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16 March 2023 A universal method for sensorless adaptive microscopy: a physics-embedded machine learning approach (Conference Presentation)
Qi Hu, Martin Hailstone, Jingyu Wang, Matthew Wincott, Huriye Antilgan, Richard Parton, Danail Stoychev, Jacopo Antonello, Adam Packer, Martin Booth
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Proceedings Volume PC12388, Adaptive Optics and Wavefront Control for Biological Systems IX; PC1238802 (2023) https://doi.org/10.1117/12.2649347
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Aberrations are a common problem in microscopes resulting in compromised imaging contrast and resolution. Adaptive optics (AO) can correct aberrations but requires either a wavefront sensor or a wavefront-sensorless AO method that requires multiple sample exposures. We created a machine learning (ML) approach that embeds physical understanding of the imaging process into a sensorless AO method. This enables correction of aberrations with as few as two sample exposures. The method was translated across different microscope modalities. This includes two-photon microscopy and three-photon microscopy of in vivo mouse neural activity, showing robustness to specimen motion and activity related intensity variations.
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Qi Hu, Martin Hailstone, Jingyu Wang, Matthew Wincott, Huriye Antilgan, Richard Parton, Danail Stoychev, Jacopo Antonello, Adam Packer, and Martin Booth "A universal method for sensorless adaptive microscopy: a physics-embedded machine learning approach (Conference Presentation)", Proc. SPIE PC12388, Adaptive Optics and Wavefront Control for Biological Systems IX, PC1238802 (16 March 2023); https://doi.org/10.1117/12.2649347
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KEYWORDS
Adaptive optics
Sensors
Machine learning
Microscopy
Microscopes
Point spread functions
Imaging systems
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