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.
The refractive index heterogeneity severely limits the imaging performance of optical microscopy in deep tissue. Adaptive optics (AO) is currently widely used to recover the diffraction-limited resolution at depth. However, there is a tradeoff between the time resolution and spatial resolution, which makes it difficult to achieve the real-time imaging in deep tissue. This is partially because that the effective correction area of conventional AO is limited with a single guide star (GS). Therefore, the using of multiple guide stars is a potential solution to increase the corrected field of view. Here we report an automatic selection algorithm of multiple guide stars and demonstrate the feasibility by implementing this method in the system of conjugate adaptive optical correction with multiple GSs. The simulation results indicate that compared with the case of the single guide star, high-resolution imaging can be obtained in most imaging areas with automatically selected 9 guide stars. Further, we can obtain optimally numbers and positions of the guide stars automatically and expect larger area aberrations. Therefore, this method has the great potential in in vivo deep tissue imaging.
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