Science AdvancesBioengineeringPeer-reviewed journal article
Inverse-scattering in biological samples via beam-propagation
Jeongsoo KimiD, Blythe Marie BoltoniD, Khashayar MoshksayaniD, Rishika Khanna, Mary E. SwartziD, Michał ZiemczonokiD, Mohini KamraiD, Karin A. JorniD, Sapun H. ParekhiD, Małgorzata KujawińskaiD, Johann K. EberhartiD, Elif Sarinay CenikiD, Adela Ben‐YakariD, Shwetadwip ChowdhuryiD
Abstract
Multiple scattering limits optical imaging in thick biological samples by scrambling sample-specific information. Physics-based inverse-scattering methods aim to computationally recover this information, often using non-convex optimization to reconstruct the scatter-corrected sample. However, this non-convexity can lead to inaccurate reconstructions, especially in highly scattering samples. Here, we show that various implementation strategies for even the same inverse-scattering method significantly affect reconstruction quality. We demonstrate this using multi-slice beam propagation (MSBP), a relatively simple nonconvex inverse-scattering method that reconstructs a scattering samples 3D refractive-index (RI). By systematically conducting MSBP-based inverse-scattering on both phantoms and biological samples, we showed that an amplitude-only cost function in the inverse-solver, combined with angular and defocus diversity in the scattering measurements, enabled high-quality, fully-volumetric RI imaging. This approach achieved subcellular resolution and label-free 3D contrast across diverse, multiple-scattering samples. These results lay the groundwork for robust use of inverse-scattering techniques to achieve biologically interpretable 3D imaging in increasingly thick, multicellular samples, introducing a new paradigm for deep-tissue computational imaging.
- assay: imaging
- research focus: imputation
- research focus: dimensionality reduction
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 4
- FWCI
- 10.21
- Field percentile
- top 2%
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