Abstract
Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.
Highlights
In recent years, the development of biological imaging was focusing on real-time, high resolution and deep in vivo imaging [1,2]
We demonstrate a sensor-less Adaptive optics (AO) method based on the machine-learning algorithm, which employs a Convolutional Neural Network (CNN) to obtain the intricate non-linear mappings from the distorted point spread function images to the wavefront aberrations expressed as the Zernike coefficients
The proposed machine learning guided fast AO method was first applied on a phase-mask, which is a phase pattern composed by a set of random Zernike coefficients (1st–15th) and loaded on the spatial light modulator (SLM) at the back-pupil plane
Summary
The development of biological imaging was focusing on real-time, high resolution and deep in vivo imaging [1,2]. Adaptive optics (AO) becomes a valuable technique for high-resolution microscopy. It compensates the aberrations introduced by the specimens and obtains high-resolution images in deep biological tissue [3]. AO is originally developed for telescopes to overcome the atmospheric distortions, which degrade the image qualities of the extraterrestrial objects. It has been applied in optical microscopy to recover diffraction-limited imaging deep in the biological tissue [4,5,6] by using an active element such as a deformable mirror (DM) or a spatial light modulator (SLM). When increasing the number of the pupil segments for finer wavefront corrections, mCOAT might cause much more time consumptions
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