽  
Live Mouse

Enabling simultaneous and high resolution quantification of the total concentration of hemoglobin (CHb), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi parametric photoacoustic microscopy (PAM) has emerged as a promising tool for functional and metabolic imaging of the live mouse brain. However, due to the limited depth of focus imposed by the Gaussian beam excitation, the quantitative measurements become inaccurate when the imaging object is out of focus. To address this problem, we have developed a hardware-software combined approach by integrating Bessel beam excitation and conditional generative adversarial network (cGAN) based deep learning. Side by side comparison of the new cGAN powered Bessel-beam multi parametric PAM against the conventional Gaussian beam multi parametric PAM shows that the new system enables high resolution, quantitative imaging of CHb, sO2, and CBF over a depth range of ~600 μm in the live mouse brain, with errors 13 to 58 times lower than those of the conventional system. Better fulfilling the rigid requirement of light focusing for accurate hemodynamic measurements, the deep learning powered Bessel beam multi parametric PAM may find applications in large field functional recording across the uneven brain surface and beyond (e.g., tumor imaging).

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