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Optical diffraction tomography of biological cells is an emerging label-free sensing technique. It is promising for studies of cellular functions towards understanding the causes of diseases. Illumination scanning is Commonly used, which enables accurate calibration of the projection angle. However, as a trade-off the technique suffers from the missing cone problem. 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. The multicore fiber endoscope enables keyhole access for cell rotation tomography of HL60 human cells for cancer studies.
Juergen W. Czarske
"Optical diffraction tomography of rotated cancer cells using lensless fiber endoscopy and deep neural networks", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520N (13 March 2024); https://doi.org/10.1117/12.3008586
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Juergen W. Czarske, "Optical diffraction tomography of rotated cancer cells using lensless fiber endoscopy and deep neural networks," Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520N (13 March 2024); https://doi.org/10.1117/12.3008586