De-scattering with Excitation Patterning enables rapid wide-field imaging through scattering media

Neurophotonics beyond the surface: unmasking the brain's complexity exploiting optical scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

3D super-resolution optical fluctuation imaging with temporal focusing two-photon excitation

3D super-resolution fluorescence microscopy typically requires sophisticated setups, sample preparation, or long measurements. A notable exception, SOFI, only requires recording a sequence of frames and no hardware modifications whatsoever but being a wide-field method, it faces problems in thick, dense samples. We combine SOFI with temporal focusing two-photon excitation - the wide-field method that is capable of exciting a thin slice in 3D volume. Temporal focusing is simple to implement whenever the excitation path of the microscope can be accessed. The implementation of SOFI is straightforward. By merging these two methods, we obtain super-resolved 3D images of neurons stained with quantum dots. Our approach offers reduced bleaching of out-of-focus fluorescent probes and an improved signal-to-background ratio that can be used when robust resolution improvement is required in thick, dense samples.

Multiline orthogonal scanning temporal focusing (mosTF) microscopy for scattering reduction in in vivo brain imaging

Temporal focusing two-photon microscopy has been utilized for high-resolution imaging of neuronal and synaptic structures across volumes spanning hundreds of microns in vivo. However, a limitation of temporal focusing is the rapid degradation of the signal-to-background ratio and resolution with increasing imaging depth. This degradation is due to scattered emission photons being widely distributed, resulting in a strong background. To overcome this challenge, we have developed multiline orthogonal scanning temporal focusing (mosTF) microscopy. mosTF captures a sequence of images at each scan location of the excitation line. A reconstruction algorithm then reassigns scattered photons back to their correct scan positions. We demonstrate the effectiveness of mosTF by acquiring neuronal images of mice in vivo. Our results show remarkable improvements in in vivo brain imaging with mosTF, while maintaining its speed advantage.

Ultrafast laser state active controlling based on anisotropic quasi-1D material

Laser state active controlling is challenging under the influence of inherent loss and other nonlinear effects in ultrafast systems. Seeking an extension of degree of freedom in optical devices based on low-dimensional materials may be a way forward. Herein, the anisotropic quasi-one-dimensional layered material Ta2PdS6 was utilized as a saturable absorber to modulate the nonlinear parameters effectively in an ultrafast system by polarization-dependent absorption. The polarization-sensitive nonlinear optical response facilitates the Ta2PdS6-based mode-lock laser to sustain two types of laser states, i.e., conventional soliton and noise-like pulse. The laser state was switchable in the single fiber laser with a mechanism revealed by numerical simulation. Digital coding was further demonstrated in this platform by employing the laser as a codable light source. This work proposed an approach for ultrafast laser state active controlling with low-dimensional material, which offers a new avenue for constructing tunable on-fiber devices.

Neurophotonics beyond the Surface: Unmasking the Brain's Complexity Exploiting Optical Scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

DEEP-squared: deep learning powered De-scattering with Excitation Patterning

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced "De-scattering with Excitation Patterning" or "DEEP" as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations were needed. In this work, we present DEEP2, a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP's throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and experimental imaging studies, including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.

De-scattering Deep Neural Network Enables Fast Imaging of Spines through Scattering Media by Temporal Focusing Microscopy

Today the gold standard for in vivo imaging through scattering tissue is point-scanning two-photon microscopy (PSTPM). Especially in neuroscience, PSTPM is widely used for deep-tissue imaging in the brain. However, due to sequential scanning, PSTPM is slow. Temporal focusing microscopy (TFM), on the other hand, focuses femtosecond pulsed laser light temporally while keeping wide-field illumination, and is consequently much faster. However, due to the use of a camera detector, TFM suffers from the scattering of emission photons. As a result, TFM produces images of poor quality, obscuring fluorescent signals from small structures such as dendritic spines. In this work, we present a de-scattering deep neural network (DeScatterNet) to improve the quality of TFM images. Using a 3D convolutional neural network (CNN) we build a map from TFM to PSTPM modalities, to enable fast TFM imaging while maintaining high image quality through scattering media. We demonstrate this approach for in vivo imaging of dendritic spines on pyramidal neurons in the mouse visual cortex. We quantitatively show that our trained network rapidly outputs images that recover biologically relevant features previously buried in the scattered fluorescence in the TFM images. In vivo imaging that combines TFM and the proposed neural network is one to two orders of magnitude faster than PSTPM but retains the high quality necessary to analyze small fluorescent structures. The proposed approach could also be beneficial for improving the performance of many speed-demanding deep-tissue imaging applications, such as in vivo voltage imaging.

