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Optical diffraction tomography of biological cells is commonly based on illumination-scanning, which suffers of the missing-cone problem, but enables accurate calibration of the projection angle. We present an AI-driven alternative using precise adaptive-optical cell-rotation. A multi-core fiber, is transformed to a remote phased-array, employing a spatial light modulator and a novel phase encoder neural network called CoreNet. The resulting high-fidelity light-field delivery enables targeted 3D cell-rotation resulting in full spatial frequency coverage. The cell-motion and rotation angle are detected automatically and in real-time by a workflow based on machine learning and computer vision leading to rapid and robust tomographic reconstruction.
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Nektarios Koukourakis, Jiawei Sun, Jürgen W. Czarske, "Optical diffraction tomography based on AI-driven adaptive optical cell-rotation," Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550B (28 September 2023); https://doi.org/10.1117/12.2678217