Authors
Jeongsoo Kim, Blythe Bolton, Khashayar Moshksayan, Rishika Khanna, Mary E Swartz, Michał Ziemczonok, Mohini Kamra, Karin Allenspach, Sapun H Parekh, Małgorzata Kujawińska, Johann K Eberhart, Elif Sarinay Cenik, Adela Ben-Yakar, Shwetadwip Chowdhury
Published in
Science advances. Volume 12. Issue 34. Pages eaec8678. Aug 21, 2026. Epub Aug 21, 2026.
Abstract
Multiple scattering limits optical imaging in thick biological samples by scrambling sample-specific information. Physics-based inverse-scattering methods aim to computationally unscramble this information often by using nonconvex optimization solvers. However, their inherent nonconvexity often leads to highly sample-dependent performance and inaccurate reconstructions, particularly in strongly scattering specimens. Here, we introduce a novel inverse-scattering framework based on multislice beam propagation (MSBP) that robustly achieves high-quality scatter correction and label-free volumetric imaging across a diverse range of scattering biological samples. We rigorously benchmarked imaging performance across multiple MSBP solver implementations using both scattering calibration phantoms and biological specimens. We found that an amplitude-only cost function in the inverse solver, combined with angular and defocus diversity in the scattering measurements, enabled volumetric, label-free imaging with high-quality and subcellular-level scatter correction. Together, these results establish a foundation for the reliable application of inverse scattering to achieve biologically interpretable three-dimensional imaging in increasingly thick, multicellular samples, thus introducing a new paradigm for deep-tissue computational imaging.
PMID:
42627909
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.
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