Machine learning guided rapid focusing with sensor-less aberration corrections

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 sensorless 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.

Practical sensorless aberration estimation for 3D microscopy with deep learning

“…Over the last years, deep learning-based approaches using convolutional neural networks (CNNs) have proven to be powerful and computationally efficient for image-based classification and regression tasks for microscopy images [18,19]. Recently, several studies demonstrated that deep learning-based phase retrieval can produce accurate results at fast processing speeds [20][21][22][23][24][25], however they fall short regarding their practical applicability. Some of these approaches [22][23][24] used purely simulated synthetic data, where generalizability to real microscopy images is unclear.…”

“…Some of these approaches [22][23][24] used purely simulated synthetic data, where generalizability to real microscopy images is unclear. Others focused on specific microscopy acquisition modes (such as using biplanar PSFs [20]) or microscopy setups that allow to collect large sets of experimental ground truth data for training and prediction [21,25], thus limiting this approach in practice. Moreover, most studies lack comparison against strong classical phase retrieval methods that are used in practice.…”

Differentiable model-based adaptive optics with transmitted and reflected light

“…Machine learning offers novel approaches to correct for aberrations encountered when imaging though scattering materials, [1][2][3][4] from astronomy [5][6][7][8][9] to microscopy with transmitted (for example [10][11][12]) and reflected light [13]. To find aberration corrections in these situations, machine learning typically relies on large synthetic datasets.…”

“…In practice, training datasets are often based on combinations of Zernike polynomials [5][6][7][8][9][10][11][12][13] which might however not accurately capture all aspects of experimentally encountered aberrations. Additionally, for more strongly scattering samples, which require increasingly higher orders of Zernike modes, covering all potential scattering situations by sampling a sufficient number of different mode combinations eventually results in very large datasets.…”

Aberration corrections of doughnut beam by adaptive optics in the turbid medium

“…Currently, adaptive optics (AO) is used to correct the wavefront aberrations, taking advantages of an active element such as a deformable mirror or a SLM . Nowadays, several methods based on AO were reported to successfully correct the phase patterns for doughnut beams.…”