RESUMEN
Whole slide imaging (WSI) has become an essential tool in pathological diagnosis, owing to its convenience on remote and collaborative review. However, how to bring the sample at the optimal position in the axial direction and image without defocusing artefacts is still a challenge, as traditional methods are either not universal or time-consuming. Until recently, deep learning has been shown to be effective in the autofocusing task in predicting defocusing distance. Here, we apply quantized spiral phase modulation on the Fourier domain of the captured images before feeding them into a light-weight neural network. It can significantly reduce the average predicting error to be lower than any previous work on an open dataset. Also, the high predicting speed strongly supports it can be applied on an edge device for real-time tasks with limited computational source and memory footprint.
RESUMEN
It is a great challenge in two-photon microscopy (2PM) to have a high volumetric imaging speed without sacrificing the spatial and temporal resolution in three dimensions (3D). The structure in 2PM images could be reconstructed with better spatial and temporal resolution by the proper choice of the data processing algorithm. Here, we propose a method to reconstruct 3D volume from 2D projections imaged by mirrored Airy beams. We verified that our approach can achieve high accuracy in 3D localization over a large axial range and is applicable to continuous and dense sample. The effective field of view after reconstruction is expanded. It is a promising technique for rapid volumetric 2PM with axial localization at high resolution.
