Revealing biomolecular structure and motion with neural ab initio cryo-EM reconstruction.

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) can access the intrinsic heterogeneity of these complexes and is therefore a key tool for understanding mechanism and function. However, 3D reconstruction of the resulting imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, we introduce a method, DRGN-AI, for ab initio heterogeneous cryo-EM reconstruction. With a two-step hybrid approach combining search and gradient-based optimization, DRGN-AI can reconstruct dynamic protein complexes from scratch without input poses or initial models. Using DRGN-AI, we reconstruct the compositional and conformational variability contained in a variety of benchmark datasets, process an unfiltered dataset of the DSL1/SNARE complex fully ab initio, and reveal a new "supercomplex" state of the human erythrocyte ankyrin-1 complex. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM as a high-throughput tool for structural biology and discovery.

Full-colour 3D holographic augmented-reality displays with metasurface waveguides.

Emerging spatial computing systems seamlessly superimpose digital information on the physical environment observed by a user, enabling transformative experiences across various domains, such as entertainment, education, communication and training1-3. However, the widespread adoption of augmented-reality (AR) displays has been limited due to the bulky projection optics of their light engines and their inability to accurately portray three-dimensional (3D) depth cues for virtual content, among other factors4,5. Here we introduce a holographic AR system that overcomes these challenges using a unique combination of inverse-designed full-colour metasurface gratings, a compact dispersion-compensating waveguide geometry and artificial-intelligence-driven holography algorithms. These elements are co-designed to eliminate the need for bulky collimation optics between the spatial light modulator and the waveguide and to present vibrant, full-colour, 3D AR content in a compact device form factor. To deliver unprecedented visual quality with our prototype, we develop an innovative image formation model that combines a physically accurate waveguide model with learned components that are automatically calibrated using camera feedback. Our unique co-design of a nanophotonic metasurface waveguide and artificial-intelligence-driven holographic algorithms represents a significant advancement in creating visually compelling 3D AR experiences in a compact wearable device.

Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction.

In computed tomography (CT), the forward model consists of a linear Radon transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law. Conventional reconstruction often involves inverting this nonlinearity as a preprocessing step and then solving a convex inverse problem. However, this nonlinear measurement preprocessing required to use the Radon transform is poorly conditioned in the vicinity of high-density materials, such as metal. This preprocessing makes CT reconstruction methods numerically sensitive and susceptible to artifacts near high-density regions. In this paper, we study a technique where the signal is directly reconstructed from raw measurements through the nonlinear forward model. Though this optimization is nonconvex, we show that gradient descent provably converges to the global optimum at a geometric rate, perfectly reconstructing the underlying signal with a near minimal number of random measurements. We also prove similar results in the under-determined setting where the number of measurements is significantly smaller than the dimension of the signal. This is achieved by enforcing prior structural information about the signal through constraints on the optimization variables. We illustrate the benefits of direct nonlinear CT reconstruction with cone-beam CT experiments on synthetic and real 3D volumes. We show that this approach reduces metal artifacts compared to a commercial reconstruction of a human skull with metal dental crowns.

High-brightness holographic projection.

We propose a holographic projection system that achieves high image quality, brightness, and light efficiency. Using a novel, to the best of our knowledge, light-efficiency loss function, we are able to concentrate more light on the projection region and improve display brightness compared with conventional projectors. Leveraging emerging artificial intelligence-driven computer-generated holography and camera-in-the-loop calibration techniques, we learn a holographic wave propagation model using experimentally captured holographic images and demonstrate state-of-the-art light reallocation performance with high image quality.

Off-Axis Layered Displays: Hybrid Direct-View/Near-Eye Mixed Reality with Focus Cues.

This work introduces off-axis layered displays, the first approach to stereoscopic direct-view displays with support for focus cues. Off-axis layered displays combine a head-mounted display with a traditional direct-view display for encoding a focal stack and thus, for providing focus cues. To explore the novel display architecture, we present a complete processing pipeline for the real-time computation and post-render warping of off-axis display patterns. In addition, we build two prototypes using a head-mounted display in combination with a stereoscopic direct-view display, and a more widely available monoscopic direct-view display. In addition we show how extending off-axis layered displays with an attenuation layer and with eye-tracking can improve image quality. We thoroughly analyze each component in a technical evaluation and present examples captured through our prototypes.

Modeling linear accelerator (Linac) beam data by implicit neural representation learning for commissioning and quality assurance applications.

