Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

We present quantitative 3D live nerve cell imaging using Gaussian-beam light-sheet modalities with conventional lattice light-sheet and interferometric optical detection approaches. The interferometric detection approach offers maximum FOV scanning area than the conventional one.

Light sheet microscopy in live cells requires minimal excitation intensity and resolves three-dimensional (3D) information rapidly. Lattice light sheet microscopy (LLSM) works similarly but uses a lattice configuration of Bessel beams to generate a flatter, diffraction-limited z-axis sheet suitable for investigating subcellular compartments, with better tissue penetration. We developed a LLSM method for investigating cellular properties of tissue in situ. Neural structures provide an important target. Neurons are complex 3D structures, and signaling between cells and subcellular structures requires high resolution imaging. We developed an LLSM configuration based on the Janelia Research Campus design or in situ recording that allows simultaneous electrophysiological recording. We give examples of using LLSM to assess synaptic function in situ. In presynapses, evoked Ca2+ entry causes vesicle fusion and neurotransmitter release. We demonstrate the use of LLSM to measure stimulus-evoked localized presynaptic Ca2+ entry and track synaptic vesicle recycling. We also demonstrate the resolution of postsynaptic Ca2+ signaling in single synapses. A challenge in 3D imaging is the need to move the emission objective to maintain focus. We have developed an incoherent holographic lattice light-sheet (IHLLS) technique to replace the LLS tube lens with a dual diffractive lens to obtain 3D images of spatially incoherent light diffracted from an object as incoherent holograms. The 3D structure is reproduced within the scanned volume without moving the emission objective. This eliminates mechanical artifacts and improves temporal resolution. We focus on LLS and IHLLS applications and data obtained in neuroscience and emphasize increases in temporal and spatial resolution using these approaches.

We previously developed an incoherent holography technique for use in lattice light sheet (LLS) microscopes that represents a specialized adaptation of light sheet microscopy. Light sheet instruments resolve 3D information by illuminating the sample at 90° to the imaging plane with a sheet of laser light that excites fluorophores in the sample only in a narrow plane. Imaging this plane and then moving it in the imaging z-axis allows construction of the sample volume. Among these types of instruments, LLS microscopy gives higher z-axis resolution and tissue depth penetration. It has a similar working principle to light sheet fluorescence microscopy but uses a lattice configuration of Bessel beams instead of Gaussian beams. Our incoherent light detection technique replaces the glass tube lens of the original LLS with a dual diffractive lens system to retrieve the axial depth of the sample. Here, we show that the system is applicable to all light sheet instruments. To make a direct comparison in the same emission light path, we can imitate the nature of non-Bessel light sheet systems by altering the mask annuli used to create Bessel beams in the LLS system. We change the diffractive mask annuli from a higher NA anulus to a smaller NA anulus. This generates a Gaussian excitation beam similar to conventional light sheet systems. Using this approach, we propose an incoherent light detection system for light sheet 3D imaging by choosing a variable NA and moving only the light sheet while keeping the sample stage and detection microscope objective stationary.

Recently, lattice light-sheet instruments and imaging technology have shown a great improvement in exploring the dynamics of life at subcellular resolution. However, to reconstruct 3-dimensional structures the objective lens or the sample stage are invariably moved. Invention of incoherent holography lattice light-sheet (IHLLS) using FINCH provides quantitative information on the state and size of subcellular changes of neurons over the visible spectrum. The technique allows both faster three-dimensional amplitude as well as phase imaging without moving either sample stage or the detection objective. Amplitude and phase measurements provide intrinsic instrumental simplicity, larger scanning area, and higher resolution when compared to the original LLS schemes. IHLLS is built as an additional detection arm of an original lattice light-sheet (LLS) system. In this chapter, we review the evolution of IHLLS to address its feasibility and limitations.

The purpose of the article is to explore the need and advantages of using the incoherent color holography lattice light-sheet (ICHLLS) to provide multiwavelength quantitative monitoring of 3D cellular dynamics in live tissue to further understand complex functions of cells and cellular compartments. We have explored the use of incoherent color holography lattice light-sheet to investigate colocalization of fluorescent markers in live cells in intact tissue. Neuronal structures provide an attractive target for incoherent color holography lattice light-sheet. The cells show a complex architecture in 3D space in which signaling both between cells and within subcellular structures requires colocalization of proteins and lipids to function. During activity and over long periods it is important in understanding these signaling functions in Parkinson’s, Alzheimer’s and motoneuron diseases within live cells in intact tissue. As a proof of concept this article recalls the key aspects in lattice light-sheet imaging and provides a description of the incoherent detection system configuration to actively control dual diffractive lenses phase-shifting at multiple excitation wavelengths sequentially, and per each z-galvo scanning level, with extended field-of-view. The incoherent color holography lattice light-sheet system will allow simultaneous recording of multidimensional object waves that contain intensity in 3D space, phase, and wavelength information. We measure colocalization of fluorescence indicators introduced into live cells in intact neural tissue.

Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. The object’s 3D shape can be obtained by numerical analysis of the captured holograms and recovering the incurred phase. Recently, deep learning (DL) methods have been used for more accurate holographic processing. However, most supervised methods require large datasets to train the model, which is rarely available in most DH applications due to the scarcity of samples or privacy concerns. A few one-shot DL-based recovery methods exist with no reliance on large datasets of paired images. Still, most of these methods often neglect the underlying physics law that governs wave propagation. These methods offer a black-box operation, which is not explainable, generalizable, and transferrable to other samples and applications. In this work, we propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality while using a generative network as a function approximator to model the inverse of hologram formation. We impose smoothness on the background part of the recovered image using a progressive masking module powered by simulated annealing to enhance the reconstruction quality. The proposed method is one of its kind that exhibits high transferability to similar samples, which facilitates its fast deployment in time-sensitive applications without the need for retraining the network. The results show a considerable improvement to competitor methods in reconstruction quality (about 5 dB PSNR gain) and robustness to noise (about 50% reduction in PSNR vs noise increase rate).

We present quantitative 3D live nerve cell imaging with subcellular resolution and extended FOV using two-color incoherent holography lattice light-sheet system.

We introduce two-color incoherent holography lattice light-sheet system with a detection module that scans an extended FOV of 208x208 µm2, to provide quantitative monitoring of cellular dynamics in live tissue.

Fresnel incoherent correlation holography (FINCH) was a milestone in incoherent holography. In this roadmap, two pathways, namely the development of FINCH and applications of FINCH explored by many prominent research groups, are discussed. The current state-of-the-art FINCH technology, challenges, and future perspectives of FINCH technology as recognized by a diverse group of researchers contributing to different facets of research in FINCH have been presented.

The design of the first lattice light-sheet microscope equipped with incoherent holographic detection for neuronal imaging is presented. The device is designed for the capture of 3-D complex amplitude images moving neither the sample stage nor the detection microscope objective. The system is built onto a conventional lattice light-sheet (LLS) microscope, as a second detection arm, equipped with an incoherent holographic optical design and a monochromatic CMOS sensor. The compact system could be mounted on any lattice light-sheet and light sheet instruments due to flexibility of changing the numerical aperture of the excitation light by changing the anulus of the diffraction mask. For this study, fluorescence imaging is supported by illumination at 488 nm. This work relies upon the use of the self-interference property of the emitted fluorescent light, in which three or four Fresnel patterns are projected onto samples to create interference patterns of a 3-D object using a phase shifting technique. The projection of the diffraction patterns of samples is achieved with a spatial light modulator which allows single (IHLLS 1L) or dual lens (IHLLS 2L) patterns superposition with randomly selected pixels. The focal lengths of the lenses are calculated in two steps using OpticStudio (Zemax, LLC) design to provide the optimal compromise between the two requirements for magnification and dual-beam size matching at the camera plan on the one hand and space between the objective and camera on the other. We used the IHLLS-1L for calibration purposes and IHLLS-2L for recording sample holograms. The system allows the generation of high-resolution amplitude and phase images with larger scanning area and depth of field than the original LLS. Neuronal 3-D maps are built from sets of images acquired at various z-sections, determined by galvanometric mirror depth positions in the sample. This paper describes the concept of the instrument and details its optical design. This paper briefly describes the concept of the instrument and details its optical design. An overview of the key performances is also provided.

Stimuli to excitable cells and various cellular processes can cause cell surface deformations; for example, when excitable cell membrane potentials are altered during action potentials. However, these cellular changes may be at or below the diffraction limit (in dendrites the structures measured are as small as 1 µm), and imaging by traditional methods is challenging. Using dual lenses incoherent holography lattice light-sheet (IHLLS-2L) detection with holographic phase imaging of selective fluorescent markers, we can extract the full-field cellular morphology or structural changes of the object’s phase in response to external stimulus. This approach will open many new possibilities in imaging neuronal activity and, overall, in light sheet imaging. In this paper, we present IHLLS-2L as a well-suited technique for quantifying cell membrane deformation in neurons without the actuation of a sample stage or detection microscope objective.

