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The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.

The definition and reporting of spatial resolution for coherent imaging methods varies widely in the imaging community. We advocate the use of a standard spoke-pattern imaging target and the mandatory inclusion of information about underlying a priori assumptions.

Controlling light propagation across scattering media by wavefront shaping holds great promise for a wide range of communications and imaging applications. But, finding the right shape for the wavefront is a challenge when the mapping between input and output scattered wavefronts (that is, the transmission matrix) is not known. Correlations in transmission matrices, especially the so-called memory effect, have been exploited to address this limitation. However, the traditional memory effect applies to thin scattering layers at a distance from the target, which precludes its use within thick scattering media, such as fog and biological tissue. Here, we theoretically predict and experimentally verify new transmission matrix correlations within thick anisotropically scattering media, with important implications for biomedical imaging and adaptive optics. Light propagating through a scattering medium exhibits correlations in the transmission matrix. A theoretical and experimental study uncovers intensity correlations that survive multiple scattering, which could be exploited for imaging.

High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique’s state-of-the-art super-resolution SPAD imaging performance. High-resolution single-photon imaging is challenging due to complex hardware and noise disturbances. Here, the authors realise simultaneous single-photon denoising and super-resolution enhancement by physics-informed deep learning, with a physical multi-source noise model, two single-photon image datasets, and a deep transformer network.

•We thoroughly derive and compare different layered analytical models used in continuous-wave (CW), time-domain (TD), and frequency-domain (FD)-DCS, highlighting their strengths and applications.•We discuss novel artificial intelligence (AI)-enhanced DCS analysis strategies, addressing their effectiveness and potential.•We conclude new applications of CMOS SPAD cameras and compare them with existing sensors used in DCS.•We compare TD-DCS and CW-DCS systems and emphasize the benefits of TD-DCS and its potential for future development.•The authors are leading scientists in DCS and SPAD sensors.•Discussion and outlooks are also provided. Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

We describe an encrypted communication principle that forms a secure link between two parties without electronically saving either of their keys. Instead, random cryptographic bits are kept safe within the unique mesoscopic randomness of two volumetric scattering materials. We demonstrate how a shared set of patterned optical probes can generate 10 gigabits of statistically verified randomness between a pair of unique 2 mm 3 scattering objects. This shared randomness is used to facilitate information-theoretically secure communication following a modified one-time pad protocol. Benefits of volumetric physical storage over electronic memory include the inability to probe, duplicate or selectively reset any bits without fundamentally altering the entire key space. Our ability to securely couple the randomness contained within two unique physical objects can extend to strengthen hardware required by a variety of cryptographic protocols, which is currently a critically weak link in the security pipeline of our increasingly mobile communication culture.

The rapid proliferation of intelligent systems (e.g., fully autonomous vehicles) in today's society relies on sensors with low latency and computational effort. Yet current sensing systems ignore most available a priori knowledge, notably in the design of the hardware level, such that they fail to extract as much task‐relevant information per measurement as possible. Here, a “learned integrated sensing pipeline” (LISP), including in an end‐to‐end fashion both physical and processing layers, is shown to enable joint learning of optimal measurement strategies and a matching processing algorithm, making use of a priori knowledge on task, scene, and measurement constraints. Numerical results demonstrate accuracy improvements around 15% for object recognition tasks with limited numbers of measurements, using dynamic metasurface apertures capable of transceiving programmable microwave patterns. Moreover, it is concluded that the optimal learned microwave patterns are nonintuitive, underlining the importance of the LISP paradigm in current sensorization trends. Optimal wavefronts to probe a scene with microwave radiation in order to recognize objects therein are learned by integrating a model of the sensor's reconfigurable physical layer into the machine‐learning pipeline used to process the measurements. Thereby the amount of task‐relevant information obtained per measurement is maximized, enabling sensing with low latency and computational burden.

Ptychography is a powerful computational imaging technique that transforms a collection of low-resolution images into a high-resolution sample reconstruction. Unfortunately, algorithms that currently solve this reconstruction problem lack stability, robustness, and theoretical guarantees. Recently, convex optimization algorithms have improved the accuracy and reliability of several related reconstruction efforts. This paper proposes a convex formulation of the ptychography problem. This formulation has no local minima, it can be solved using a wide range of algorithms, it can incorporate appropriate noise models, and it can include multiple a priori constraints. The paper considers a specific algorithm, based on low-rank factorization, whose runtime and memory usage are near-linear in the size of the output image. Experiments demonstrate that this approach offers a 25% lower background variance on average than alternating projections, the ptychographic reconstruction algorithm that is currently in widespread use.

While knee osteoarthritis (OA) is a leading cause of disability in the United States, OA within the patellofemoral joint is understudied compared to the tibiofemoral joint. Mechanical alterations to cartilage may be among the first changes indicative of early OA. MR-based protocols have probed patellar cartilage mechanical function by measuring deformations in response to exercise. These studies, however, often rely on manual segmentation, which is time-intensive and may introduce variability. Therefore, our goals were (1) to develop convolutional neural networks to segment the patella and patellar cartilage from knee MR scans and (2) to evaluate the ability of these networks to measure exercise-induced cartilage deformations. Using a dataset of 109 knee MR scans, 2D and 3D U-Nets were developed and compared using the mean dice similarity coefficient (mDSC). Reliability of the best-performing networks was examined and the ability of these networks to detect patellar cartilage deformations following a hopping activity was evaluated. The 2D U-Net outperformed the 3D U-Net for both the patella (mDSC, 2D: 0.967 vs 3D: 0.960) and patellar cartilage (mDSC, 2D: 0.896 vs. 3D: 0.895). The 2D U-Nets demonstrated excellent reliability (ICC = 0.99, mean difference < 0.03 mm) in reproducing the mean patellar cartilage thickness across different days. Lastly, significant mean (mean ± standard deviation, 1.5 ± 1.8 %, P = 0.014) and maximum (10.6 ± 3.2 %, P < 0.001) patellar cartilage strains were detected following hopping. The autosegmentation tools developed herein provide a powerful framework for probing patellar cartilage mechanics in vivo.

Optical scattering limits the ability to image or focus beneath one millimetre of tissue in biomedical optics. This Review summarizes recently developed 'guidestar' mechanisms that provide feedback for intra-tissue focusing. In the field of biomedical optics, optical scattering has traditionally limited the range of imaging within tissue to a depth of one millimetre. A recently developed class of wavefront-shaping techniques now aims to overcome this limit and achieve diffraction-limited control of light beyond one centimetre. By manipulating the spatial profile of an optical field before it enters a scattering medium, it is possible to create a micrometre-scale focal spot deep within tissue. To successfully operate in vivo , these wavefront-shaping techniques typically require feedback from within the biological sample. This Review summarizes recently developed 'guidestar' mechanisms that provide feedback for intra-tissue focusing. Potential applications of guidestar-assisted focusing include optogenetic control over neurons, targeted photodynamic therapy and deep tissue imaging.