BACKGROUND: Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after. PURPOSE: We propose to model Linac beam data through implicit neural representation (NeRP) learning. The potential of the beam model in predicting beam data from sparse measurements and detecting data collection errors was evaluated, with the goal of using the beam model to verify beam data collection accuracy and simplify the commissioning and QA process. MATERIALS AND METHODS: NeRP models with continuous and differentiable functions parameterized by multilayer perceptrons (MLPs) were used to represent various beam data including percentage depth dose (PDD) and profiles of 6 MV beams with and without flattening filter. Prior knowledge of the beam data was embedded into the MLP network by learning the NeRP of a vendor-provided "golden" beam dataset. The prior-embedded network was then trained to fit clinical beam data collected at one field size and used to predict beam data at other field sizes. We evaluated the prediction accuracy by comparing network-predicted beam data to water tank measurements collected from 14 clinical Linacs. Beam datasets with intentionally introduced errors were used to investigate the potential use of the NeRP model for beam data verification, by evaluating the model performance when trained with erroneous beam data samples. RESULTS: Linac beam data predicted by the model agreed well with water tank measurements, with averaged Gamma passing rates (1%/1 mm passing criteria) higher than 95% and averaged mean absolute errors less than 0.6%. Beam data samples with measurement errors were revealed by inconsistent beam predictions between networks trained with correct versus erroneous data samples, characterized by a Gamma passing rate lower than 90%. CONCLUSION: A NeRP beam data modeling technique has been established for predicting beam characteristics from sparse measurements. The model provides a valuable tool to verify beam data collection accuracy and promises to simplify commissioning/QA processes by reducing the number of measurements without compromising the quality of medical physics service.

Scalable 3D Reconstruction From Single Particle X-Ray Diffraction Images Based on Online Machine Learning.

X-ray free-electron lasers (XFELs) offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate XFELs enable single particle imaging (X-ray SPI) where individual, weakly scattering biomolecules are imaged under near-physiological conditions with the opportunity to access fleeting states that cannot be captured in cryogenic or crystallized conditions. Existing X-ray SPI reconstruction algorithms, which estimate the unknown orientation of a particle in each captured image as well as its shared 3D structure, are inadequate in handling the massive datasets generated by these emerging XFELs. Here, we introduce X-RAI, an online reconstruction framework that estimates the structure of a 3D macromolecule from large X-ray SPI datasets. X-RAI consists of a convolutional encoder, which amortizes pose estimation over large datasets, as well as a physics-based decoder, which employs an implicit neural representation to enable high-quality 3D reconstruction in an end-to-end, self-supervised manner. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings and demonstrate its unprecedented ability to process large datasets containing millions of diffraction images in an online fashion. These abilities signify a paradigm shift in X-ray SPI towards real-time capture and reconstruction.

Computational optical sensing and imaging 2021: feature issue introduction.

This Feature Issue includes 2 reviews and 34 research articles that highlight recent works in the field of Computational Optical Sensing and Imaging. Many of the works were presented at the 2021 OSA Topical Meeting on Computational Optical Sensing and Imaging, held virtually from July 19 to July 23, 2021. Articles in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

Larger visual changes compress time: The inverted effect of asemantic visual features on interval time perception.

Time perception is fluid and affected by manipulations to visual inputs. Previous literature shows that changes to low-level visual properties alter time judgments at the millisecond-level. At longer intervals, in the span of seconds and minutes, high-level cognitive effects (e.g., emotions, memories) elicited by visual inputs affect time perception, but these effects are confounded with semantic information in these inputs, and are therefore challenging to measure and control. In this work, we investigate the effect of asemantic visual properties (pure visual features devoid of emotional or semantic value) on interval time perception. Our experiments were conducted with binary and production tasks in both conventional and head-mounted displays, testing the effects of four different visual features (spatial luminance contrast, temporal frequency, field of view, and visual complexity). Our results reveal a consistent pattern: larger visual changes all shorten perceived time in intervals of up to 3min, remarkably contrary to their effect on millisecond-level perception. Our findings may help alter participants' time perception, which can have broad real-world implications.

Computational Optical Sensing and Imaging 2021: introduction to the feature issue.

This feature issue includes two reviews and 34 research papers that highlight recent works in the field of computational optical sensing and imaging. Many of the works were presented at the 2021 Optica (formerly OSA) Topical Meeting on Computational Optical Sensing and Imaging, held virtually from 19 July to 23 July 2021. Papers in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

Focus issue introduction: 3D image acquisition and display: technology, perception and applications.

This Feature Issue of Optics Express is organized in conjunction with the 2021 Optica (OSA) conference on 3D Image Acquisition and Display: Technology, Perception and Applications which was held virtually from 19 to 23, July 2021 as part of the Imaging and Sensing Congress 2021. This Feature Issue presents 29 articles which cover the topics and scope of the 2021 3D conference. This Introduction provides a summary of these articles.

ScanGAN360: A Generative Model of Realistic Scanpaths for 360° Images.

