Lei Tian

Understanding biological function often requires complementary information on molecular identity, morphology, behavior, and physiology acquired simultaneously from the same specimen. Achieving this capability in miniature microscopes remains challenging because integrating multiple imaging contrasts typically increases optical complexity while compromising field of view (FOV), spatial resolution, or form factor. Here we present dual-mode Bio-CM ² , a multimodal computational miniature mesoscope that simultaneously captures fluorescence and label-free reflectance through a shared optical architecture based on distributed computational optics. The system enables frame-interleaved, co-registered dual-mode imaging over a ∼7.5 × 10 mm ² FOV with ∼6 μm lateral resolution. We demonstrate the versatility of dual-mode Bio-CM ² across diverse biological systems. In freely moving Caenorhabditis elegans , reflectance captures body posture and locomotor behavior that contextualize fluorescently labeled protein aggregates. In freely swimming larval zebrafish, reflectance captures whole-body morphology and swimming behavior, while fluorescence visualizes cardiac activity. In head-fixed mice, simultaneous fluorescence and reflectance imaging across the bilateral dorsal cortex provides complementary measurements of neuronal calcium activity and intrinsic hemodynamic signals. By integrating molecularly specific fluorescence with complementary reflectance contrast, dual-mode Bio-CM ² extends distributed computational optics into a scalable platform for multimodal miniature imaging.

Understanding distributed biological systems, particularly neural circuits, requires simultaneous cellular-resolution imaging across millimeter-scale fields of view (FOV). Existing miniature microscopes remain fundamentally constrained by trade-offs among FOV, spatial resolution, and optical complexity, limiting their ability to bridge cellular microscopy with cortex-scale imaging. Here we introduce distributed computational optics, a framework that distributes image formation across coordinated optical modules and computationally integrates their measurements into a unified image. We realize this framework in Bio-CM², a computational miniature mesoscope that partitions the imaging field across four optical modules while converging their measurements onto a common image sensor. This architecture overcomes the aberration-scaling limitations of conventional miniature optics while avoiding the hardware complexity of multi-camera systems and the contrast degradation associated with optical multiplexing. Bio-CM² achieves a 7.5 × 10 mm² FOV while enabling cellular-resolution in vivo imaging at video rates. We demonstrate its utility through two complementary imaging modalities in head-fixed mice: cortex-wide functional vascular imaging, enabling simultaneous quantification of pial arteriole vasomotion and mesoscale hemodynamic functional connectivity, and cellular-resolution calcium imaging, resolving the activity of over 3,000 neurons together with mesoscale neuronal functional connectivity. We further demonstrate the versatility of the platform through cellular-resolution imaging of entire coronal mouse brain sections, population-scale imaging of freely behaving Caenorhabditis elegans , and odor-evoked calcium imaging of the main olfactory bulb in head-fixed mice, highlighting its broad applicability across diverse biological systems and imaging modalities. By overcoming the conventional trade-off between FOV and spatial resolution in a compact miniature platform, Bio-CM² establishes distributed computational optics as a scalable framework for multiscale biological imaging.

Optical aberrations fundamentally limit the performance of optical systems across scales, from microscopy and astronomy to quantum imaging and ubiquitous technologies such as smartphone cameras, autonomous vehicles, and optical communication networks. Despite sustained progress in wavefront sensing and correction, existing approaches typically require multiple intensity measurements and iterative phase retrieval algorithms and are often restricted to narrowband operation and simple beam profiles. Their performance further degrades when aberrations are weak or obscured by noise. Here we report a single-shot wavefront sensing and correction paradigm that overcomes the limitations of existing approaches. Central to the approach is the integration of a learned optical phase mask that acts as a physical encoder and is jointly optimized with a neural-network decoder. This encoding removes intrinsic phase ambiguities and significantly enhances sensitivity to weak aberrations. By leveraging this hybrid deep-learning architecture, our method directly and unambiguously retrieves Zernike-based phase distortions from a single focal-plane intensity image, eliminating the need for defocus measurements, or iterative phase reconstruction. The framework is intrinsically broadband, exhibits strong robustness to noise, and generalizes across a diverse class of structured light fields. Together, these capabilities establish a scalable and practical foundation for real-time aberration correction in next-generation optical and photonic systems.

