@article {69744,
	title = {Bio-inspired 3D-printed phantom: Encoding cellular heterogeneity for characterization of quantitative phase imaging},
	journal = {Measurement},
	volume = {247},
	year = {2025},
	month = {2025 Apr 15},
	pages = {116765},
	abstract = {<p>Quantitative phase imaging (QPI) has proven to be a valuable tool for advanced biological and pharmacological research, providing phase information for the study of cell features and physiology in label-free conditions. The next step for QPI to become a gold standard is the quantitative assessment of the phase gradients over the different microscopy setups. Given the large variety of QPI systems, a systematic comparison is a challenging task, and requires a calibration target representative of the living samples. In this paper, we introduce a tailor-made 3D-printed phantom derived from phase images of eukaryotic cells. It comprises typical morphologies and optical thicknesses found in biological cultures and is characterized with digital holographic microscopy (reference measurements). The performance of three different full field QPI optical systems, in terms of optical path difference and dry mass accuracy, were evaluated. This phantom opens up other possibilities for the validation of reconstruction algorithms and post-processing routines, and paves the way for calibration targets designed ad hoc for specific biological questions.</p>
},
	keywords = {Cell phantom, Inter-laboratory comparison, Metrology, Phase calibration target, Quantitative phase imaging, Two-photon polymerization lithography},
	issn = {0263-2241},
	doi = {10.1016/j.measurement.2025.116765},
	url = {https://www.sciencedirect.com/science/article/pii/S0263224125001241},
	author = {Sylvia Desissaire and Micha{\l} Ziemczonok and Tigrane Cantat-Moltrecht and Arkadiusz Ku{\'s} and Guillaume Godefroy and Lionel Herv{\'e} and Chiara Paviolo and Wojciech Krauze and C{\'e}dric Allier and Ondrej Mandula and Ma{\l}gorzata Kujawi{\'n}ska}
}
@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}
}
