Deep learning-based, misalignment resilient, real-time Fourier Ptychographic Microscopy reconstruction of biological tissue slides

Fourier ptychographic microscopy probes label-free samples from multiple angles and achieves super resolution phase-contrast imaging according to a synthetic aperture principle. Thus, it is particularly suitable for high-resolution imaging of tissue slides over a wide field of view. Recently, in order to make the optical setup robust against misalignments-induced artefacts, numerical multi-look has been added to the conventional phase retrieval process, thus allowing the elimination of related phase errors but at the cost of a long computational time. Here we train a generative adversarial network to emulate the process of complex amplitude estimation. Once trained, the network can accurately reconstruct in real-time Fourier ptychographic images acquired using a severely misaligned setup. We benchmarked the network by reconstructing images of animal neural tissue slides. Above all, we show that important morphometric information, relevant for diagnosis on neural tissues, are retrieved using the network output. These are in very good agreement with the parameters calculated from the ground-truth, thus speeding up significantly the quantitative phase-contrast analysis of tissue samples.