We propose an Incoherent holography detection technique for lattice light-sheet (IHLLS) systems for 3D imaging without moving either the sample stage or the detection microscope objective, providing intrinsic instrumental simplicity and high accuracy when compared to the original LLS schemes. The approach is based on a modified dual-lens Fresnel Incoherent Correlation Holography technique to produce a complex hologram and to provide the focal distance needed for the hologram reconstruction. We report such an IHLLS microscope, including characterization of the sensor performance, and demonstrate a significant contrast improvement on beads and neuronal structures within a biological test sample as well as quantitative phase imaging. The IHLLS has similar or better transverse performances when compared to the LLS technique. In addition, the IHLLS allows for volume reconstruction from fewer z-galvo displacements, thus facilitating faster volume acquisition.

We present phase images of neuronal structures within a biological test sample using the incoherent holography lattice light-sheet (IHLLS) microscope detection methodology.

We introduce lattice light-sheet system with an incoherent detection module that scans an extended FOV of 208x208 μm2. We demonstrated the IHLLS 3D imaging capabilities, amplitude and phase distributions, of polystyrene beads and nerve cells.

Skeletal muscle has a remarkable ability to regenerate owing to its resident stem cells (also called satellite cells, SCs). SCs are normally quiescent; when stimulated by damage, they activate and expand to form new fibers. The mechanisms underlying SC proliferative progression remain poorly understood. Here we show that DHX36, a helicase that unwinds RNA G-quadruplex (rG4) structures, is essential for muscle regeneration by regulating SC expansion. DHX36 (initially named RHAU) is barely expressed at quiescence but is highly induced during SC activation and proliferation. Inducible deletion of Dhx36 in adult SCs causes defective proliferation and muscle regeneration after damage. System-wide mapping in proliferating SCs reveals DHX36 binding predominantly to rG4 structures at various regions of mRNAs, while integrated polysome profiling shows that DHX36 promotes mRNA translation via 5′-untranslated region (UTR) rG4 binding. Furthermore, we demonstrate that DHX36 specifically regulates the translation of Gnai2 mRNA by unwinding its 5′ UTR rG4 structures and identify GNAI2 as a downstream effector of DHX36 for SC expansion. Altogether, our findings uncover DHX36 as an indispensable post-transcriptional regulator of SC function and muscle regeneration acting through binding and unwinding rG4 structures at 5′ UTR of target mRNAs.

Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic objects from their 2D holograms. One of the technical challenges in the reconstruction stage is eliminating the twin image originating from the phase-conjugate wavefront. The twin image removal is typically formulated as a non-linear inverse problem since the scattering process involved in generating the hologram is irreversible. Conventional phase recovery methods rely on multiple holographic imaging at different distances from the object plane along with iterative algorithms. Recently, end-to-end deep learning (DL) methods are utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from the single-shot in-line digital hologram. However, massive data pairs are required to train the utilize DL model for an acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography do not exist. The trained models are also highly influenced by the objects' morphological properties, hence can vary from one application to another. Therefore, data collection can be prohibitively laborious and time-consuming, as a critical drawback of using DL methods for DH. In this article, we propose a novel DL method that takes advantages of the main characteristic of auto-encoders for blind single-shot hologram reconstruction solely based on the captured sample and without the need for a large dataset of samples with available ground truth to train the model. The simulation results demonstrate the superior performance of the proposed method compared to the state-of-the-art methods used for single-shot hologram reconstruction.

We present tomographic fluorescence imaging using an incoherent detection technique on a lattice light-sheet microscope (LLSM) without moving sample stage or the detection objective. The system provides an adaptable low excitation and high-resolution 3D image.

Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects. One of the technical challenges that arise in the signal processing stage is removing the twin image that is caused by the phase-conjugate wavefront from the recorded holograms. Twin image removal is typically formulated as a non-linear inverse problem due to the irreversible scattering process when generating the hologram. Recently, end-to-end deep learning-based methods have been utilized to reconstruct the object wavefront (as a surrogate for the 3D structure of the object) directly from a single-shot in-line digital hologram. However, massive data pairs are required to train deep learning models for acceptable reconstruction precision. In contrast to typical image processing problems, well-curated datasets for in-line digital holography does not exist. Also, the trained model highly influenced by the morphological properties of the object and hence can vary for different applications. Therefore, data collection can be prohibitively cumbersome in practice as a major hindrance to using deep learning for digital holography. In this paper, we proposed a novel implementation of autoencoder-based deep learning architecture for single-shot hologram reconstruction solely based on the current sample without the need for massive datasets to train the model. The simulations results demonstrate the superior performance of the proposed method compared to the state of the art single-shot compressive digital in-line hologram reconstruction method.