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Metabolic imaging is critical for understanding cellular functions beyond morphology, offering significant insights into various biological processes and disease states. Label-free optical imaging techniques stand out by providing high-resolution, molecularly specific, and/or non-invasive assessments of metabolic activity without relying on exogenous contrast agents. This review discusses the key photon-tissue interactions—absorption, emission, and scattering—that underpin label-free optical imaging modalities for interrogating tissue’s metabolic activities at various scales. Specifically, photoacoustic imaging (PAI) leverages absorption-based contrasts such as hemoglobin oxygenation and glucose concentrations to quantify metabolic dynamics. Emission-based techniques, including two-photon fluorescence (TPF) and fluorescence lifetime imaging microscopy (FLIM), exploit intrinsic fluorophores like nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) to assess cellular energy metabolism. Interferometric methods, particularly optical coherence tomography (OCT), provide insights into tissue morphological changes. Second harmonic generation (SHG) detects extracellular matrix components such as the collagen network. Molecular vibrational imaging methods, such as stimulated Raman scattering (SRS) microscopy, visualizes spatial heterogeneity of molecular compositions. Recent clinical translations of these methods highlight their growing roles in oncology, neurology, and dermatology, underscoring their potential in early disease diagnosis and monitoring therapeutic responses. Despite challenges such as depth limitations, advancements like wavefront engineering and optical clearing techniques promise to enhance imaging penetration and clinical applicability, paving the way for broader adoption of label-free optical metabolic imaging in both research and clinical settings. 

Abstract Optical‐resolution photoacoustic microscopy (OR‐PAM) has been increasingly utilized for in vivo imaging of biological tissues, offering structural, functional, and molecular information. In OR‐PAM, it is often necessary to make a trade‐off between imaging depth, lateral resolution, field of view, and imaging speed. To improve the lateral resolution without sacrificing other performance metrics, we developed a virtual‐point‐based deconvolution algorithm for OR‐PAM (VP‐PAM). VP‐PAM has achieved a resolution improvement ranging from 43% to 62.5% on a single‐line target. In addition, it has outperformed Richardson‐Lucy deconvolution with 15 iterations in both structural similarity index and peak signal‐to‐noise ratio on an OR‐PAM image of mouse brain vasculature. When applied to an in vivo glass frog image obtained by a deep‐penetrating OR‐PAM system with compromised lateral resolution, VP‐PAM yielded enhanced resolution and contrast with better‐resolved microvessels. 

Abstract Lung tissue is composed of various functional units, each essential for maintaining the intricate functions of the lung. Disruptions in the molecular and cellular mechanisms in the lung can cause tissue fibrosis, inflammation, and severe breathing difficulties, which are common in conditions such as bronchopulmonary dysplasia (BPD). BPD’s molecular changes are not well understood, which hinders effective diagnosis and treatment. Here, we present a new multimodal imaging workflow for detailed molecular and metabolic characterization of tissues at multiple spatial scales. We applied a combined imaging approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and ultrafast focused light-based imaging & photonics platform (U-FLIP) that included two-photon fluorescence (TPF), second harmonic generation (SHG), and stimulated Raman scattering (SRS). We also developed a hierarchical multimodal registration network (HiMReg) for the precise co-registration of each modality. This approach revealed previously unknown metabolic changes in distinct functional tissue units affected by BPD, including altered lipid distributions, reduced optical redox states, and specific collagen remodeling in bronchioles. Our findings evidenced alterations in lipid composition and metabolism of BPD-affected alveoli compared to healthy tissue, providing novel insights into disease pathophysiology. Our findings elucidate the intricate spatial and molecular complexity of BPD, building on prior research that did not provide the spatial resolution necessary to capture the nuances of metabolic alterations. This multimodal approach offers exceptional insights into disease exploration and could transform the way we study spatially heterogeneous conditions. By providing detailed maps of the metabolic shifts occurring in distinct tissue microanatomical features, the methods developed here could enable the discovery of new therapeutic avenues, making it highly attractive for the field of biomedical research. 

There is interest in using photon entanglement in biomedical applications. In one application, polarization-entangled photons pass through brain tissue. The effect of the brain tissue on the photon entanglement is measured via the decoherence that is imparted on the entangled state. Our current method to obtain a measure of the decoherence involves quantum state tomography, where a minimum of 16 measurements are used in conjunction with tomographic optimization to obtain the density matrix representing the state of the photons. In this work we report on a method to avoid tomographic optimization on behalf of a direct measurement of the elements of the density matrix. We make preliminary comparisons between the two methods. 

This is a correction to the original manuscript. The original manuscript had missing text describing funding sources.