arXiv subjects

Leona Licht

Publications and source records attributed to Leona Licht.

3 recordsLinked to original sources

arXiv2026

Quantitative EUV ptychography reveals nanoscale morphological responses of bacteria under physiological and antibiotic stress

Table-top extreme ultraviolet (EUV) ptychography enables nanoscale, label-free, and quantitative imaging with intrinsic elemental sensitivity, offering a unique modality for subcellular profiling of bacterial morphology and composition. In this work, we apply a state-of-the-art EUV ptychographic microscope to systematically investigate the structural and compositional features of two model prokaryotic bacteria, Escherichia coli and Bacillus subtilis. With quantitative amplitude and phase reconstructions at 44 nm resolution on a tabletop, we visualize subtle phenomena during bacterial sporulation based on distinct morphological signatures. Notably, we examine the single-cell response of B. subtilis to the antibiotic monazomycin, uncovering ultrastructural disruption and compositional alterations. To further characterize the phenotypic variations, we perform multivariate statistical analysis on extracted morphological features, revealing diversity and clustering patterns associated with defined biological states. This work establishes EUV ptychography as a powerful, element-sensitive imaging platform for label-free bacterial imaging, showcasing its promise for biomedical and antimicrobial research.

physics.bio-ph↗

arXiv2025

Dose Constraints for High-Resolution Imaging of Biological Specimens with Extreme Ultraviolet and Soft X-ray radiation

We present a theoretical evaluation of radiation dose constraints for extreme ultraviolet (EUV) and soft X-ray microscopy. Our work particularly addresses the long-standing concern regarding strong absorption of EUV radiation in biological specimens. Using an established dose-resolution model, we compare hydrated and dehydrated cellular states and quantify the fluence required for nanoscale imaging. Our analysis identifies a protein window spanning photon energies from 70 eV up to the carbon K-edge (284 eV), where EUV microscopy could in principle achieve sub-10 nm half-pitch resolution in dehydrated samples at doses well below the Henderson limit, thereby eliminating the need for cryogenic conditions. In this situation, the radiation dose required for EUV imaging is also substantially lower than what is required for comparable resolution in water window soft X-ray microscopy. Furthermore, EUV photons with sufficiently high energy exhibit penetration depths of um-level in dehydrated biomatter, enabling exceptional amplitude and phase contrast through thin cellular regions and small cells. These findings provide quantitative guidelines for photon energy selection and establish the EUV protein window as a dose-efficient and physically viable modality for high-resolution, label-free, material-specific imaging of dehydrated biological matter.

physics.app-ph↗

arXiv2024

Bayesian multi-exposure image fusion for robust high dynamic range ptychography

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. To infer the model parameters, the expectation-maximization (EM) algorithm is employed. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

eess.IV↗