Optical-resolution photoacoustic microscopy with ultrafast dual-wavelength excitation.
Yingying ZhouSiyi LiangMingsheng LiChengbo LiuPuxiang LaiLidai WangPublished in: Journal of biophotonics (2020)
Fast functional and molecular photoacoustic microscopy requires pulsed laser excitations at multiple wavelengths with enough pulse energy and short wavelength-switching time. Recent development of stimulated Raman scattering in optical fiber offers a low-cost laser source for multiwavelength photoacoustic imaging. In this approach, long fibers temporally separate different wavelengths via optical delay. The time delay between adjacent wavelengths may eventually limits the highest A-line rate. In addition, a long-time delay in fiber may limit the highest pulse energy, leading to poor image quality. In order to achieve high pulse energy and ultrafast dual-wavelength excitation, we present optical-resolution photoacoustic microscopy with ultrafast dual-wavelength excitation and a signal separation method. The signal separation method is validated in numerical simulation and phantom experiments. We show that when two photoacoustic signals are partially overlapped with a 50-ns delay, they can be recovered with 98% accuracy. We apply this ultrafast dual-wavelength excitation technique to in vivo OR-PAM. Results demonstrate that A-lines at two wavelengths can be successfully separated, and sO2 values can be reliably computed from the separated data. The ultrafast dual-wavelength excitation enables fast functional photoacoustic microscopy with negligible misalignment among different wavelengths and high pulse energy, which is important for in vivo imaging of microvascular dynamics.
Keyphrases
- high resolution
- energy transfer
- high speed
- single molecule
- fluorescence imaging
- image quality
- blood pressure
- low cost
- quantum dots
- mass spectrometry
- computed tomography
- high throughput
- label free
- optical coherence tomography
- photodynamic therapy
- magnetic resonance imaging
- liquid chromatography
- big data
- magnetic resonance
- deep learning
- artificial intelligence
- zika virus