Deep tissue focusing based on machine learning

Ke Si; Yiye Zhang; Yuncheng Jin; Lejia Hu; Wei Gong

doi:10.1117/12.2518111

12 March 2019 Deep tissue focusing based on machine learning

Ke Si, Yiye Zhang, Yuncheng Jin, Lejia Hu, Wei Gong

Proceedings Volume 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application; 1102340 (2019) https://doi.org/10.1117/12.2518111
Event: Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 2018, Xi'an, China

Abstract

Non-invasive deep tissue imaging and focusing is highly demanded in biomedical research. However, for in vivo applications, the major challenge is the limited imaging depth, a of random scattering in biological tissue causing exponential attenuation of the ballistic component the light wave. Here we present the optical focusing with diffraction-limited resolution deep inside highly scattering media by using machine learning. Compared with conventional adaptive optics, method can not only provide high-speed sensor-less wavefront measurement with more than 90% accuracy, but also dramatically reduce photobleaching and photodamage. This technology paves the way for many important applications in fundamental biology research, especially in neuroscience.

Citation Download Citation

Ke Si, Yiye Zhang, Yuncheng Jin, Lejia Hu, and Wei Gong "Deep tissue focusing based on machine learning", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 1102340 (12 March 2019); https://doi.org/10.1117/12.2518111

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available