Three-dimensional molecular imaging of living cells is essential for unraveling cellular metabolism and response to therapies. However, existing volumetric methods, including fluorescence microscopy and quantitative phase imaging, either require fluorescent labels or lack chemical specificity. Mid-infrared (mid-IR) photothermal microscopy provides label-free spectroscopic contrast with sub-micrometer resolution but is limited by slow acquisition rates, precluding 3D live-cell studies. Here, we present a photothermal relaxation intensity diffraction tomography (PRIDT) system that encodes mid-IR absorption-induced refractive index changes via a photothermal relaxation scheme and recovers it through intensity diffraction tomography. PRIDT achieves video-rate volumetric chemical imaging with up to 15 Hz per wavelength and offers lateral and axial resolutions of 264 nm and 1 . 12 µm, respectively, within a volumetric field of view of 50 × 50 × 10 µm ³ . We showcase high-speed PRIDT imaging of protein and lipid metabolism in ovarian cancer cells and lipid-droplet dynamics in live cells. PRIDT opens new avenues for rapid, quantitative, three-dimensional molecular imaging in living systems.

Many fundamental biological processes—spanning immune–tumor interactions, neuronal signaling, and microvascular flow—exhibit fast, multiscale dynamics among diverse cell types within three-dimensional tissue environments. Capturing such activity requires imaging systems that simultaneously achieve high temporal resolution, multicolor capability, and large axial coverage over large fields of view (FOVs). However, existing modalities remain limited by trade-offs among imaging speed, spectral capacity, depth of field (DOF), and spatial resolution. Here, we present Dual-Channel Event Microscopy (DEM), which integrates digital micromirror device–based pulsed illumination, extended-DOF (EDOF) optics, and event-based sensing for ultrafast, dual-channel EDOF imaging across a 2.3 × 1.3 mm ² FOV with an effective 200 μ m DOF. Using dual-color fluorescent phantoms and microsphere flow assays, DEM achieves accurate spectral separation and reconstruction of rapid motion at kilohertz frame rates. In vivo, DEM enables simultaneous visualization of neutrophils and premalignant tumors in freely swimming zebrafish. In immobilized specimens, it provides robust, sensor-level contrast enhancement near the heart, suppressing diffuse background to reveal fine vascular networks and active blood circulation into and out of the cardiac chambers. DEM further enables quantitative mapping of blood-flow dynamics in the zebrafish tail, resolving arterial–venous differences and capturing heartbeat-driven oscillations that reflect cardiac pumping with high temporal fidelity. By combining ultrafast acquisition, dual-channel capability, large axial coverage, and intrinsic contrast enhancement within a single event-driven architecture, DEM offers a powerful platform for visualizing rapid multicellular interactions and physiological dynamics in living systems.

Forward and backward scattering provide complementary volumetric and interfacial information, yet conventional three-dimensional (3D) imaging typically accesses only one. We present a substrate-enhanced diffraction tomography approach that simultaneously recovers both channels under multi-angle epi-illumination. This geometry captures one forward- and two backward-scattering bands in axially symmetric Fourier regions, where their complementary coverage enables phase–absorption separation in a non-Hermitian spectrum. Explicit 3D transfer functions are derived for both channels, and an axial Kramers–Kronig relation is established to incorporate substrate-induced boundary conditions in a unified framework. Our results establish a label-free, high-resolution 3D imaging modality that surpasses the limits of existing methods.

Neuromodulatory transmitters have vasoactive properties. Therefore, the impulse response function (IRF) linking spontaneous neuronal activity with hemodynamics may depend on neuromodulation. To test this hypothesis, we used optical imaging to measure norepinephrine (NE) or acetylcholine (ACh), calcium (Ca²⁺) activity of cortical neurons and hemodynamics in cerebral cortex in awake mice. We show that modeling of hemodynamics as a weighted sum of Ca²⁺-specific and NE-specific IRFs (IRF_Ca2+ and IRF_NE) convolved with the respective time courses dramatically improved the model performance compared to using IRF_Ca2+ alone. In contrast to NE, ACh was largely redundant with Ca²⁺ and, therefore, did not improve the hemodynamic estimation. Because NE covaried with arousal, we observed instances of the diminished hemodynamic coherence between cortical regions during high arousal despite coherent behavior of the underlying neuronal Ca²⁺ activity. We conclude that, without accounting for noradrenergic neuromodulation, diminished hemodynamic coherence can be falsely interpreted as neuronal desynchronizations in neuroimaging studies.

