Conventional microscopy usually has a single function aiming to measure one type of signal, such as fluorescence microscopy or phase microscopy. Here, we develop a new computational hybrid imaging method, based on a multi-slice multiple scattering model, that reconstructs both 3D fluorescence and 3D RI by solving an inverse problem from a single dataset of dozens fluorescence images captured at a single image plane. This multi-functional system not only bridges the gap between fluorescence and RI imaging, but also can digitally correct multiple scattering effects in the fluorescence images by using the phantom structure recovered by the reconstructed 3D RI.
3D differential phase contrast (3D DPC) microscopy uses asymmetric illumination patterns and axial scanning to recover volumetric maps of refractive index. To avoid the expense of automated axial scanning, we demonstrate 3D DPC without a z-stage by hand spinning the microscope’s defocus knob to scan the object axially while updating illumination patterns on the LED-array microscope. We utilize an inverse problem optimization to retrieve the sample’s volumetric information with measurements from unknown axial positions by jointly solving for each measurement’s axial position. Finally, we explore how to optimize the LED-array illumination patterns for varying axial sampling rates.
3D quantitative phase (refractive index) microscopy reveals volumetric structure of biological specimens. Optical diffraction tomography (ODT) is a common technique for 3D phase imaging. By angularly scanning a spatially coherent light source and measuring scattered fields on the imaging plane, 3D refractive index (RI) is recovered by solving an inverse problem. However, ODT often linearizes the process by using a weakly scattering model, e.g. the first Born approximation or Rytov approximation, which underestimate the RI and fail to reconstruct realistic shape of high RI contrast multiple scattering objects. On the other hand, non-linear models such as the multi-slice or beam propagation methods mitigate artifacts by modeling multiple scattering. However, they ignore back-scattering and intra-slice scattering and make a paraxial approximation by assuming each slice is infinitesimally thin. In this work, we propose a new 3D scattering model Multi-layer Born (MLB), which treats the object as thin 3D slabs with finite thickness and applies the first Born approximation on each slab as the field propagates through the object, increasing the accuracy significantly. In the meantime, a similar computation complexity is achieved comparing to the previously proposed multi-slice models. Therefore, MLB can achieve accuracy similar to that of FDTD or SEAGLE, a frequency domain solver, with orders of magnitude less computation time. In addition to forward scattering, multiple back-scattering effects are also captured by MLB unlike existing models. We apply MLB to recover the RI distribution of 3D phantoms and biological samples with intensity-only measurements from an LED array microscope and show that the results are superior to existing methods.
Fourier Ptychographic Microscopy (FPM) and Differential Phase Contrast (DPC) are quantitative phase imaging (QPI) methods that recover the complex transmittance function of a sample through coded illumination measurements and phase retrieval optimization. The successes of these methods rely upon acquiring several or possibly hundreds of illumination-encoded measurements. The multi-shot nature of such methods limits their temporal resolution. Similar to motion-induced blur during a long photographic exposure, motion occurring during these acquisitions causes spatial distortion and errors in the reconstructed phase, which inhibits these methods' ability to image fast moving live samples.
Here we present a novel approach to correct for motion during QPI capture that relies on motion navigation to register measurements together prior to phase retrieval. The different illumination patterns required for QPI cause the measurements to have a different contrasts. This makes it difficult to use standard registration approaches to estimate complex sample motion directly from the measurements. Instead, we use a color-multiplexed navigator signal (red) that is comprised of a constant illumination pattern and leverage a color camera to separate it from the primary QPI information (green). The reliable motion estimate allows measurements to be shared across time points through image registration. This enables a full set of measurements for a phase retrieval problem to be solved at each time point. We demonstrate proof-of-concept experimental results in which blurring due to live sample motion (swimming Zebra fish, cell motion, and organelle movement) is reduced.
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