@article {70429,
	title = {Abstract 5550: Phase diversity wavefront sensing for aberration correction in light sheet microscopy},
	journal = {Journal of Biological Chemistry},
	volume = {302},
	year = {2026},
	month = {2026 May },
	pages = {112990},
	abstract = {<p>Fluorescence microscopy provides insights into cellular structure and function, but the undesirable bending of light from the sample or imperfect optics ({\textquotedblleft}optical aberrations{\textquotedblright}) often degrade imaging resolution or signal. Adaptive Optics (AO) techniques that sense and subsequently correct the aberrations can restore diffraction-limited imaging, but most implementations remain technically complex and expensive. Wavefront sensing, the key step in AO, remains particularly challenging due to slow speed or additional illumination dose imparted to the sample. Previously we showed that phase diversity (PD), a method for wavefront sensing originally developed for use in astronomy, can rapidly sense and correct aberrations in a widefield fluorescence microscope. Here we extend this approach to light sheet microscopy, showing that the PD-based AO of sample-induced aberrations in live Caenorhabditis elegans embryos restores image contrast and resolution, enabling subcellular imaging throughout the imaging volume. In addition to these preliminary results, the further improvement and extension of this technology to additional samples and microscopes will be discussed.</p>
},
	keywords = {Adaptive optics, Fluorescence microscopy, light-sheet microscopy},
	issn = {0021-9258},
	doi = {https://doi.org/10.1016/j.jbc.2026.112990},
	url = {https://www.sciencedirect.com/science/article/pii/S0021925826018624},
	author = {Courtney Johnson and Magdalena Schneider and Ryan Christensen and Xuesong Li and Min Guo and Patrick La Riviere and Hari Shroff}
}
@article {69880,
	title = {DeepPD: Joint phase and object estimation from phase diversity with neural calibration of a deformable mirror},
	journal = {arXiv},
	year = {2025},
	month = {2025 Apr 19},
	abstract = {<h4>Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.</h4>
},
	doi = {10.48550/arxiv.2504.14157},
	url = {https://doi.org/10.48550/arxiv.2504.14157},
	author = {Magdalena C. Schneider and Courtney Johnson and C{\'e}dric Allier and Larissa Heinrich and Diane Adjavon and Joren Husic and Patrick La Riviere and Stephan Saalfeld and Hari Shroff}
}