There is an increasing need for large-scale high-speed volumetric recording in complex multi-cellular model systems to define dynamic processes. Oblique plane microscopy (OPM) provides a solution that features oblique illumination, rapid optical scanning, and remote focusing to achieve real-time 4D microscopy. OPM implements light sheet imaging via a single primary objective lens, making the entire space below the objective accessible for large specimens, such as living mouse brain. Yet it is challenging to adopt OPM beyond a microscopic scale (i.e. size < 1 mm), limiting its broad applications. Here we present a self-adaptive OPM that leverages Abbe’s sine condition to unlock flexibility across a range of field-of-views (FOVs) (up to 8 mm ² ) and resolutions (down to 2.2 µm ³ ). This versatility enables brain-wide single neuron volumetric calcium imaging in behaving larval zebrafish (1x0.4 mm ² FOV at 5 Hz) and capillary blood cell tracking in living mouse brain (>3x3 mm ² FOV) with a sweeping 0.32 mm wide volume section at 100 Hz. In optically cleared mouse brain, the flexibility allows a screen-and-zoom capability by sequentially imaging the whole brain at low-and-high magnifications to locate and resolve subcellular structures such as dendritic trees and spines. By offering a switchable imaging resolution, volume, and speed, the self-adaptive OPM achieves a versatile platform for studying a wide range of multi-cellular model system, whether in vivo or fixed and optically cleared.

Diffraction tomography (DT) has been widely explored in transmission-mode configurations, enabling high-resolution, label-free 3D imaging. However, industrial metrology applications, such as semiconductor inspection, typically involve opaque or highly reflective substrates (e.g., silicon or metal), necessitating a reflection-mode imaging configuration. In this work, we introduce reflection-mode Multi-Slice Fourier Ptychographic Tomography (rMS-FPT) that achieves high-resolution, volumetric imaging of multi-layered, strongly scattering samples on reflective substrates. We develop a reflection-mode multi-slice beam propagation method (rMSBP) to model multiple scattering and substrate interactions, enabling precise 3D reconstruction. By incorporating darkfield measurements, rMS-FPT enhances resolution beyond the traditional brightfield limit and provides sub-micrometer lateral resolution while achieving optical sectioning. We validate rMS-FPT through numerical simulations on a four-layer resolution target and experimental demonstrations using a reflection-mode LED array microscope. Experiments on a two-layer resolution target and a multi-layer scattering sample confirm the method's effectiveness. Our optimized implementation enables rapid imaging, covering a 1.2mm × 1.2mm area in 1.6 seconds, reconstructing over 10⁹ voxels within a 0.4mm³ volume. This work represents a significant step in extending DT to reflection-mode configurations, providing a robust and scalable solution for 3D metrology and industrial inspection.

Significance: Quantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying cerebral blood flow and neurovascular coupling, particularly in the context of stroke. Conventional multi-exposure fitting is slow and difficult to scale. Efficient, physically grounded methods are needed to extract both vascular and tissue dynamic biomarkers from LSCI data. Aim: To develop and validate a physics-informed neural network (PINN) that quantitatively estimates fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from LSCI measurements without requiring ground-truth labels. Approach: We developed a physics-informed neural network (PINN) to estimate fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from multi-exposure LSCI data without requiring ground-truth labels. The analytical LSCI model is embedded in the network loss function, enforcing consistency with speckle physics during training. The model operates in a self-supervised manner and performs pixel-wise inference across full-field images. The framework was evaluated using in vivo mouse stroke LSCI datasets. Results: The PINN accurately recovered fast decorrelation rates associated with cerebral blood flow and slower dynamics linked to tissue and cellular motion. The parameter maps closely match those from traditional nonlinear fitting, but at orders-of-magnitude higher speed, reducing analysis to under one second per image. It also generalizes to unseen subjects and remains robust under noise. Conclusions: Our approach establishes physics-informed learning as a practical framework for near real-time extraction of vascular and cellular biomarkers from LSCI, enabling longitudinal monitoring of stroke progression and potentially facilitating clinical translation.