Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

HiLo microscopy with caustic illumination.

HiLo microscopy is an optical sectioning structured illumination microscopy technique based on computationally combining two images: one with uniform illumination and the other with structured illumination. The most widely used structured illumination in HiLo microscopy is random speckle patterns, due to their simplicity and resilience to tissue scattering. Here, we present a novel HiLo microscopy strategy based on random caustic patterns. Building on an off-the-shelf diffuser and a low-coherence LED source, we demonstrate that caustic HiLo can achieve 4.5 µm optical sectioning capability with a 20× 0.75 NA objective. In addition, with the distinct intensity statistical properties of caustic patterns, we show that our caustic HiLo outperforms speckle HiLo, achieving enhanced optical sectioning capability and preservation of fine features by imaging scattering fixed brain sections of 100 µm, 300 µm, and 500 µm thicknesses. We anticipate that this new structured illumination technique may find various biomedical imaging applications.

Pupil engineering for extended depth-of-field imaging in a fluorescence miniscope.

Significance: Fluorescence head-mounted microscopes, i.e., miniscopes, have emerged as powerful tools to analyze in-vivo neural populations but exhibit a limited depth-of-field (DoF) due to the use of high numerical aperture (NA) gradient refractive index (GRIN) objective lenses. Aim: We present extended depth-of-field (EDoF) miniscope, which integrates an optimized thin and lightweight binary diffractive optical element (DOE) onto the GRIN lens of a miniscope to extend the DoF by 2.8× between twin foci in fixed scattering samples. Approach: We use a genetic algorithm that considers the GRIN lens' aberration and intensity loss from scattering in a Fourier optics-forward model to optimize a DOE and manufacture the DOE through single-step photolithography. We integrate the DOE into EDoF-Miniscope with a lateral accuracy of 70 µm to produce high-contrast signals without compromising the speed, spatial resolution, size, or weight. Results: We characterize the performance of EDoF-Miniscope across 5- and 10-µm fluorescent beads embedded in scattering phantoms and demonstrate that EDoF-Miniscope facilitates deeper interrogations of neuronal populations in a 100-µm-thick mouse brain sample and vessels in a whole mouse brain sample. Conclusions: Built from off-the-shelf components and augmented by a customizable DOE, we expect that this low-cost EDoF-Miniscope may find utility in a wide range of neural recording applications.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Compensating the cell-induced light scattering effect in light-based bioprinting using deep learning.

Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes.

Three-Dimensional Printing of Bisphenol A-Free Polycarbonates.

Polycarbonates are widely used in food packages, drink bottles, and various healthcare products such as dental sealants and tooth coatings. However, bisphenol A (BPA) and phosgene used in the production of commercial polycarbonates pose major concerns to public health safety. Here, we report a green pathway to prepare BPA-free polycarbonates (BFPs) by thermal ring-opening polymerization and photopolymerization. Polycarbonates prepared from two cyclic carbonates in different mole ratios demonstrated tunable mechanical stiffness, excellent thermal stability, and high optical transparency. Three-dimensional (3D) printing of the new BFPs was demonstrated using a two-photon laser direct writing system and a rapid 3D optical projection printer to produce structures possessing complex high-resolution geometries. Seeded C3H10T1/2 cells also showed over 95% viability with potential applications in biological studies. By combining biocompatible BFPs with 3D printing, novel safe and high-performance biomedical devices and healthcare products could be developed with broad long-term benefits to society.