Reflection-mode switchable subwavelength Bessel-beam and Gaussian-beam photoacoustic microscopy in vivo

2018 ◽  
Vol 12 (2) ◽  
pp. e201800215 ◽  
Author(s):  
Byullee Park ◽  
Hoyong Lee ◽  
Seungwan Jeon ◽  
Joongho Ahn ◽  
Hyung H. Kim ◽  
...  
Keyword(s):  
Gaussian Beam ◽  
Bessel Beam ◽  
Photoacoustic Microscopy ◽  
Reflection Mode

scholarly journals Reflection-mode in vivo photoacoustic microscopy with subwavelength lateral resolution

2014 ◽  
Vol 5 (12) ◽  
pp. 4235 ◽  
Author(s):  
Wei Song ◽  
Wei Zheng ◽  
Ruimin Liu ◽  
Riqiang Lin ◽  
Hongtao Huang ◽  
...  
Keyword(s):  
Lateral Resolution ◽  
Photoacoustic Microscopy ◽  
Reflection Mode

In Vivo Reflection-Mode Photoacoustic Microscopy Enhanced by Plasmonic Sensing with an Acoustic Cavity

ACS Sensors ◽  
2019 ◽  
Vol 4 (10) ◽  
pp. 2697-2705 ◽  
Author(s):  
Wei Song ◽  
Guangdi Guo ◽  
Jing Wang ◽  
Yan Zhu ◽  
Chonglei Zhang ◽  
...  
Keyword(s):  
Photoacoustic Microscopy ◽  
Reflection Mode ◽  
Acoustic Cavity ◽  
Plasmonic Sensing

Reflection-mode submicron-resolution photoacoustic microscopy in vivo

2012 ◽  
Author(s):  
Chi Zhang ◽  
Konstantin Maslov ◽  
Song Hu ◽  
Ruimin Chen ◽  
Qifa Zhou ◽  
...  
Keyword(s):  
Photoacoustic Microscopy ◽  
Reflection Mode ◽  
Submicron Resolution

scholarly journals In vivo dark-field reflection-mode photoacoustic microscopy

2005 ◽  
Vol 30 (6) ◽  
pp. 625 ◽  
Author(s):  
Konstantin Maslov ◽  
Gheorghe Stoica ◽  
Lihong V. Wang
Keyword(s):  
Dark Field ◽  
Photoacoustic Microscopy ◽  
Reflection Mode

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).

scholarly journals Fast 3-D dark-field reflection-mode photoacoustic microscopy in vivo with a 30-MHz ultrasound linear array

2008 ◽  
Vol 13 (5) ◽  
pp. 054028 ◽  
Author(s):  
Liang Song ◽  
Konstantin Maslov ◽  
Rachel Bitton ◽  
K. Kirk Shung ◽  
Lihong V. Wang
Keyword(s):  
Linear Array ◽  
Dark Field ◽  
Photoacoustic Microscopy ◽  
Reflection Mode
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