Understanding and modeling the dynamics of human gaze behavior in 360° environments is crucial for creating, improving, and developing emerging virtual reality applications. However, recruiting human observers and acquiring enough data to analyze their behavior when exploring virtual environments requires complex hardware and software setups, and can be time-consuming. Being able to generate virtual observers can help overcome this limitation, and thus stands as an open problem in this medium. Particularly, generative adversarial approaches could alleviate this challenge by generating a large number of scanpaths that reproduce human behavior when observing new scenes, essentially mimicking virtual observers. However, existing methods for scanpath generation do not adequately predict realistic scanpaths for 360° images. We present ScanGAN360, a new generative adversarial approach to address this problem. We propose a novel loss function based on dynamic time warping and tailor our network to the specifics of 360° images. The quality of our generated scanpaths outperforms competing approaches by a large margin, and is almost on par with the human baseline. ScanGAN360 allows fast simulation of large numbers of virtual observers, whose behavior mimics real users, enabling a better understanding of gaze behavior, facilitating experimentation, and aiding novel applications in virtual reality and beyond.

Video See-Through Mixed Reality with Focus Cues.

This work introduces the first approach to video see-through mixed reality with full support for focus cues. By combining the flexibility to adjust the focus distance found in varifocal designs with the robustness to eye-tracking error found in multifocal designs, our novel display architecture reliably delivers focus cues over a large workspace. In particular, we introduce gaze-contingent layered displays and mixed reality focal stacks, an efficient representation of mixed reality content that lends itself to fast processing for driving layered displays in real time. We thoroughly evaluate this approach by building a complete end-to-end pipeline for capture, render, and display of focus cues in video see-through displays that uses only off-the-shelf hardware and compute components.

Amortized Inference for Heterogeneous Reconstruction in Cryo-EM.

Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights into the dynamics of proteins and other building blocks of life. The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule from millions of noisy and randomly oriented 2D projections in a computationally efficient manner, however, remains unsolved. Our method, cryoFIRE, performs ab initio heterogeneous reconstruction with unknown poses in an amortized framework, thereby avoiding the computationally expensive step of pose search while enabling the analysis of conformational heterogeneity. Poses and conformation are jointly estimated by an encoder while a physics-based decoder aggregates the images into an implicit neural representation of the conformational space. We show that our method can provide one order of magnitude speedup on datasets containing millions of images without any loss of accuracy. We validate that the joint estimation of poses and conformations can be amortized over the size of the dataset. For the first time, we prove that an amortized method can extract interpretable dynamic information from experimental datasets.

CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.

Deep learning multi-shot 3D localization microscopy using hybrid optical-electronic computing.

Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.

Unfiltered holography: optimizing high diffraction orders without optical filtering for compact holographic displays.

Computer-generated holography suffers from high diffraction orders (HDOs) created from pixelated spatial light modulators, which must be optically filtered using bulky optics. Here, we develop an algorithmic framework for optimizing HDOs without optical filtering to enable compact holographic displays. We devise a wave propagation model of HDOs and use it to optimize phase patterns, which allows HDOs to contribute to forming the image instead of creating artifacts. The proposed method significantly outperforms previous algorithms in an unfiltered holographic display prototype.

Roadmap on digital holography [Invited].

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Holographic pancake optics for thin and lightweight optical see-through augmented reality.

Holographic pancake optics have been designed and fabricated in eyewear display optics literature dating back to 1985, however, a see-through pancake optic solution has not been demonstrated to date. The key contribution here is the first full-color volume holographic pancake optic in an optical see-through configuration for applications in mobile augmented reality. Specifically, the full-color volume holographic pancake is combined with a flat lightguide in order to achieve the optical see-through property. The fabricated hardware optics has a measured field of view of 29 degrees (horizontal) by 12 degrees (vertical) and a measured large eyebox that allows a ±10 mm horizontal motion and ∼±3 mm vertical motion for a 4 mm diameter pupil. The measured modulation transfer function (average orientation) is 10% contrast at 10 lp/deg. Three holograms were characterized with respect to their diffraction efficiency, angular bandwidth, focal length, haze, and thickness parameters. The phase function in the reflection mode hologram implements a spherical mirror that has a relatively simple recording geometry.

Speckle-free holography with partially coherent light sources and camera-in-the-loop calibration.

Computer-generated holography (CGH) holds transformative potential for a wide range of applications, including direct-view, virtual and augmented reality, and automotive display systems. While research on holographic displays has recently made impressive progress, image quality and eye safety of holographic displays are fundamentally limited by the speckle introduced by coherent light sources. Here, we develop an approach to CGH using partially coherent sources. For this purpose, we devise a wave propagation model for partially coherent light that is demonstrated in conjunction with a camera-in-the-loop calibration strategy. We evaluate this algorithm using light-emitting diodes (LEDs) and superluminescent LEDs (SLEDs) and demonstrate improved speckle characteristics of the resulting holograms compared with coherent lasers. SLEDs in particular are demonstrated to be promising light sources for holographic display applications, because of their potential to generate sharp and high-contrast two-dimensional (2D) and 3D images that are bright, eye safe, and almost free of speckle.