Three-dimensional bi-functional refractive index and fluorescence microscopy (BRIEF)

Fluorescence microscopy is a powerful tool for imaging biological samples with molecular specificity. In contrast, phase microscopy provides label-free measurement of the sample's refractive index (RI), which is an intrinsic optical property that quantitatively relates to cell morphology, mass, and stiffness. Conventional imaging techniques measure either the labeled fluorescence (functional) information or the label-free RI (structural) information, though it may be valuable to have both. For example, biological tissues have heterogeneous RI distributions, causing sample-induced scattering that degrades the fluorescence image quality. When both fluorescence and 3D RI are measured, one can use the RI information to digitally correct multiple-scattering effects in the fluorescence image. Here, we develop a new computational multi-modal imaging method based on epi-mode microscopy that reconstructs both 3D fluorescence and 3D RI from a single dataset. We acquire dozens of fluorescence images, each 'illuminated' by a single fluorophore, then solve an inverse problem with a multiple-scattering forward model. We experimentally demonstrate our method for epi-mode 3D RI imaging and digital correction of multiple-scattering effects in fluorescence images.

Computational optics for high-throughput imaging of neural activity

Optical microscopy offers a noninvasive way to image neural activity in the mouse brain. To simultaneously record neural activity across a large population of neurons, optical systems that have high spatiotemporal resolution and can access a large volume are necessary. The throughput of a system, that is, the number of resolvable spots acquired by the system at a given time, is usually limited by optical hardware. To overcome this limitation, computation optics that designs optical hardware and computer software jointly becomes a new approach that achieves micronscale resolution, millimeter-scale field-of-view, and hundreds of hertz imaging speed at the same time. This review article summarizes recent advances in computational optics for high-throughput imaging of neural activity, highlighting technologies for three-dimensional parallelized excitation and detection. Computational optics can substantially accelerate the study of neural circuits with previously unattainable precision and speed.

Non-paraxial diffraction analysis for developing DMD-based optical systems

We propose a non-paraxial diffraction model of the digital micromirror device (DMD) by combining the conventional Fraunhofer diffraction and a simple method of coordinative mapping. It is equivalent to adding aberrations of diffracted wave fields to the aberration-free Fraunhofer diffraction instead of complex integral calculations, allowing the simulated diffraction patterns to be consistent with the actual experimental counterparts. Moreover, it is verified by the experiments and literature that the diffraction angles, orders, and efficiency can all be well predicted for arbitrary incident angles and wavelengths. Especially for diffracted zenith angles within 50°, the predicted values reveal ∼1% error, and in a broader range, the predicted errors of diffracted azimuth angles are less than 4%. To the best of our knowledge, it is the first model capable of describing the non-paraxial diffraction behavior of the DMD. The proposed model with universality and effectiveness will help users to optimally construct DMD-based optical systems by guiding optical layouts, selection of light sources, and utilization and suppression of diffraction effects.

Remote Focusing in a Temporal Focusing Microscope

In a temporal focusing microscope, dispersion can remotely shift the temporal focal plane axially, but only a single depth can be in focus at a time on a fixed camera. In this paper, we demonstrate remote focusing in a temporal focusing microscope. Dispersion tuning with an electrically tunable lens (ETL) in a 4 f pulse shaper scans the excitation plane axially, and another ETL in the detection path keeps the shifted excitation plane in focus on the camera. Image stacks formed using two ETLs versus a traditional stage scan are equivalent.

HiLo Based Line Scanning Temporal Focusing Microscopy for High-Speed, Deep Tissue Imaging

High-speed, optical-sectioning imaging is highly desired in biomedical studies, as most bio-structures and bio-dynamics are in three-dimensions. Compared to point-scanning techniques, line scanning temporal focusing microscopy (LSTFM) is a promising method that can achieve high temporal resolution while maintaining a deep penetration depth. However, the contrast and axial confinement would still be deteriorated in scattering tissue imaging. Here, we propose a HiLo-based LSTFM, utilizing structured illumination to inhibit the fluorescence background and, thus, enhance the image contrast and axial confinement in deep imaging. We demonstrate the superiority of our method by performing volumetric imaging of neurons and dynamical imaging of microglia in mouse brains in vivo.