Acoustic-feedback wavefront-adapted photoacoustic microscopy
Abstract
Optical microscopy is indispensable to biomedical research and clinical investigations. As all molecules absorb light, optical-resolution photoacoustic microscopy (PAM) is an important tool to image molecules at high resolution without labeling. However, due to tissue-induced optical aberration, the imaging quality degrades with increasing imaging depth. To mitigate this effect, we develop an imaging method, called acoustic-feedback wavefront-adapted PAM (AWA-PAM), to dynamically compensate for tissue-induced aberration at depths. In contrast to most existing adaptive optics assisted optical microscopy, AWA-PAM employs acoustic signals rather than optical signals to indirectly determine the optimized wavefront. To demonstrate this technique, we imaged zebrafish embryos and mouse ears in vivo. Experimental results show that compensating for tissue-induced aberration in live tissue effectively improves both signal strength and lateral resolution. With this capability, AWA-PAM reveals fine structures, such as spinal cords and microvessels, that were otherwise unidentifiable using conventional PAM. We anticipate that AWA-PAM will benefit the in vivo imaging community and become an important tool for label-free optical imaging in the quasi-ballistic regime.
Copyright and License
© 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
Funding
National Natural Science Foundation of China (12004446, 12274129, 12325408, 62135006, 62275106, 92150102); National Institutes of Health (R01 EB028277); Chan Zuckerberg Initiative Donor-Advised Fund (DAF) at the Silicon Valley Community Foundation (2020-225832)
Data Availability
All data needed to evaluate the conclusions in the paper are present in the main text and Supplement 1. Additional data are available from authors upon request.
Conflict of Interest
L.W. has a financial interest in Microphotoacoustics, Inc., CalPACT, LLC, and Union Photoacoustic Technologies, Ltd., which, however, did not support this work.
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Additional details
- National Natural Science Foundation of China
- 12004446
- National Natural Science Foundation of China
- 12274129
- National Natural Science Foundation of China
- 12325408
- National Natural Science Foundation of China
- 62135006
- National Natural Science Foundation of China
- 62275106
- National Natural Science Foundation of China
- 92150102
- National Institutes of Health
- R01 EB028277
- Chan Zuckerberg Initiative (United States)
- 2020-225832
- Silicon Valley Community Foundation